Systems and methods for managing tractor-trailers

Description

BACKGROUND

Enabling a vehicle to follow closely behind one vehicle safely through partial or full automation has significant fuel savings, safety, and/or labor savings benefits, but is generally unsafe when a driver tries to do this manually. Presently, during normal driving, vehicle motion is controlled either manually, by a driver, or by convenience systems, such as cruise control or adaptive cruise control. The various types of cruise control systems control vehicle speed to make driving more pleasurable or relaxing, by partially automating the driving task. Some of these systems use range sensors and/or vehicle sensors to control the speed to maintain a constant headway relative to the leading vehicle (also referred to herein as a front vehicle). In general, these cruise control systems provide minimal added safety, and do not have full control of the vehicle (in terms of being able to fully brake or accelerate).

Driver control does not match the safety performance of even current systems, for several reasons. First, a driver cannot safely maintain a close following distance. In fact, the relatively short distances between vehicles necessary to get any measurable fuel savings results in an unsafe condition if the vehicle is under driver control, thereby risking a costly and destructive accident. Further, the driver is not as capable of maintaining an optimal headway as an automated system is. In fact, a driver trying to maintain a constant headway often causes rapid and large changes in command (accelerator pedal position for example), resulting in a loss of efficiency.

Thus, it would be desirable to have reliable and economical semi-automated vehicular convoying/platooning systems which enable vehicles to follow closely together in a safe, efficient, convenient manner.

Moreover, regardless of whether a vehicle, such as a tractor-trailer (e.g., a tractor hauling a trailer), is platooning or not, today's safety systems do not take advantage of the sensor technology developed over the past few decades. By in large, trailers connected to tractors do not communicate with those tractors using modern techniques, which would allow tractor-trailers to travel more safely. This is in large part due to the lifespan of trailers. Without too much corrosion, a trailer can last decades. That said, as long as there are trailers there will always be a need to replace them. As such, there is a need in the art to develop new trailers that implement various technologies to make them safer, more fuel efficient, more reliable, etc.

SUMMARY

The systems and methods comprising various aspects of the disclosure described herein cause vehicles to avoid adverse actions such as colliding with an object such as a curb. For example, without limitation, aspects of the present invention enable methods and systems for establishing a communication link between vehicles, determining dimensions of a first vehicle, determining the kinematics of the first vehicle, determining a pose of the first vehicle based information gathered by a sensor located on a second vehicle, determining a location of an object based on information gathered by a sensor located on a second vehicle, determining a path for the first vehicle to travel based on the dimensions of the first vehicle, pose of the first vehicle, kinematics of the first vehicle, and location of the object, and causing the first vehicle to avoid an adverse action by causing it to travel on the determined path.

It will be appreciated by those skilled in the art that the various features of the present disclosure can be practiced alone or in combination.

These and other features of the present disclosure will be described in more detail below in the detailed description of the disclosure and in conjunction with the following figures.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the various aspects of the present disclosure, some detailed description now will be provided, by way of illustration, with reference to the accompanying drawings, in which:

FIG. 1 illustrates a diagram of a platooning system, in accordance with some embodiments;

FIG. 2 illustrates a block diagram of a platooning system, in accordance with some embodiments;

FIG. 3 illustrates a block diagram of a system including an electronic control unit, in accordance with some embodiments;

FIGS. 4-8 illustrate example vehicle systems, in accordance with some embodiments;

FIG. 9 illustrates attributes associated with a battery, in accordance with some embodiments;

FIG. 10 illustrates a graph associated with at least one battery, in accordance with some embodiments;

FIG. 11 illustrates a flow chart of an example process, in accordance with some embodiments; and

FIG. 12 illustrates an example computing system, in accordance with some embodiments.

DETAILED DESCRIPTION

The present invention will now be described in detail with reference to several embodiments thereof as illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention, including the description of a plurality of different aspects of the invention, including, in some cases, one or more alternatives. It will be apparent to those skilled in the art that the invention can be practice without implementing all of the features disclosed herein. Further, although many embodiments included in the instant application are related to the concept of managing vehicle safety, including tractor-trailers, and platooning. It should be appreciated that many broader applications are envisioned.

Without limitation, the Applicant has proposed various vehicle platooning systems in which a second, and potentially additional, vehicle(s) is/are automatically, or semi-automatically controlled to closely follow a lead/front vehicle in a safe manner. By way of example, U.S. patent application Ser. Nos. 15/605,456, 15/607,902; 13/542,622 and 13/542,627; U.S. Provisional Patent Application Nos. 61/505,076, 62/377,970 and 62/343,819; and PCT Patent Application Nos. PCT/US2014/030770, PCT/US2016/049143, PCT/US2018/41684, and PCT/US2016/060167 describe various vehicle platooning systems in which a trailing vehicle (also referred to herein as a rear vehicle) is at least partially automatically controlled to closely follow a designated lead vehicle. Each of these earlier applications are incorporated herein by reference in their entirety for all purposes.

One of the goals of platooning is typically to maintain a desired gap between the platooning vehicles and/or a desired relative speed and/or time headway (e.g., a gap may refer to a distance, a headway, or both). Thus, it should be appreciated that, herein, any reference to the term “gap” could refer to a distance, a headway, or both. Further, while the term “maintain” is used throughout this disclosure, maintaining may mean staying within a gap (distance/headway), staying at or within a gap, and/or keeping at least a certain gap. Further, a desired gap may include a relative distance, time headway, and/or angle/offset. A longitudinal distance and/or time headway is frequently referred to herein as a “target gap”. That is, it is desirable for the trailing vehicle (e.g., a rear vehicle) to maintain a designated gap relative to a specific vehicle (e.g., a lead vehicle). The vehicles involved in a platoon will typically have sophisticated control systems suitable for initiating a platoon, maintaining the gap under a wide variety of different driving conditions, and gracefully dissolving (e.g., ending) the platoon as appropriate. Again, it should be appreciated that herein, a gap may refer to a distance, a time headway, or both.

As described herein, the concept of platooning, also known as convoying, is still in its infancy. Academics have toyed with the concept over the last few decades, but to date there are no commercial systems on the road where a vehicle is at least partially controlled by another vehicle via a vehicle-to-vehicle connection (V2V). The benefits provided by such systems are obvious. Namely, the safety provided by these systems is far greater than a system where a rear vehicle doesn't begin to slow down until its radar or LIDAR sensors determine that a lead vehicle is slowing down, such as with some adaptive cruise control systems. Further, by being able to follow another vehicle at a close distance, in some cases both a rear vehicle and a front vehicle may experience significant fuel savings.

As platoonable vehicles (e.g., vehicles capable of platooning or any type of following based on V2V communication, whether directly following each other, offset in different lanes, and/or with one or more vehicles between them) begin to roll out of the labs and into commercial production, their adoption faces significant challenges. For example, the safety and reliability of tractor-trailers remains is a concern for at least semi-automated platoonable vehicles.

Platoonable vehicles may serve many purposes, including reducing drag on tractor-trailers. As discussed above, trailers are seldom replaced, but when they are, it is contemplated that new trailers may be “smarter” than the ones before. For example, in one or more embodiments described herein, a tractor-trailer may sense its own surroundings to prevent it from colliding with an object, such as a curb. As another example, in one or more embodiments described herein, a vehicle (which may or may not be a tractor-trailer) traveling behind a tractor-trailer can sense information about the trailer in front of it such as whether the trailer is traveling within its lane, or how close a trailer is from colliding with an object. Also, as applicable herein, it should be appreciated that a tractor-trailer may instead be any vehicle towing a trailer, boat, etc.

One or more systems described herein prevent at least a portion of a tractor-trailer (e.g., a trailer) from colliding with objects, prevent at least a portion of a tractor-trailer from veering out of its lane, and/or otherwise increase safety, reliability, fuel economy, etc. by gathering information about at least a portion of a tractor-trailer. That information may be gathered via sensors located on the tractor-trailer (e.g., the tractor connected to the trailer, or the trailer itself), a vehicle behind the trailer (which could be a second tractor and/or a second trailer), a base station, a cellular tower, etc. By using sensors remote from a vehicle, an independent verification can be performed remote from a tractor-trailer to assist with preventing adverse actions. Adverse actions may include, but are not limited to: a tractor-trailer's trailer leaving its lane, or about to leave its lane; a tractor-trailer trailer colliding with an object, or about to collide with an object; a tractor-trailer's trailer that is moving/swaying more than a threshold amount; and a tractor-trailer jackknifing.

In some embodiments, a rear vehicle may determine that an adverse action is occurring/being performed. Forward facing sensors may be used to detect lane markings, curbs, and/or assist with determining a front tractor-trailer's pose and/or kinematics. A tractor-trailer's pose and/or kinematics may be projected into the future (e.g., an estimate may be made indicating the tractor-trailer is about to leave its lane or collide with an object), based on its current steering angle, an articulation angle, and/or other attributes. This way, systems described herein can prevent a trailer from running over a curb, for instance. In some embodiments, a vehicle's pose may be of greater, or lesser, importance based on its location (relative or otherwise). For example, processes described herein may place less weight (e.g., relative importance) on a tractor-trailer's pose if the tractor-trailer is further away from an obstacle it (e.g., one or more vehicle systems) is trying to avoid. Similarly, a vehicle's kinematics may be of greater, or lesser, importance based on its kinematics (e.g., an articulation angle/angular displacement, angular velocity, and/or angular acceleration of one or more portions of a tractor-trailer, such as its trailer). For example, a process described herein may place less weight (e.g., relative importance) on a vehicle's kinematics if the vehicle is further away from an obstacle it is trying to avoid. In some embodiments, a tractor-trailer's kinematics may be determined based on various attributes of the vehicle including, but not limited to: dimensions of a tractor, dimensions of a trailer, a location of a kingpin, a location of a fifth wheel, a location of an axle, a location of a dolly, and angular speeds of a tractor, trailer, and/or dolly. In some embodiments, the tractor-trailer's kinematics may be determined from observations made by the other vehicle. For example, the following vehicle might partially or entirely determine the position of the fifth wheel on the tractor, and/or the position of the rear axle of one or more trailers. It may also determine the length of the tractor and or trailer. It may also determine the position and geometry of the steering system of the tractor.

In one or more embodiments, the rear vehicle may be another tractor-trailer, the rear vehicle may be platooning with the front tractor-trailer, and/or the rear vehicle may determine that the front tractor-trailer's trailer is performing an adverse action based on information received at a sensor on the rear vehicle such as a camera, radar, or LIDAR.

Further, in some embodiments, as would be understood by a person skilled in the art, a sensor on a front vehicle (e.g., a front tractor or a front trailer) may be used to prevent a rear vehicle from performing an adverse action. As an example, a front vehicle may use an attached sensor to determine a location of an object and provide that location to a rear vehicle such that the rear vehicle may avoid that object. Systems herein may prevent vehicles from colliding with objects during maneuvers such as U-turns, or backing up. In some embodiments, a sensor may be on a building (e.g., a loading dock/bay), and/or a pole (such as one supporting a traffic light and/or a street light) to assist with preventing adverse actions.

In one or more embodiments, determining whether an adverse action is being performed may be performed by at least one or more of: establishing a communication link between a front tractor-trailer and a rear vehicle; determining one or more dimensions of the front tractor-trailer (dimensions may be determined by a transceiver in a trailer transmitting dimensions to an ECU in a tractor, scanning a code, capturing an image of at least part of a tractor-trailer on an electronic device, information entered by a user, etc.); determining a pose of the front tractor-trailer (which may be performed at least in part by a sensor on the front tractor-trailer and/or rear vehicle); determining vehicle kinematics of the front tractor-trailer (which may be performed at least in part by a sensor on the front tractor-trailer and/or rear vehicle); determining a location of a lane (e.g., via lane markers) and/or an object (which may be performed at least in part by a sensor on the front tractor-trailer and/or rear vehicle); determining a path for the front tractor-trailer; causing the front tractor-trailer to avoid departing the lane; and causing the front tractor-trailer to avoid colliding with the object.

In some embodiments, a path for the front tractor-trailer may be determined by one or more electronic control units (ECUs) on the front tractor-trailer and/or the rear vehicle (e.g., one or more platooning ECUs (PECUs), engine ECUs (EECUs), brake ECUs (BECUs), vehicle ECUs (VECUs) (which may comprise multiple ECUs), etc.). For example, an ECU may determine that a dissolve should be performed, as described in U.S. Patent Applications 62/638,794 and Ser. No. 15/926,809 (in other words, determining a path can include a dissolve, in some embodiments) (these U.S. Patent Applications are hereby incorporated in their entirety for all purposes). In some embodiments a path may be determined at least in part in a multi-tenant environment (e.g., at the front vehicle, the rear vehicle, and/or the cloud). In some embodiments, the location of a lane or object may be provided by a high-definition map (e.g., a map that includes locations of objects such as buildings, curbs, and/or trees, a map that includes road grades, etc.).

In some embodiments, the front tractor-trailer and the rear vehicle may be platooning while calculations are being performed that provide information to the front vehicle about a lane and/or an object. An object may include a curb, a box, a third vehicle, etc. As described herein, platooning may include a first vehicle controlling one or more brakes and/or torque of a second vehicle. For example, a front tractor-trailer may control the brakes of a rear vehicle.

In some embodiments, in response to determining a path for avoiding an adverse action: a rear vehicle may cause a front vehicle (e.g., a tractor-trailer) to avoid the adverse action; a rear vehicle may transmit information to the front vehicle and the front vehicle may use that information to determine a path to avoid the adverse action; and/or a notification may be provided to a driver of the front vehicle (e.g., via a graphical user interface, audio, a haptic device, etc.). In some embodiments, a system may continuously update the likelihood of an adverse action occurring. For instance, a system may cause an audio system to beep at certain speeds in response to a likelihood of an adverse action (similar to how backup sensors beep when they approach an object).

In some embodiments, determining a path for avoiding an adverse action may be based on attributes of a front and/or rear vehicle, which may include, but are not limited to a/an: tractor-trailer's trailer dimensions (e.g., width, length, height), tractor-trailer's tractor dimensions, gap (e.g., distance and/or headway) between a front and rear vehicle, pose, kinematics, speed, an articulation angle, yaw, yaw rate, turning radius, kingpin and/or fifth wheel location, number of trailers, wheelbase, coupler position, information entered by a driver into a system (e.g., via a knob, button, and/or GUI), features of a tractor-trailer designed to reduce drag and/or friction, steering angle, sensed objects (upcoming and/or passed), sensed lane markings, information provided by an inertial measurement unit, turning radius, latitude, longitude, altitude, heading, longitudinal and lateral acceleration, relative angle, type of load (e.g., type of materials a vehicle is carrying), brake status, brake pressure, path history, path projection, travel plans, destination, vehicle size, vehicle type, brake type, current operating mode (autonomous or manual), map data, traffic information, GPS augmentation information (e.g., delays from infrastructure), wheel speed, wheel torque, gross torque, net torque, wind, rain, music, video, infotainment system, suspension, axle weight(s), transmission status (e.g., what gear the vehicle is in, what gear the vehicle was in, what gears the vehicle transferred from and to (e.g., fifth gear to fourth gear)), previous transmission status, hybrid vehicle drivetrain (e.g., a parallel hybrid or an electric hybrid), electric motor, battery, battery life, super charger, electronic throttle control, throttle pedal, brake pedal, power steering, adaptive cruise control, a blowout, interior lighting, exterior lighting, retarder, anti-lock brakes, emergency braking, engine governor, powertrain, gear ratio, wheel size, wheel type, trailer length, trailer type, trailer height, amount of trailers, trailer position, current trailer position, past trailer position, tractor type, tractor height, transceiver type, current fuel, next determined stop, projected miles remaining until fuel tanks are empty, malfunctions, turn signals, LIDAR, radar, ultrasonic sensors, wheel angle, tire pressure, tire tread depth, cabin temperature, engine temperature, trailer interior temperature, camera, fleet of vehicles, network operations center (NOC), computer vision, and intervening traffic (e.g., cut-ins, also referred to as the situation when a vehicle enters an area between a lead vehicle and a rear vehicle).

In some embodiments, the attributes described above may be determined multiple times. For example, a wheel angle may by determined multiple times (e.g., it could be updated/a wheel angle rate may be determined), or an object may be sensed multiple times (e.g., a camera taking a video may be considered a sensor that is updated multiple times).

In various embodiments described herein, determining the likelihood of an adverse action may be based at least in part on information provided by a machine learning algorithm, artificial intelligence, a neural network, etc. For example, instances of tractor-trailers with certain attributes may be used to train an algorithm that causes a system to better project a future location of a tractor-trailer based on various attributes of the tractor-trailer.

In some embodiments described herein, whether a vehicle should be in front of another vehicle or in back of another vehicle when platooning may be determined by characteristics of a battery, either as static design characteristics or dynamic characteristics resulting from their use over their life—regardless of whether either of the vehicles are using sensors to prevent adverse actions.

For example, vehicles in a platoon may be ordered based on a battery cycle count of one of the batteries on one of the platooning vehicles. For example, a first vehicle with one or more batteries with lower battery cycle counts than a second vehicle may be preferred to platoon with the second vehicle it is the rear vehicle in the platoon.

Of course, battery cycle count, or other battery attributes (e.g., capacity, remaining charge) may be used to determine an order of platooning vehicles in addition to one or more of the vehicle attributes described above (e.g., with regard to determining a path to avoid an adverse action).

FIG. 1 illustrates a diagram of vehicles transmitting data, in accordance with some embodiments. FIG. 1. depicts multiple vehicles 110, 112, 114, 116, 120, and 122. FIG. 1 also depicts a base station 130 and a network 140. In various embodiments, vehicle 110 may transmit data (also referred to as information) to other vehicles 112, 114, 116, 120, and 122 directly, via base station 130, and/or via network 140. Vehicle 110 may also receive data from other vehicles 112, 114, 116, 120, and 122 directly, via base station 130, and/or via network 140. In some embodiments, a vehicle (e.g., vehicle 112) may retransmit information received from a first vehicle (e.g., vehicle 110) to another vehicle (e.g., vehicle 116) with or without additional information (e.g., information generated at vehicle 112 in addition to information received from vehicle 110).

In various embodiments, vehicles 110, 112, 114, 116, 120, and 122 may be configured to platoon, and may platoon with one another. In some embodiments, vehicles may transmit and/or receive data (e.g., to a NOC and/or fleet management system, etc.) including, but not limited to data indicating: whether they are available to platoon; whether they are platooning; whether a platoon they were part of dissolved; what direction they are traveling; what direction they are predicted (e.g., predetermined/planning on/suggested) to be traveling on for a particular period of time; when they are expected to stop (e.g., predetermined to stop, planning on stopping, suggested stopping time); where they plan on stopping; what route(s) they plan to travel (e.g., a route suggested and/or determined by a system, a route determined by a navigation/mapping system based on their destination such a system may be a rendezvousing system, a fleet management system, a navigation system, etc.); what type of platooning system they are equipped with; how many hours they have been on the road; weather they are capable of following the leader (e.g., if one or more vehicles can platoon without a driver); whether they are capable of being the leader in a follow-the-leader system; whether the vehicle is fully autonomous (e.g., capable of level 4 according to the SAE classification system); how much fuel they have saved; how much money they have saved; an area they are allowed to travel within; an area they are not allowed to travel outside of; whether they are capable of platooning on city streets; whether they are only capable of platooning on a highway; whether they are capable of platooning on non-public roads; whether they are capable of platooning in a particular construction site, mine, forest, etc.; and whether other attributes associated with a vehicle's account allows them to platoon. As should be understood, one or more of these attributes may be used to determine whether a vehicle can platoon with one or more additional vehicles, and whether a vehicle should platoon with one or more additional vehicles. It is contemplated that in some embodiments, a system may rank one or more vehicles with which a vehicle should platoon. In such an embodiment, if a target vehicle (e.g., a vehicle with a high ranking) that a first vehicle attempts to platoon with platoons with second vehicle before the first vehicle is able to platoon with the target vehicle, then the first vehicle may select another (e.g., the next) ranked vehicle that the system would like it to (e.g., determines that it should attempt to) platoon with.

In addition to these factors, other information that a vehicle may transmit, receive, and/or base determinations described herein on vehicle attribute data including, but not limited to a/an: position, latitude, longitude, altitude, heading, speed, longitudinal and lateral acceleration, relative angle, type of load (e.g., type of materials a vehicle is carrying), brake status, brake pressure, path history, path projection, travel plans, vehicle size, vehicle type, brake type, current operating mode (autonomous or manual), map data, traffic information, GPS augmentation information (e.g., delays from infrastructure), wheel speed, wheel torque, gross torque, net torque, wind, rain, music, video, infotainment system, suspension, axle weight(s), transmission status (e.g., what gear the vehicle is in, what gear the vehicle was in, what gears the vehicle transferred from and to (e.g., fifth gear to fourth gear)), previous transmission status, hybrid vehicle drivetrain (e.g., a parallel hybrid or an electric hybrid), whether a vehicle has an electric motor, battery, electronic throttle control, throttle pedal, brake pedal, power steering, adaptive cruise control, a blowout, interior lighting, exterior lighting, retarder, anti-lock brakes, emergency braking, engine governor, powertrain, gear ratio, wheel size, wheel type, trailer length, trailer type, trailer height, amount of trailers, trailer position, current trailer position, past trailer position, tractor type, tractor height, transceiver type, current fuel, next determined stop, projected miles remaining until fuel tanks are empty, malfunctions, turn signals, LIDAR, radar, ultrasonic sensors, road surface, wheel angle, tire pressure, cabin temperature, engine temperature, trailer interior temperature, camera, fleet of vehicles, NOC, computer vision, other vehicle traveling in the same direction, other vehicle traveling in an opposite direction, and intervening traffic (e.g., cut-ins, also referred to as the situation when a vehicle enters an area between a lead vehicle and a rear vehicle). This information can be used by one or more vehicles, systems, fleets, etc. to determine whether a vehicle may platoon with another vehicle and/or to determine the best vehicle with which a vehicle may platoon. Again, it is contemplated that in some embodiments, a system may rank one or more vehicles with which a vehicle should platoon, and this ranking may be based on vehicle attributes described above. In such an embodiment, if a target vehicle that a first vehicle wishes to platoon with platoons with another vehicle before the first vehicle is able to platoon with the target vehicle, then the first vehicle may move to another (e.g., the next) ranked vehicle that the system would like it to (e.g., determines that it should attempt to) platoon with.

It should be understood that, herein, when a system determines a rendezvous location and/or rendezvous time, that any of these attributes/information/data may be used alone or in combination to determine: whether two or more vehicles can platoon together, a rendezvous location, a rendezvous time, etc.

FIG. 2 illustrates an example system 200 including two vehicles capable of platooning and associated communication links. Vehicles 210 and 220 are depicted by trucks which are capable of platooning, and can communicate with each other directly or through network 230. Direct communication between two vehicles can occur wirelessly via Dedicated Short Range Communications (DSRC) (e.g., the IEEE 802.11p protocol), which is a two-way short to medium range wireless communications technology that has been developed for vehicle-to-vehicle (V2V) communications. Of course, other communications protocols and channels may be used in addition to or in place of a DSRC link. For example, the inter-vehicle communications may additionally or alternatively be transmitted over a cellular communications channel such as 4G LTE Direct, 5G, a Citizen's Band (CB) Radio channel, one or more General Mobile Radio Service (GMRS) bands, one or more Family Radio Service (FRS) bands, Wi-Fi, Zigbee and/or any other now existing or later developed communications channels using any suitable communication protocols either alone or in combination.

FIG. 2 also includes a network operations center (NOC) 240. NOC 240 may include one or more locations from which network monitoring, control, and/or management may be exercised over a communication network (e.g., a NOC may be located in the cloud/a multi-tenant environment). NOC 240 can oversee a complex network of vehicles, satellite communications, cellular networks, web applications, and/or management tools. Users of NOC 240 may be responsible for monitoring one or more networks, sub-networks, fleets of vehicles, and/or sub-fleets of vehicles that may require special attention to avoid degraded service. For example, NOC 240 may receive information about various vehicles 210 and 220 such as their locations and attributes, run various programs based on the received information, and send information back to vehicles 210 and 220, including indicating whether they are allowed to platoon.

In addition to NOC 240, client devices 252 (e.g., a smartphone or tablet), 254 (e.g., a desktop computer or terminal), and 256 (e.g., a laptop computer or terminal) may be used to send and/or receive information about vehicles 210 and 220, NOC 240, or information from canonical sources such as the Internet (e.g., Google Maps or another online map provider, a traffic provider, a weather provider, etc.). Client devices can be used to view attributes of vehicles 210 and 220 such as their location, an estimate of their weight, their speed, an amount of engine torque, amount of applied break, a destination, etc.

FIG. 2 also includes a satellite 260, which can send signals to network 230, NOC 240, and/or vehicles 210 and 220. Satellite 260 may be part of a satellite navigation system such as a global navigation satellite system (GNSS). GNSSs include the United States's Global Positioning System (GPS), Russia's GLONASS, China's BeiDou Navigation Satellite System, and the European Union's Galileo. Based on information sent from satellite 260, systems described herein can determine locations of vehicles 210 and 220.

Of course, it should be appreciated that the system described in FIG. 2 is only an example, and that many other configurations may exist. For example, a NOC may assist with the monitoring and control of hundreds or thousands of vehicles, and many types of web applications may exist.

FIG. 3 illustrates and example system 300 including a platoon controller 310 (also referred to as a platoon electronic control unit, a platoon ECU, or a PECU). As described throughout this disclosure, a wide variety of configurations may be used to implement platooning systems described herein. The specific controller design can vary based on the level of automation contemplated for the controller, as well as the nature of and equipment available on the host vehicles participating in the platoon. FIG. 3 illustrates components of one possible configuration.

FIG. 3 diagrammatically illustrates a vehicle control architecture that can be suitable for use with platooning vehicles such as tractor-trailers. The specific controller, or platooning ECU, illustrated is primarily designed for use in conjunction with a platooning system in which both vehicles include an active driver. The driver of the lead vehicle being fully responsible for control of the lead vehicle. In some embodiments the driver of the rear vehicle may be responsible for steering the rear vehicle, but the platoon controller 310 is primarily responsible for controlling the rear vehicle's torque and braking requests during active platooning. However, as discussed herein, it should be appreciated that generally similar control schemes can be used in systems which contemplate more automated control of one or both of the platoon partners or which utilize vehicle control commands other than or in addition to torque and braking requests.

In the example embodiment illustrated in system 300, a platoon controller 310, receives inputs from a number of sensors 330 on the tractor and/or one or more trailers or other connected units, and a number of actuator controllers 350 (also referred to as electronic control units or ECUs) arranged to control operation of the tractor's powertrain and other vehicle systems. An actuator interface 360 may be provided to facilitate communications between the platoon controller 310 and the actuator controllers 350. In some embodiments, one or more of the actuator interfaces 360 may be included in one or more of the actuator controllers 350 (e.g., an actuator interface may be included in an ECU). Platoon controller 310 also interacts with an inter-vehicle communications controller 370 (also referred to as an inter-vehicle communications ECU) which orchestrates communications with the platoon partner and a NOC communications controller 380 (also referred to as a NOC communication ECU) that orchestrates communications with a NOC. The vehicle also may have selected configuration files 390 that include known information about the vehicle.

Some of the functional components of the platoon controller 310 include gap controller 312, a variety of estimators 314, one or more partner vehicle trackers 316 and various monitors 318. In many applications, the platoon controller 310 will include a variety of other components 319 as well.

Some of the sensors utilized by platoon controller 310 may include GNSS unit 331, wheel speed sensors 332, inertial measurement devices 334, radar unit 337, LIDAR unit 338, cameras 339, accelerator pedal position sensor 341, steering wheel position sensor 342, brake pedal position sensor 343, and various accelerometers 344. Of course, not all of these sensors will be available on all vehicles involved in a platoon and not all of these sensors are required in any particular embodiment. A variety of other sensors 349 (now existing or later developed or commercially deployed) may be additionally or alternatively be utilized by platoon controller 310 in other embodiments.

Many (but not all) of the described sensors, including wheel speed sensors 332, radar unit 337, accelerator pedal position sensor 341, steering wheel position sensor 342, brake pedal position sensor 343, and accelerometer 344 are relatively standard equipment on newer trucks (tractors) used to pull semi-trailers. However, others, such as GNSS unit 331 and LIDAR unit 338 (if used) are not currently standard equipment on such tractors or may not be present on a particular vehicle and may be installed as needed or desired to help support platooning.

FIG. 3 also illustrates various actuator controllers 350. It should be understood that, in various embodiments, some or all types of controllers may be referred to interchangeably as electronic control units (ECUs). It should, however, be understood that some ECUs may control actuators, some ECUs may control communications, some ECUs may monitor sensors, and some may perform any combination thereof. Thus, it should be appreciated that the system shown in FIG. 3 is merely one of a wide variety of systems that may be used to control platooning.

Some of the vehicle actuator controllers 350 that platoon controller 310 may direct at least in part include engine torque controller 352; brake controller 354; transmission controller 356; steering/automated steering controller 357; and clutch controller 358. Of course, not all of these actuator controllers will be available or are required in any particular embodiment and it may be desirable to interface with a variety of other vehicle actuator controllers 359 that may be available on the vehicle as well. Therefore, it should be appreciated that the specific actuator controllers 350 directed or otherwise utilized by the platoon controller on any particular controlled vehicle may vary widely. Further, the capabilities of any particular actuator controller (e.g. engine torque controller 352), as well as its interface (e.g., the nature and format of the commands, instructions, requests and messages it can handle or generate) will often vary with the make and model of that particular actuator controller. Therefore, an actuator interface 360 is preferably provided to translate requests, commands, messages and instructions from the platoon controller 310 into formats that are appropriate for the specific actuator controller hardware and software utilized on the controlled vehicle. The actuator interface 360 also provides a mechanism for communicating/translating messages, commands, instructions and requests received from the various actuator controllers back to the platoon controller 310. In some embodiments, an appropriate actuator interface may be provided to interact with each of the specific vehicle controllers utilized. In various embodiments, this may include one or more of: an engine torque interface 361; a brake interface 362; a transmission interface 364; a retarder interface 365; a steering interface 367; and/or any other appropriate controller interface 369. In some embodiments, various controllers may be combined (e.g., in the case of a chassis controller, or an engine ECU that also controls a retarder—which may obviate the need for a retarder ECU).

Large trucks and other heavy vehicles frequently have multiple systems for “braking” the truck. These include the traditional brake system assemblies mounted in the wheels of the vehicle—which are often referred to in the industry as the “foundation brakes.” Most large trucks/heavy vehicles also have an engine brake (colloquially referred to as a “retarder”) that can be used to augment the foundation brakes and/or serve as an alternative mechanism for slowing the vehicle or to help prevent the vehicle from accelerating down a hill. Often, the retarder may be controlled by the engine torque controller 352 and in such embodiments, the retarder can be controlled by sending appropriate torque commands (which may be negative) to engine torque controller 352. In other embodiments a separate retarder controller (not shown) may be accessible to, and therefore directed by, platoon controller 310 through an appropriate retarder interface 365. In still other embodiments, the platoon controller 310 may separately determine a retarder command that it sends to the actuator interface 360. In such embodiments the actuator interface will interpret the retard command and pass on appropriate retardation control commands to an Engine ECU or other appropriate vehicle controller.

The communications between vehicles may be directed over any suitable channel and may be coordinated by inter-vehicle communications controller 370. As described above, the DSRC protocol may work well.

The specific information transmitted back and forth between the vehicles may vary widely based on the needs of the controllers. In various embodiments, the transmitted information may include the current commands generated by the platoon controller 310 such as requested/commanded engine torque, and/or requested/commanded braking deceleration 382. They may also include steering commands, gear commands, etc. when those aspects are controlled by platoon controller 310. Corresponding information is received from the partner vehicle, regardless of whether those commands are generated by a platoon controller or other suitable controller on the partner vehicle (e.g., an adaptive cruise control system (ACC) or a collision mitigation system (CMS)), or through other or more traditional mechanisms—as for example, in response to driver inputs (e.g., accelerator pedal position, brake position, steering wheel position, etc.).

In many embodiments, much or all of the tractor sensor information provided to platoon controller 310 is also transmitted to the platoon partner and corresponding information is received from the platoon partner so the platoon controllers 310 on each vehicle can develop an accurate model of what the partner vehicle is doing. The same is true for any other relevant information that is provided to platoon controller 310, including any vehicle configuration information 390 that is relevant to platoon controller 310. It should be appreciated that the specific information transmitted may vary widely based on the requirements of platoon controllers 310, the sensors and actuators available on the respective vehicles, and the specific knowledge that each vehicle may have about itself.

The information transmitted between vehicles may also include information/data about intended future actions as will be discussed in greater detail below. For example, if the lead vehicle knows it is approaching a hill, it may expect to increase its torque request (or decrease its torque request in the context of a downhill) in the near future and that information can be conveyed to a rear vehicle for use as appropriate by the platoon controller 310. Of course, there is a wide variety of other information that can be used to foresee future torque or braking requests and that information can be conveyed in a variety of different forms. In some embodiments, the nature of the expected events themselves can be indicated (e.g., a hill, curve, or exit is approaching) together with the expected timing of such events. In other embodiments, the intended future actions can be reported in the context of expected control commands such as the expected torques and/or other control parameters and the timing at which such changes are expected. Of course, there are a wide variety of different types of expected events that may be relevant to the platoon control.

The communications between the vehicles and the NOC may be transmitted over a variety of different networks, such as a cellular network, various Wi-Fi networks, DSRC networks, satellite communications networks and/or any of a variety of other networks as appropriate. The communications with the NOC may be coordinated by NOC communications controller 380. The information transmitted to and/or received from the NOC may vary widely based on the overall system design. In some circumstances, the NOC may provide specific control parameters such as a target gap. These control parameters or constraints may be based on factors known at the NOC such as speed limits, the nature of the road/terrain (e.g., hilly vs. flat, winding vs. straight, etc.) weather conditions, traffic or road conditions, etc. In other circumstances the NOC may provide information such information to platoon controller 310. The NOC may also provide information about the partner vehicle including its configuration information and any known relevant information about its current operational state such as weight, trailer length, etc.

Lastly, with regard to FIG. 3, configuration file 390 may include a wide variety of information about the host vehicle that may be considered relevant to controller 310. By way of example, some of the information might include the vehicle's specification including such things as engine performance characteristics, available sensors, the existence and/or type of platooning indicators (e.g., lights that indicate a vehicle is platooning), the nature of its braking system, the location of its GNSS antenna relative to the front of the cab, gear ratios, differential ratios etc. In some embodiments, configuration file 390 may include information about a driver, a fleet, a fleet's schedule, a driver rating, a driver's ability to use the system, whether a vehicle has permission to use a system, whether a vehicle is certified to use the system, etc.

FIG. 4 illustrates an example vehicle system, in accordance with some embodiments. FIG. 4 includes a tractor-trailer 420 that comprises a tractor 420-1 and a trailer 420-2. In FIG. 4, tractor-trailer 420 is traveling on road 410. In various embodiments described herein, tractor-trailer 420 is at least semi-autonomous, and may receive signals over a wireless link (as shown in FIG. 1).

FIG. 5 illustrates an example vehicle system, in accordance with some embodiments. FIG. 5 includes tractor-trailer 520 traveling on road 510 and avoiding an adverse action, in this example, avoiding object 540. In some examples, object 540 is in the middle of a road, is a tree, a curb, the edge of a road, etc. In various embodiments, tractor-trailer 520 may receive information from a remote source, such as a multi-tenant environment (e.g., the cloud) that can assist tractor-trailer 520 with identifying object 540. Based on what type of object object 540 is, tractor-trailer 520 may drive over object 540 or drive around object 540. In some embodiments described herein, driving over object 540 may not be considered an adverse action, even though a vehicle comes into contact with object 540.

FIG. 6 illustrates an example vehicle system, in accordance with some embodiments. FIG. 6 includes tractor-trailers 620 and 630 traveling on road 610. In some embodiments, tractor-trailer 630 may include a camera or other sensor capable of gathering information within its field of view 650. Similarly, tractor-trailer 620 may include a camera or other sensor capable of gathering information within its field of view 655. In some embodiments, tractor-trailer 630 may wirelessly provide information gathered by one or more sensors located on tractor-trailer 630 to tractor-trailer 620 (or vice-versa). This information may include attributes of and/or identities of objects within a threshold distance from a sensor (e.g., within fields of view 650 and/or 655, or a portion thereof). For example, a camera on tractor-trailer 630 may gather information about itself (e.g., its location or pose), and/or about tractor-trailer 620 in front of it (tractor-trailer 630) including the pose and/or kinematics of at least a portion of tractor-trailer 620. If a camera on tractor-trailer 630 determines that tractor-trailer 620 is performing an adverse action such as its trailer's wheels rolling over lane markers, tractor-trailer 630 may transmit that information (including a derivative thereof), to tractor-trailer 620 (e.g., so it may adjust its pose). In some embodiments, the information transmitted from tractor-trailer 630 to tractor-trailer 620 may cause tractor-trailer 620 to perform actions including, but not limited to: dissolving a platoon, pulling over to the side of road 610 (e.g., out of the way of traffic), cause an audible sound/alert/notification within the cabin of tractor-trailer 620, cause one or more tractor-trailers to engage in a fully autonomous mode (e.g., self-driving without the need for a driver or another vehicle controlling it), etc.

In one or more embodiments, tractor-trailer 630 may platoon with tractor-trailer 620. In some embodiments, tractor-trailer 630 may only transmit information about tractor-trailer 620 and/or an object to tractor-trailer 620 (or vice-versa) when tractor-trailer 630 is platooning with tractor-trailer 620. In some embodiments, tractor-trailer 630 may only transmit information about tractor-trailer 620 and/or an object to tractor-trailer 620 (or vice-versa) when tractor-trailer 630 is paired with tractor-trailer 620. Two vehicles may be paired when they communicatively coupled but not platooning, designated as being paired, etc. In some embodiments, two vehicles may be designated as being paired based on their attributes (e.g., as described above in reference to determining a path for avoiding an adverse action), and/or in response to the input at a remote terminal causing the two vehicles to be paired (e.g., one or more mouse clicks that cause two vehicles to pair, one or more mouse clicks that cause two vehicles to platoon).

FIG. 7 illustrates an example vehicle system, in accordance with some embodiments. FIG. 7 includes tractor-trailers 720 and 730 traveling on road 710. As shown in FIG. 7, tractor-trailer 720 may be avoiding an adverse action such as coming into contact with object 740. In some embodiments, tractor-trailer 730 may include a camera or other sensor capable of gathering information within its field of view 750.

In some embodiments, different adverse actions may correspond with different weights (which may be referred to as penalties). For example, crossing over lane markers may have a lower weight than colliding with a tree. In such an example, tractor-trailer 720 may perform the adverse action with a lower weight. In other words, in this example tractor-trailer 720 may drive outside of its lane even though driving outside of its lane may be considered an adverse action because the weight (or penalty) associated with exiting its lane is less than the weight (or penalty) associated with colliding with object 740 (e.g., a weight (or penalty) may be part of a calculation/algorithm that determines actions to take).

In some embodiments, one vehicle may gather information from a sensor to determine whether another vehicle may exit its lane. For example, a sensor on tractor-trailer 730 may sense/identify object 740, and/or a vehicle traveling in a lane such that it may collide with tractor-trailer 720 were tractor-trailer 720 to exit its lane. In some embodiments, tractor-trailer 720 may travel out of its lane, stop, and/or dissolve a platoon in response to tractor-trailer 720 receiving information gathered by a sensor on tractor-trailer 730 (e.g., information indicating whether tractor-trailer 720 will collide with an object if it leaves its lane).

FIG. 8 illustrates an example vehicle system, in accordance with some embodiments. FIG. 8 includes a tractor-trailer 810, an object 840, a vehicle 860 traveling in an opposite direction of tractor-trailer 810, and lane markings (which can be used to determine a lane) 880 and 890. In some embodiments, tractor-trailer 810 may include fiducial markers 870A and 870B. Further, in some embodiments, tractor-trailer 810 may include sensors 895A-C.

In some embodiments, FIG. 8 is a view from a camera included in a rear vehicle that implements systems and methods described herein. For example, a rear vehicle may calculate the pose (e.g., location of and/or direction of travel) of the trailer portion of tractor-trailer 810, and/or the kinematics (e.g., an articulation angle/angular displacement, angular velocity, and/or angular acceleration of one or more portions of a tractor-trailer, such as its trailer). In response to the calculated pose and/or kinematics indicating an adverse action is about to occur or is occurring, a wireless signal may be sent from a rear vehicle to tractor-trailer 810. This wireless signal may cause a notification to be provided to a driver in a cabin of tractor-trailer 810, such as a sound or a message on a user interface (e.g., notifying a driver to pull over because their trailer is not staying within its lane). When determining whether a trailer is with in its lane, a rear vehicle may compare the pose, kinematics, and/or location of a trailer with lane markings 880 and 890. In some embodiments, the compared information may be combined with information determined by tractor-trailer 810 itself to determine a path or other action to take.

In various embodiments, a rear vehicle or a front vehicle may determine that a lane marker indicates that a vehicle may pass another vehicle. If the rear vehicle or front vehicle determines such, it may share that information with at least the front vehicle, and/or a third vehicle 860. Information indicating a vehicle is going to pass another vehicle by changing lanes may be shared via wireless signals, and may include information gathered by one or more sensors located on the rear vehicle, one or more sensors located on the front vehicle (e.g., sensors 895A-C), one or more sensors located on a third vehicle 860, a multi-tenant environment, a NOC, and/or a base station. Further, in some embodiments, a rear vehicle and/or a third vehicle 860 may detect object 840, and transmit information indicating it sensed and/or identified object 840 to tractor-trailer 810. In some embodiments, a vehicle such as tractor-trailer 810 may independently sense and/or identify object 840, and receive information from a rear vehicle (which may be platooning with it), a third vehicle 860, a base station, a NOC, and/or a multi-tenant environment that verifies object 840 exists and/or is a particular thing (e.g., a person, a curb, or a tree).

In various embodiments, example fiducial markers 870A and/or 870B, or other computer vision techniques, may be used by a rear vehicle to determine a pose of tractor-trailer 810 or a portion thereof (e.g., its trailer). In some embodiments, it is contemplated that a rear vehicle may communicate with a front vehicle in response to detecting a sensor on a front vehicle. For example, a rear vehicle may detect a front vehicle in response to detecting a sensor on the front vehicle, or a rear vehicle may not platoon unless it detects one or more particular sensors on a front vehicle.

In one or more embodiments, as described throughout, a rear truck may detect and/or calculate information about a lead vehicle. In some embodiments, a front vehicle may also detect such information about itself, and/or a rear vehicle. Such information may include a front vehicle's position, heading, bearing, length, width, kinematics, fifth wheel position, axle position, kingpin position, yaw, etc. This information, and other information, may be received at a system on a rear vehicle via sensors located on the rear vehicle, and/or via sensors and/or wireless transmissions from a front vehicle. Of course, length and width may be dimensions of a vehicle. In some embodiments a length may refer to a length of a tractor-trailer in combination (which may include multiple trailers, in some embodiments), only a tractor, only a trailer, etc. In some embodiments, a width may refer to a width of a tractor, a width of a trailer, or the widest point at any portion of the tractor-trailer combination. Further, a rear vehicle may be able to determine information about an axle on a front vehicle (e.g., its location, which may be relative to a trailer), a fifth wheel position on a front vehicle, a number of trailers and/or dollies on a front vehicle, etc.

Based on such information, for example, a rear vehicle may determine, project, and/or estimate where at least a portion of a front tractor-trailer will be in 5 seconds based on one or more of the front tractor-trailer's: tractor dimensions, trailer dimensions, steering angle, fifth wheel location, trailer length, number of trailers (i.e., a number of trailers connected to a front tractor), number of dollies, kingpin position, two kingpin positions (e.g., if there is more than one trailer), an amount of tire slip and/or a slip angle, road conditions (e.g., moisture on a road), obstacles in its path, etc. Other factors that may assist a rear vehicle determine, project, and/or estimate where a front vehicle will be include a position (relative or otherwise) of a curb, wall, or other obstacle, including other vehicles (e.g., vehicles other than a front and rear vehicle (the two of which may be platooning)).

Of course, it should be understood that any of the determinations, projections, and/or estimations described herein may be made about a front vehicle, a rear vehicle, or another vehicle, and may be made at a front vehicle, rear vehicle, multi-tenant environment (such as the cloud), or a combination thereof.

In some embodiments, systems cause actions at a front and/or rear vehicle based on this information (e.g., the vehicle kinematics of a front and/or rear vehicle). Of course, more vehicles may calculate the information described herein, and a front vehicle may also be a rear vehicle, or vice-versa (e.g., a middle vehicle may be both a front vehicle and a rear vehicle, and in some embodiments a rear vehicle may cause a front vehicle to perform actions described herein). In some embodiments, one or more vehicles may sense information, calculate/determine/project/cause actions to occur in multiple vehicles (e.g., two, three, four, or more vehicles—whether part of a platoon or otherwise). Moreover, in addition to a front or rear vehicle being a tractor-trailer, in some embodiments a front and/or rear vehicle may be a straight truck (e.g., a box truck), a passenger vehicle, etc. In some embodiments, determinations, projections, estimations, and the transmission of information related thereto may occur at any vehicle and/or infrastructure such as a cellular tower or other base station.

Actions that may be taken based on information (e.g., vehicle kinematics) may include: information a driver of actions about to occur or about obstacles via audio and/or video; actuating steering, braking, transmission, and/or torque changes; changing weightings (e.g., scaling/gain) for variables in various determination, estimation, projection, speed control, and/or other algorithms, etc.

FIG. 9 illustrates attributes 920 associated with a battery 910, in accordance with some embodiments. In various embodiments, battery 910 may be used to propel a vehicle such as the tractor-trailers described herein. In this example, attributes 920 indicate that a vehicle is active, a remaining amount of battery life is 90% of its capacity, its remaining capacity is 90 kWh, a full capacity is 100 kWh, a temperature is 29° C., and a battery cycle count is 175. In some examples, battery cycle count is an amount a battery has been used over its life. For instance, it may be a number of times the battery's capacity has been drained, and/or a total amount of charging or discharging over its life (e.g., 950 kWh discharged over the life of a 100 kWh battery could be represented as 9.5 cycles), and/or it may be a number of times a battery is charged by a certain amount (e.g., 100%, which may be two 50% charges).

FIG. 10 illustrates a graph 1000 associated with at least one battery, in accordance with some embodiments. Graph 1000 includes an x-axis representing time, and a y-axis representing a battery's capacity. Graph 1000 also includes a line indicating an amount of a vehicle's battery capacity required to arrive at a destination without platooning 1010, and a line indicating an amount of a vehicle's battery capacity required to arrive at a destination while platooning. In addition, graph 1000 includes a line representing a battery's capacity over time for a first battery 1030 with a first cycle count, and a line representing a battery's capacity over time for a second battery 1040 with a cycle count higher than the first battery 1030. As can be seen in graph 1000, a battery with a higher cycle count may drain battery capacity in less time than a battery with a lower cycle count. As such, in some embodiments, a platoon may be ordered such that a vehicle with a higher battery cycle count is behind a vehicle with a battery with a lower cycle count. In some embodiments, batteries may not be the same type, or one or more vehicles may have multiple batteries. Thus, in some embodiments, battery cycle counts may be abstracted and/or equalized by one or more ECUs/battery management systems such that the battery cycle counts may be compared using a common baseline.

FIG. 11 illustrates a flowchart of an example process, in accordance with some embodiments. Example process 1100 includes a method for causing a vehicle to avoid an adverse action, in accordance with various embodiments. While the various steps in the flowchart are presented and described sequentially, one of ordinary skill will appreciate that some or all of the steps can be executed in different orders and some or all of the steps can be executed in parallel. In one or more embodiments, if practicable, tractor-trailers described in this process may be other types of vehicles. Also, in some embodiments, not only does a rear vehicle determine and/or verify attributes of a front vehicle including whether the front vehicle is avoiding an adverse action, but a system is contemplated that performs similarly but wherein a front vehicle determines and/or verifies attributes of a rear vehicle including whether the rear vehicle is avoiding an adverse action. In some embodiments, both a front vehicle transmit data to a rear vehicle to at least in part cause the rear vehicle to avoid an adverse action and a rear vehicle may transmit data to a front vehicle to at least in part cause the rear vehicle to avoid an adverse action. Further, in one or more embodiments of the invention, one or more of the steps can be omitted, repeated, and/or performed in a different order. Accordingly, the specific arrangement of steps shown in FIG. 11 should not be construed as limiting the scope of the invention. In one or more embodiments, the steps of FIG. 11 can be performed by example systems 100, 200, 300, and/or computing system 1200.

In step 1102, in some embodiments, a communication link is established between a first tractor-trailer and a second tractor-trailer. Of course, in some embodiments, a tractor-trailer may be any type of vehicle. Using the communication link, the first tractor-trailer and the second tractor-trailer may platoon with one another. For example, the communication link may transmit information from the first tractor-trailer that causes the second tractor-trailer's brakes to activate, and/or allows the first tractor-trailer to command and/or control an amount of torque produced by an engine and/or motor in the second tractor-trailer.

In step 1104, in some embodiments, one or more dimensions of the first tractor-trailer are determined. For example, the one or more dimensions may be determined by a camera, radar, and/or LIDAR on the second tractor-trailer. In some examples, one or more dimensions of the first tractor-trailer may be determined based on information entered via a user interface in the first tractor-trailer by a driver, information entered via a user interface remote from the first tractor-trailer and a second tractor-trailer, a code that may be scanned, an image of the first tractor-trailer (e.g., taken by a camera), information provided by a multi-tenant environment (e.g., a NOC), information provided at a web application, information provided by a base station physically attached to a dock/bay, information provided by a base station connected to a facility capable of weighing a vehicle (colloquially known as a weigh station), etc.

The dimensions of the first tractor-trailer may include, but are not limited to: the shape of its tractor, the shape of its trailer, its wheelbase, the length of its trailer, the length of its tractor, its total length, the width of its trailer, the width of its tractor, its maximum width (e.g., MAX[tractor width, trailer width]), the height of its trailer, the height of its tractor, its maximum height (e.g., MAX[tractor height, trailer height]), and a distance between its fifth wheel and its cabin.

In step 1106, in some embodiments, a pose of the first tractor-trailer is determined. A pose may include a direction of travel and/or a location of the first tractor-trailer. In some embodiments, a pose may include a direction of travel and/or a location of the first tractor-trailer's tractor and/or trailer. A location may be independent of a second tractor-trailer, or it may be relative to a second tractor-trailer. For example, based on attributes of a second tractor-trailer such as wheel speed and direction of travel, the second tractor-trailer may determine a relative location of the first tractor-trailer. As described above, in various embodiments a first tractor-trailer and/or a second tractor-trailer need not be a tractor-trailer, but instead a vehicle such as a pick-up truck or passenger car.

In some embodiments, a pose of a trailer may be determined. A trailer's pose may be determined based on attributes including, but not limited to: a tractor's pose, a tractor's location (independent and/or relative to another vehicle such as a second tractor-trailer), a tractor's direction of travel, an articulation angle, a yaw angle, a yaw rate (e.g., of a trailer with respect to a tractor and/or coordinates), a trailer's dimensions (e.g., length, width, height), etc.

In some embodiments, a pose may be determined based on a combination of information gathered by sensors on both a first tractor-trailer and a second tractor-trailer, such as a camera, radar, LIDAR, wheel speed sensor, a GNSS, dimensions of at least a portion of the first tractor-trailer and second tractor-trailer, etc.

In various embodiments, a pose may be calculated by determining a location of the first tractor-trailer, a direction of travel of the first tractor-trailer, and/or one or more articulation angles of the first tractor-trailer. In one or more embodiments, one or more articulation angles may correspond to a yaw and/or yaw rate of a trailer.

In step 1108, in some embodiments, the kinematics of a vehicle may be determined. The kinematics of a vehicle may include an articulation angle/angular displacement, angular velocity, and/or angular acceleration of one or more portions of a tractor-trailer, such as its trailer. In some embodiments, a tractor-trailer's kinematics may be determined based on various attributes of the vehicle including, but not limited to: dimensions of a tractor, dimensions of a trailer, a location of a kingpin, a location of a fifth wheel, a location of an axle, a location of a dolly, and angular speeds of a tractor, trailer, and/or dolly. In some embodiments the tractor-trailer's kinematics may be determined from observations made by another vehicle. For example, a following/rear vehicle might partially or entirely determine the position of the fifth wheel on the tractor, and/or the position of the rear axle of one or more trailers. A following/rear vehicle may also determine the length of the tractor and or trailer. A following/rear may also determine the position and geometry of the steering system of the tractor.

In some embodiments, vehicle kinematics may be determined based on a combination of information gathered by sensors on both a first tractor-trailer and a second tractor-trailer, such as a camera, radar, LIDAR, wheel speed sensor, a GNSS, dimensions of at least a portion of the first tractor-trailer and second tractor-trailer, etc.

In step 1110, in some embodiments, the location of a lane or object is determined. For example, a second tractor-trailer (which may be the rear tractor-trailer in a platoon) may use one or more sensors to sense and/or identify a lane, lane markers, and/or an object. In some embodiments, at least some of a lane and/or object may be sensed and/or identified by a first tractor-trailer (which may be the front tractor-trailer in a platoon). An object may be a curb, another vehicle, etc. In various embodiments, the location of the lane or object sensed and/or identified by the second tractor-trailer may be independent of the second tractor-trailer and/or first tractor-trailer, or its location may be relative to the second tractor-trailer and/or first tractor-trailer.

In one or more embodiments, the location of a lane marker or object may be determined based at least in part on a pose and/or kinematics of a second tractor-trailer. For example, the direction that a second tractor-trailer (and/or its sensors) are facing may be used to determine a location of a lane marker or object independent of, or relative to, the first tractor-trailer. In some embodiments, it is contemplated that if a front tractor-trailer does not perform an adverse action while traveling through an envelope (e.g., the area covered by a tractor and its trailer while traveling) then the front tractor-trailer may transmit information (directly or indirectly) about the envelope it traveled through while avoiding an adverse action. In other words, a rear tractor-trailer may travel on a particular path based on the front vehicle traveling on that path. In some embodiments, to stay within an envelope created by a first tractor-trailer, a second tractor-trailer may use different commands, or amounts thereof, compared to the first tractor-trailer. For example, a system may cause a second tractor-trailer to travel at a different speed or adjust its wheel angles such that its tractor and trailer stay within the envelope created by the first tractor-trailer, even if the second tractor-trailer's tractor and/or trailer are a different size and/or weight than the first tractor-trailer.

In step 1112, in some embodiments, a path for the first tractor-trailer is determined based on its dimensions, pose, and a location of the lane or object. A path may be determined for the first tractor-trailer to travel based on the one or more dimensions of the first tractor-trailer, the pose of the first tractor-trailer, and/or the location of the lane or the object.

In one or more embodiments, a path may also, or alternatively, be determined based on attributes of a front and/or rear vehicle, which may include, but are not limited to a/an: tractor-trailer's trailer dimensions (e.g., width, length, height), tractor-trailer's tractor dimensions, gap (e.g., distance and/or headway) between a front and rear vehicle, pose, speed, articulation angle, yaw, yaw rate, turning radius, kingpin and/or fifth wheel location, location of a trailer's rear axle (e.g., which may be determined by at least the trailer including the rear axle), the kinematics and/or dimensions, number of trailers, wheelbase, coupler position, information entered by a driver into a system (e.g., via a knob, button, and/or GUI), features of a tractor-trailer designed to reduce drag and/or friction, steering angle, sensed objects (upcoming and/or passed), sensed lane markings, information provided by an inertial measurement unit, turning radius, latitude, longitude, altitude, heading, longitudinal and lateral acceleration, relative angle, type of load (e.g., type of materials a vehicle is carrying), brake status, brake pressure, path history, path projection, travel plans, vehicle size, vehicle type, brake type, current operating mode (autonomous or manual), map data, traffic information, GPS augmentation information (e.g., delays from infrastructure), wheel speed, wheel torque, gross torque, net torque, wind, rain, music, video, infotainment system, suspension, axle weight(s), transmission status (e.g., what gear the vehicle is in, what gear the vehicle was in, what gears the vehicle transferred from and to (e.g., fifth gear to fourth gear)), previous transmission status, hybrid vehicle drivetrain (e.g., a parallel hybrid or an electric hybrid), electric motor, battery, battery life, super charger, electronic throttle control, throttle pedal, brake pedal, power steering, adaptive cruise control, a blowout, interior lighting, exterior lighting, retarder, anti-lock brakes, emergency braking, engine governor, powertrain, gear ratio, wheel size, wheel type, trailer length, trailer type, trailer height, amount of trailers, trailer position, current trailer position, past trailer position, tractor type, tractor height, transceiver type, current fuel, next determined stop, projected miles remaining until fuel tanks are empty, malfunctions, turn signals, LIDAR, radar, ultrasonic sensors, wheel angle, tire pressure, tire tread depth, cabin temperature, engine temperature, trailer interior temperature, camera, fleet of vehicles, network operations center (NOC), computer vision, and intervening traffic (e.g., cut-ins, also referred to as the situation when a vehicle enters an area between a lead vehicle and a rear vehicle).

In step 1114, in some embodiments, based on the path, the first tractor-trailer is caused (e.g., by one or more vehicle systems described herein) to avoid departing from the lane or colliding with the object. For example, one or more ECUs (e.g., a PECU, EECU, and/or BECU) may cause the first tractor-trailer to avoid an adverse action based on information received from the second tractor-trailer.

FIG. 12 illustrates an example computing system 1200, in accordance with some embodiments.

In various embodiments, the calculations performed above may be discussed in the general context of computer-executable instructions residing on some form of computer-readable storage medium, such as program modules, executed by one or more computers or other devices. By way of example, and not limitation, computer-readable storage media may comprise non-transitory computer-readable storage media and communication media; non-transitory computer-readable media include all computer-readable media except for a transitory, propagating signal. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or distributed as desired in various embodiments.

This disclosure contains numerous references to a NOC and to one or more processors. According to various aspects, each of these items may include various kinds of memory, including non-volatile memory, to store one or more programs containing instructions for performing various aspects disclosed herein.

For example, as shown in FIG. 12, example computing system 1200 may include one or more computer processor(s) 1202, associated memory 1204 (e.g., random access memory (RAM), cache memory, flash memory, read only memory (ROM), electrically erasable programmable ROM (EEPROM), or any other medium that can be used to store the desired information and that can be accessed to retrieve that information, etc.), one or more storage device(s) 1206 (e.g., a hard disk, a magnetic storage medium, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory stick, etc.), and numerous other elements and functionalities. The computer processor(s) 1202 may be an integrated circuit for processing instructions. For example, the computer processor(s) may be one or more cores or micro-cores of a processor. The computing system 1200 may also include one or more input device(s) 1210, such as a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device. Further, the computing system 1200 may include one or more output device(s) 1208, such as a screen (e.g., a liquid crystal display (LCD), a plasma display, touchscreen, cathode ray tube (CRT) monitor, projector, or other display device), a printer, external storage, or any other output device. The computing system 1200 may be connected to a network 1214 (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, mobile network, or any other type of network) via a network interface connection 1218. The input and output device(s) may be locally or remotely connected (e.g., via the network 1212) to the computer processor(s) 1202, memory 1304, and storage device(s) 1206.

One or more elements of the aforementioned computing system 1200 may be located at a remote location and connected to the other elements over a network 1214. Further, embodiments of the invention may be implemented on a distributed system having a plurality of nodes, where each portion of the invention may be located on a subset of nodes within the distributed system. In one embodiment of the invention, the node corresponds to a distinct computing device. Alternatively, the node may correspond to a computer processor with associated physical memory. The node may alternatively correspond to a computer processor or micro-core of a computer processor with shared memory and/or resources.

For example, one or more of the software modules disclosed herein may be implemented in a cloud computing environment. Cloud computing environments may provide various services and applications via the Internet (e.g., the NOC). These cloud-based services (e.g., software as a service, platform as a service, infrastructure as a service, etc.) may be accessible through a Web browser or other remote interface.

Communication media can embody computer-executable instructions, data structures, and program modules, and includes any information delivery media. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. Combinations of any of the above can also be included within the scope of computer-readable media.

While the foregoing disclosure sets forth various embodiments using specific block diagrams, flowcharts, and examples, each block diagram component, flowchart step, operation, and/or component described and/or illustrated herein may be implemented, individually and/or collectively, using a wide range of hardware, software, or firmware (or any combination thereof) configurations. In addition, any disclosure of components contained within other components should be considered as examples because many other architectures can be implemented to achieve the same functionality.

The embodiments disclosed herein may also be implemented using software modules that perform certain tasks. These software modules may include script, batch, or other executable files that may be stored on a computer-readable storage medium or in a computing system. These software modules may configure a computing system to perform one or more of the example embodiments disclosed herein. One or more of the software modules disclosed herein may be implemented in a cloud computing environment.

While this disclosure has been described in terms of several aspects, there are alterations, modifications, permutations, and equivalents which fall within the scope of this disclosure. In view of the many alternative ways of implementing the methods and apparatuses of the present disclosure, it is intended that the following appended claims be interpreted to include all such alterations, modifications, permutations, and substitute equivalents as falling within the true scope of the present disclosure.

Claims

1. A method for providing a path to at least a portion of a first tractor-trailer comprising: determining a pose of the first tractor-trailer, wherein the pose of the first tractor-trailer includes a location of the first tractor-trailer, a direction of travel of the first tractor-trailer, and one or more articulation angles of the trailer of the first tractor-trailer relative to at least a portion of the tractor of the first tractor-trailer, and wherein determining the pose of the first tractor-trailer comprises: determining the location of the first tractor-trailer;determining the direction of travel of the first tractor-trailer;determining the one or more articulation angles of the trailer of the first tractor-trailer relative to the at least a portion of the tractor of the first tractor-trailer; andcalculating the pose of the first tractor-trailer based on the location of the first tractor-trailer, the direction of travel of the first tractor-trailer, and the one or more articulation angles of the first tractor-trailer;determining one or more dimensions of the first tractor-trailer, wherein the one or more dimensions of the first tractor-trailer include a length of the first tractor, a width of the first tractor, a length of the first trailer, and a width of the first trailer, wherein the first tractor is included in the first tractor-trailer, and wherein the first trailer is included in the first tractor-trailer;determining a location of a lane or an object; andproviding the path to the at least a portion of the first tractor-trailer, wherein the path avoids the departing the lane or colliding with the object, and wherein the path is determined based at least in part on the one or more dimensions of the first tractor-trailer.
2. The method of claim 1, further comprising: determining a pose of a second tractor-trailer, wherein the pose of the second tractor-trailer includes a location of the second tractor-trailer, and a direction of travel of the second tractor-trailer, and wherein determining the pose of the second tractor-trailer comprises: determining the location of the second tractor-trailer;determining the direction of travel of the second tractor-trailer; andcalculating the pose of the first tractor-trailer based on the location of the first tractor-trailer, the direction of travel of the first tractor-trailer, and the one or more articulation angles of the first tractor-trailer,wherein determining the location of the lane or the object is based on the pose of the second tractor-trailer and comprises detecting the lane or the object with a sensor included on the second tractor-trailer.
3. The method of claim 2, wherein the first tractor-trailer and the second tractor-trailer are platooning, and wherein providing the path to the at least a portion of the first tractor-trailer comprises providing the path to a platooning electronic control unity (PECU) included in the first tractor-trailer.
4. A method for providing a path to at least a portion of a rear tractor-trailer comprising: determining a pose of the rear tractor-trailer, wherein the pose of the rear tractor-trailer includes a location of the rear tractor-trailer, and a direction of travel of the rear tractor-trailer, and wherein determining the pose of the rear tractor-trailer comprises: determining, at a front tractor-trailer, the location of the rear tractor-trailer;determining, at the front tractor-trailer, the direction of travel of the rear tractor-trailer;calculating, at the front tractor-trailer, the pose of the rear tractor-trailer based on the location of the rear tractor-trailer, and the direction of travel of the rear tractor-trailer;determining one or more dimensions of the rear tractor-trailer, wherein the one or more dimensions of the rear tractor-trailer include a length of a rear tractor, a width of the rear tractor, a length of a rear trailer, and a width of the rear trailer, wherein the rear tractor is included in the rear tractor-trailer, and wherein the rear trailer is included in the rear tractor-trailer;determining a location of a lane or an object; andproviding the path to the at least a portion of the rear tractor-trailer, wherein the path avoids departing the lane or colliding with the object, and wherein the path is determined based at least in part on the one or more dimensions of the rear tractor-trailer.
5. The method of claim 4, wherein determining the location of the lane or the object is performed by both the front tractor-trailer and the rear tractor-trailer.

US Referenced Citations (348)

Number	Name	Date	Kind
3725921	Weidman et al.	Apr 1973	A
4370718	Chasek	Jan 1983	A
5166881	Akasu	Nov 1992	A
5295551	Sukonick	Mar 1994	A
5331561	Barrett	Jul 1994	A
5572449	Tang et al.	Nov 1996	A
5633456	Stander	May 1997	A
5680122	Mio	Oct 1997	A
5777451	Kobayashi et al.	Jul 1998	A
5781119	Yamashita et al.	Jul 1998	A
5815825	Tachibana et al.	Sep 1998	A
5880958	Helms et al.	Mar 1999	A
6032097	Iihoshi et al.	Feb 2000	A
6043777	Bergman et al.	Mar 2000	A
6125321	Tabata et al.	Sep 2000	A
6128559	Saitou et al.	Oct 2000	A
6188950	Tsutsumi et al.	Feb 2001	B1
6265990	Isogai et al.	Jul 2001	B1
6285929	Hashimoto	Sep 2001	B1
6304211	Boman	Oct 2001	B1
6345603	Abboud et al.	Feb 2002	B1
6356820	Hashimoto et al.	Mar 2002	B1
6397149	Hashimoto	May 2002	B1
6418370	Isogai et al.	Jul 2002	B1
6437688	Kobayashi	Aug 2002	B1
6484078	Kageyama	Nov 2002	B1
6510381	Grounds et al.	Jan 2003	B2
6604038	Lesesky et al.	Aug 2003	B1
6633006	Wolf et al.	Oct 2003	B1
6640164	Farwell et al.	Oct 2003	B1
6765495	Dunning et al.	Jul 2004	B1
6879910	Shike et al.	Apr 2005	B2
6963795	Haissig et al.	Nov 2005	B2
7110882	Moser et al.	Sep 2006	B2
7286825	Shishido et al.	Oct 2007	B2
7554435	Tengler et al.	Jun 2009	B2
7593811	Schmidt et al.	Sep 2009	B2
7596811	Lloyd et al.	Sep 2009	B2
7729823	Ruoppolo	Jun 2010	B2
7782227	Boss et al.	Aug 2010	B2
7831345	Heino et al.	Nov 2010	B2
7894982	Reeser et al.	Feb 2011	B2
8026833	Villaume et al.	Sep 2011	B2
8073574	Yamamoto et al.	Dec 2011	B2
8116921	Ferrin et al.	Feb 2012	B2
8139109	Schmiedel et al.	Mar 2012	B2
8224551	Grolle et al.	Jul 2012	B2
8275491	Ferrin et al.	Sep 2012	B2
8326473	Simpson et al.	Dec 2012	B2
8352111	Mudalige	Jan 2013	B2
8352112	Mudalige	Jan 2013	B2
8442735	Hrovat et al.	May 2013	B2
8538656	Yamashiro	Sep 2013	B2
8554468	Bullock	Oct 2013	B1
8620517	Caveney et al.	Dec 2013	B2
8649962	Davis et al.	Feb 2014	B2
8660779	Shida	Feb 2014	B2
8666587	Anderson	Mar 2014	B2
8676466	Mudalige	Mar 2014	B2
8682511	Andreasson	Mar 2014	B2
8688349	Grolle et al.	Apr 2014	B2
8738238	Rekow	May 2014	B2
8744666	Switkes et al.	Jun 2014	B2
8775060	Solyom et al.	Jul 2014	B2
8798887	Nickolaou et al.	Aug 2014	B2
8798907	Shida	Aug 2014	B2
8914225	Caskey et al.	Dec 2014	B2
8922391	Rubin et al.	Dec 2014	B2
8947531	Fischer et al.	Feb 2015	B2
8948995	Pandita et al.	Feb 2015	B2
8954272	Adam et al.	Feb 2015	B2
9037389	You	May 2015	B2
9079587	Rupp et al.	Jul 2015	B1
9141112	Loo et al.	Sep 2015	B1
9145137	Doi et al.	Sep 2015	B2
9174672	Zeng et al.	Nov 2015	B2
9224300	Lee et al.	Dec 2015	B2
9304515	Cudak et al.	Apr 2016	B2
9355423	Slusar	May 2016	B1
9367065	Dolgov et al.	Jun 2016	B2
9373149	Abhyanker	Jun 2016	B2
9384666	Harvey	Jul 2016	B1
9396661	Okamoto	Jul 2016	B2
9423794	Lind et al.	Aug 2016	B2
9449258	Palacio et al.	Sep 2016	B1
9494944	Alam et al.	Nov 2016	B2
9582006	Switkes et al.	Feb 2017	B2
9598078	Moran et al.	Mar 2017	B2
9613466	Bullock	Apr 2017	B1
9616743	Mays et al.	Apr 2017	B1
9618347	Chambers et al.	Apr 2017	B2
9632507	Kom	Apr 2017	B1
9645579	Switkes et al.	May 2017	B2
9665102	Switkes et al.	May 2017	B2
9721474	Eskilson	Aug 2017	B2
9751532	Gordon et al.	Sep 2017	B2
9771070	Zagorski et al.	Sep 2017	B2
9776638	Green	Oct 2017	B1
9799224	Okamoto	Oct 2017	B2
9823166	Dudar et al.	Nov 2017	B2
9841762	Moran et al.	Dec 2017	B2
9869560	Gordon et al.	Jan 2018	B2
9878657	Wunsche, III	Jan 2018	B2
9884631	James et al.	Feb 2018	B2
9927816	Li et al.	Mar 2018	B2
9928746	MacNeille et al.	Mar 2018	B1
9940840	Schubert et al.	Apr 2018	B1
9956964	Desnoyer et al.	May 2018	B2
9964948	Ullrich et al.	May 2018	B2
10013877	Lu et al.	Jul 2018	B2
10017039	Colavincenzo	Jul 2018	B1
10017179	Alden et al.	Jul 2018	B2
10027024	Powell	Jul 2018	B2
10031522	Moran et al.	Jul 2018	B2
10042365	Switkes et al.	Aug 2018	B2
10074894	Birnbaum et al.	Sep 2018	B1
10254764	Laubinger et al.	Apr 2019	B2
10474166	Switkes et al.	Nov 2019	B2
10482767	Miller et al.	Nov 2019	B2
10520581	Schuh et al.	Dec 2019	B2
10520952	Luckevich et al.	Dec 2019	B1
10593211	Kim	Mar 2020	B2
10618503	Lim	Apr 2020	B2
10625742	D'sa et al.	Apr 2020	B2
20010001138	Zhu et al.	May 2001	A1
20020077748	Nakano	Jun 2002	A1
20020135507	Winner et al.	Sep 2002	A1
20020152015	Seto	Oct 2002	A1
20020198632	Breed et al.	Dec 2002	A1
20040078133	Miller et al.	Apr 2004	A1
20040140143	Saeki et al.	Jul 2004	A1
20040245853	Odagawa et al.	Dec 2004	A1
20060074557	Mulligan et al.	Apr 2006	A1
20060095195	Nishimura et al.	May 2006	A1
20060106534	Kawamata et al.	May 2006	A1
20060161341	Haegebarth et al.	Jul 2006	A1
20060195250	Kawaguchi	Aug 2006	A1
20060229804	Schmidt et al.	Oct 2006	A1
20070027614	Reeser et al.	Feb 2007	A1
20070032245	Alapuranen	Feb 2007	A1
20070043502	Mudalige et al.	Feb 2007	A1
20070060045	Prautzsch	Mar 2007	A1
20070213915	Tange et al.	Sep 2007	A1
20070233337	Plishner	Oct 2007	A1
20070276597	Kato et al.	Nov 2007	A1
20080009985	Plishner	Jan 2008	A1
20080059007	Whittaker et al.	Mar 2008	A1
20080249667	Horvitz et al.	Mar 2008	A1
20080119965	McCrary	May 2008	A1
20080122652	Tengler et al.	May 2008	A1
20080255722	McClellan et al.	Oct 2008	A1
20080258890	Follmer et al.	Oct 2008	A1
20090012666	Simpson et al.	Jan 2009	A1
20090051510	Follmer et al.	Feb 2009	A1
20090062974	Tamamoto et al.	Mar 2009	A1
20090079839	Fischer et al.	Mar 2009	A1
20090118889	Heino et al.	May 2009	A1
20090157461	Wright et al.	Jun 2009	A1
20090164082	Kobayashi et al.	Jun 2009	A1
20090222186	Jensen	Sep 2009	A1
20090287412	Menzel et al.	Nov 2009	A1
20090326799	Crook	Dec 2009	A1
20100044998	Franchineau	Feb 2010	A1
20100049374	Ferrin et al.	Feb 2010	A1
20100094509	Lüke et al.	Apr 2010	A1
20100191449	Iwamoto	Jul 2010	A1
20100194638	Rivard	Aug 2010	A1
20100250088	Grolle et al.	Sep 2010	A1
20100256835	Mudalige	Oct 2010	A1
20100256836	Mudalige	Oct 2010	A1
20100256852	Mudalige	Oct 2010	A1
20100332101	Braunberger et al.	Dec 2010	A1
20110010022	Beavin	Jan 2011	A1
20110083011	DiCrescenzo	Apr 2011	A1
20110093177	Horn	Apr 2011	A1
20110112730	Rekow	May 2011	A1
20110118967	Tsuda	May 2011	A1
20110184596	Andreasson	Jul 2011	A1
20110184605	Neff	Jul 2011	A1
20110202730	Sonoda et al.	Aug 2011	A1
20110222730	Steinberg et al.	Sep 2011	A1
20110270514	Shida	Nov 2011	A1
20110270520	Kronenberg	Nov 2011	A1
20110301779	Shida	Dec 2011	A1
20120010762	Asano	Jan 2012	A1
20120059573	Nortrup	Mar 2012	A1
20120072051	Koon et al.	Mar 2012	A1
20120109421	Scarola	May 2012	A1
20120123660	Kagawa et al.	May 2012	A1
20120239268	Chen et al.	Sep 2012	A1
20120252415	Menzel et al.	Oct 2012	A1
20120259516	Grolle et al.	Oct 2012	A1
20120259538	Oexmann	Oct 2012	A1
20120323474	Breed et al.	Dec 2012	A1
20130015984	Yamashiro	Jan 2013	A1
20130018766	Christman	Jan 2013	A1
20130024084	Yamashiro	Jan 2013	A1
20130030606	Mudalige et al.	Jan 2013	A1
20130041567	Yamashiro	Feb 2013	A1
20130041576	Switkes et al.	Feb 2013	A1
20130066511	Switkes	Mar 2013	A1
20130079953	Kumabe	Mar 2013	A1
20130080040	Kumabe	Mar 2013	A1
20130080041	Kumabe	Mar 2013	A1
20130090803	Stählin et al.	Apr 2013	A1
20130116861	Nemoto	May 2013	A1
20130124064	Nemoto	May 2013	A1
20130144465	Shida	Jun 2013	A1
20130144502	Shida	Jun 2013	A1
20130151058	Zagorski et al.	Jun 2013	A1
20130158852	Stahlin et al.	Jun 2013	A1
20130165146	Stählins et al.	Jun 2013	A1
20130173114	Pillai	Jul 2013	A1
20130211624	Lind et al.	Aug 2013	A1
20130218365	Caveney et al.	Aug 2013	A1
20130317676	Cooper et al.	Nov 2013	A1
20130325306	Caveney et al.	Dec 2013	A1
20140005906	Pandita et al.	Jan 2014	A1
20140005941	Paek et al.	Jan 2014	A1
20140019031	Solyom et al.	Jan 2014	A1
20140058563	Lacaze et al.	Feb 2014	A1
20140067220	Seiler	Mar 2014	A1
20140100734	Yamashiro	Apr 2014	A1
20140107867	Yamashiro	Apr 2014	A1
20140129075	Carleton	May 2014	A1
20140142799	Ferguson et al.	May 2014	A1
20140145838	Tuukkanen	May 2014	A1
20140148994	Ando	May 2014	A1
20140172265	Funabashi	Jun 2014	A1
20140197967	Modica et al.	Jul 2014	A1
20140210645	Sharma	Jul 2014	A1
20140236449	Horn	Aug 2014	A1
20140244144	You	Aug 2014	A1
20140249693	Stark et al.	Sep 2014	A1
20140297063	Shida	Oct 2014	A1
20140303870	Switkes et al.	Oct 2014	A1
20140306799	Ricci	Oct 2014	A1
20140306826	Ricci	Oct 2014	A1
20140309836	Ollis	Oct 2014	A1
20140316671	Okamoto	Oct 2014	A1
20140316865	Okamoto	Oct 2014	A1
20140350756	Schoonmaker et al.	Nov 2014	A1
20140350793	Schräbler et al.	Nov 2014	A1
20150045993	Cooper et al.	Feb 2015	A1
20150061864	Buck, Jr. et al.	Mar 2015	A1
20150100192	Lee et al.	Apr 2015	A1
20150120137	Zeng et al.	Apr 2015	A1
20150151737	Birch et al.	Jun 2015	A1
20150153733	Ohmura et al.	Jun 2015	A1
20150153738	Al-Buraiki et al.	Jun 2015	A1
20150154871	Rothoff et al.	Jun 2015	A1
20150160014	Hewitt et al.	Jun 2015	A1
20150161894	Duncan et al.	Jun 2015	A1
20150178998	Attard et al.	Jun 2015	A1
20150251676	Golden et al.	Sep 2015	A1
20150262481	Selin	Sep 2015	A1
20150279122	Lorenzen	Oct 2015	A1
20150296019	Onishi et al.	Oct 2015	A1
20150314790	Deragarden et al.	Nov 2015	A1
20150353085	Lee	Dec 2015	A1
20150356635	Thurston	Dec 2015	A1
20150378722	Zuniga-Hernandez	Dec 2015	A1
20160009284	Tokimasa et al.	Jan 2016	A1
20160019782	Alam et al.	Jan 2016	A1
20160026187	Alam et al.	Jan 2016	A1
20160039412	Stählin	Feb 2016	A1
20160054735	Switkes et al.	Feb 2016	A1
20160102981	Hubbard et al.	Apr 2016	A1
20160187138	Chakraborty et al.	Jun 2016	A1
20160187141	Kulkarni et al.	Jun 2016	A1
20160194014	Rajendran	Jul 2016	A1
20160198303	Grotendorst et al.	Jul 2016	A1
20160267796	Hiroma et al.	Sep 2016	A1
20160272207	Dolgov et al.	Sep 2016	A1
20160273930	Wada et al.	Sep 2016	A1
20160297447	Suzuki	Oct 2016	A1
20160300186	Scharaswak et al.	Oct 2016	A1
20160359741	Cooper et al.	Dec 2016	A1
20160362048	Matthews et al.	Dec 2016	A1
20170011633	Boegel	Jan 2017	A1
20170069203	Sharma	Mar 2017	A1
20170083844	Baker et al.	Mar 2017	A1
20170115666	Kolhouse et al.	Apr 2017	A1
20170122841	Dudar et al.	May 2017	A1
20170132299	Fox et al.	May 2017	A1
20170168503	Amla et al.	Jun 2017	A1
20170174223	Munasinghe et al.	Jun 2017	A1
20170178536	Manci et al.	Jun 2017	A1
20170186327	Uysal et al.	Jun 2017	A1
20170197544	Wang et al.	Jul 2017	A1
20170212511	Ferreira et al.	Jul 2017	A1
20170227972	Sabau	Aug 2017	A1
20170235316	Shattil	Aug 2017	A1
20170238321	Sartori et al.	Aug 2017	A1
20170242095	Schuh	Aug 2017	A1
20170242443	Schuh et al.	Aug 2017	A1
20170261997	Switkes et al.	Sep 2017	A1
20170289864	Narasimha et al.	Oct 2017	A1
20170293296	Stenneth et al.	Oct 2017	A1
20170308097	Switkes et al.	Oct 2017	A1
20170309187	Lin	Oct 2017	A1
20170323244	Rani et al.	Nov 2017	A1
20170329348	Li et al.	Nov 2017	A1
20170344023	Laubinger et al.	Nov 2017	A1
20170349058	Bernier et al.	Dec 2017	A1
20170349176	Alden et al.	Dec 2017	A1
20170361762	Wunsche et al.	Dec 2017	A1
20170363430	Al-Dahle et al.	Dec 2017	A1
20180006365	Powell	Jan 2018	A1
20180018605	Light-Holets et al.	Jan 2018	A1
20180032072	Hoye	Feb 2018	A1
20180047293	Dudar	Feb 2018	A1
20180050697	Kuszmaul et al.	Feb 2018	A1
20180074514	Switkes et al.	Mar 2018	A1
20180082590	MacNeille et al.	Mar 2018	A1
20180082591	Pandy	Mar 2018	A1
20180084511	Wu et al.	Mar 2018	A1
20180095177	Peake et al.	Apr 2018	A1
20180111611	MacNeille et al.	Apr 2018	A1
20180120861	Saxena et al.	May 2018	A1
20180137763	Deragården et al.	May 2018	A1
20180143650	Klaus et al.	May 2018	A1
20180143651	Klaus et al.	May 2018	A1
20180144640	Price et al.	May 2018	A1
20180186381	Erlien et al.	Jul 2018	A1
20180188725	Cremona et al.	Jul 2018	A1
20180188744	Switkes et al.	Jul 2018	A1
20180188745	Pilkington	Jul 2018	A1
20180188746	Lesher et al.	Jul 2018	A1
20180190119	Miller et al.	Jul 2018	A1
20180190128	Saigusa	Jul 2018	A1
20180210457	Smartt et al.	Jul 2018	A1
20180210461	Cremona et al.	Jul 2018	A1
20180210462	Switkes et al.	Jul 2018	A1
20180210463	Switkes et al.	Jul 2018	A1
20180210464	Switkes et al.	Jul 2018	A1
20180211544	Smartt et al.	Jul 2018	A1
20180211545	Smartt et al.	Jul 2018	A1
20180211546	Smartt et al.	Jul 2018	A1
20180217610	Schuh et al.	Aug 2018	A1
20180284293	Pan	Oct 2018	A1
20190012919	Brandriff et al.	Jan 2019	A1
20190180629	Kim	Jun 2019	A1
20190220037	Vladimerou et al.	Jul 2019	A1
20190279513	Schuh et al.	Sep 2019	A1
20200057453	Laws et al.	Feb 2020	A1
20200092685	Fehrenbach et al.	Mar 2020	A1
20200094821	Kim	Mar 2020	A1

Foreign Referenced Citations (82)

Number	Date	Country
0982173	Mar 2000	EP
0991046	Apr 2000	EP
0991046	Mar 2005	EP
1975901	Mar 2009	EP
2390744	Nov 2011	EP
3052355	Aug 2016	EP
3316064	May 2018	EP
2540039	Jan 2017	GB
2551248	Dec 2017	GB
2557001	Jun 2018	GB
2557434	Jun 2018	GB
05170008	Jul 1993	JP
1993170008	Sep 1993	JP
2995970	Dec 1999	JP
2010030525	Feb 2010	JP
5141849	Feb 2013	JP
2014056483	Mar 2014	JP
2014145598	Aug 2014	JP
2017215681	Dec 2017	JP
2004077378	Sep 2004	WO
2009024563	Feb 2009	WO
2009043643	Apr 2009	WO
2010098554	Sep 2010	WO
2011125193	Oct 2011	WO
2011151274	Dec 2011	WO
2013006826	Jan 2013	WO
2013006826	Mar 2013	WO
2013147682	Oct 2013	WO
2013165297	Nov 2013	WO
2013187835	Dec 2013	WO
2014062118	Apr 2014	WO
2014092628	Jun 2014	WO
2014133425	Sep 2014	WO
2014137270	Sep 2014	WO
2014137271	Sep 2014	WO
2014145918	Dec 2014	WO
2015047174	Apr 2015	WO
2015047175	Apr 2015	WO
2015047176	Apr 2015	WO
2015047177	Apr 2015	WO
2015047178	Apr 2015	WO
2015047179	Apr 2015	WO
2015047181	Apr 2015	WO
2015047182	Apr 2015	WO
2015156731	Oct 2015	WO
2016065055	Apr 2016	WO
2016087901	Jun 2016	WO
2016134610	Sep 2016	WO
2016134770	Sep 2016	WO
2016135207	Sep 2016	WO
2016182489	Nov 2016	WO
2016201435	Dec 2016	WO
2017035516	Mar 2017	WO
2017048165	Mar 2017	WO
WO-2017035516	Mar 2017	WO
2017070714	Jun 2017	WO
2017148113	Sep 2017	WO
2017164792	Sep 2017	WO
2017179193	Oct 2017	WO
2017184062	Oct 2017	WO
2017184063	Oct 2017	WO
2017196165	Nov 2017	WO
2017200433	Nov 2017	WO
2017204712	Nov 2017	WO
2017209124	Dec 2017	WO
2017209666	Dec 2017	WO
2017210200	Dec 2017	WO
2018000386	Jan 2018	WO
2018035145	Feb 2018	WO
2018038964	Mar 2018	WO
2018039114	Mar 2018	WO
2018039134	Mar 2018	WO
2018043519	Mar 2018	WO
2018043520	Mar 2018	WO
2018043753	Mar 2018	WO
2018054520	Mar 2018	WO
2018085107	May 2018	WO
2018097465	May 2018	WO
2018106774	Jun 2018	WO
2018111177	Jun 2018	WO
2018135630	Jul 2018	WO
2018137754	Aug 2018	WO

Non-Patent Literature Citations (57)

Entry
“Automated Highway System: Milestone 2 Report, Task C2: Downselect System Configurations and Workshop #3” (National Automated Highway System Consortium, Troy, MI, Jun. 1997), 604 pages.
“Surface Vehicle Recommended Practice, J1939-71, Vehicle Application Layer” (SAE International, Warrendale, PA, Mar. 2011), 1201 pages.
Al Alam, Assad et al. “An Experimental Study on the Fuel Reduction Potential of Heavy Duty Vehicle Platooning”, 2010 13th International IEEE Annual Conference on Intelligent Transportation Systems, Sep. 2010, pp. 306-311.
Al Alam, Assad et al. “Establishing Safety for Heavy Duty Vehicle Platooning: A Game Theoretical Approach”, Proceedings of the 18th World Congress, The International Federation of Automatic Control (IFAC'11) Milano, Italy, Sep. 2011, pp. 3818-3823.
Alvarez, Luis & Horowitz, Roberto, “Safe Platooning in Automated Highway Systems Part I: Safety Regions Design”, Vehicle System Dynamics, vol. 32, Jul. 1999, pp. 23-55.
Aoki, Keiji, “Research and development of fully automated vehicles”, International Conference “Global/Local Innovations for Next Generation Automobiles” Part 1, paper OS5-1, Nov. 2013, 3 pages.
Bae, Hong S. et al., “Road Grade and Vehicle Parameter Estimation for Longitudinal Control Using GPS”, 2001 IEEE Intellegent Transportaion Systems Conference Proceedings, Aug. 2001, pp. 166-171.
Bergenheim, Carl et al., “Overview of Platooning Systems”, 19th ITS World Congress, Vienna, Austria, Oct. 22-26, 2012, 7 pages.
Bergenheim, Carl et al., “Vehicle-to-Vehicle Communication for a Platooning System”, Procedia—Social and Behavioral Sciences, vol. 48, Jun. 2012, pp. 1222-1233.
Bevly, David et al. “Heavy Truck Cooperative Adaptive Cruise Control: Evaluation, Testing, and Stakeholder Engagement for Near Term Deployment: Phase One Final Report”, Report to Federal Highway Administration (Auburn University, Auburn, AL, Apr. 2015), 135 pages;Retrieved Aug. 23, 2018 at http://atri-online.org/wp-content/uploads/2015/05/DATPPhase1FinalReport.pdf.
Bishop, Richard et al., “White Paper: Automated Driving and Platooning Issues and Opportunities”, ATA Technology and Maintenance Council Future Truck Program, Automated Driving and Platooning Task Force Report (Auburn Univ., Auburn, AL, Sep. 2015), 48 pages;Retrieved Nov. 17, 2017 from http://eng.aubum.edu/˜dmbevly/FHWA_AU_TRUCK_EAR/FHWA_AuburnDATP_Phase1FinalReport.
Brizzolara, Davide & Toth, Andrea, “The Emergence of Truck Platooning”, Baltic Transport Journal, Mar. 2016, pp. 58-59.
Browand, Fred et al. “Fuel Saving Achieved in the Field Test of Two Tandem Trucks”, California PATH Research Report UCB-ITS-PRR-2004-20 (U.C. Berkeley, Berkeley, CA, Jun. 2004), 29 pages.
Desjardins, Charles et al., “Cooperative Adaptive Cruise Control: A Reinforcement Learning Approach”, IEEE Transactions on Intelligent Transportation Systems, vol. 12, No. 4, Dec. 2011, pp. 1248-1260.
Erlien, Stephen M., “Shared Vehicle Control Using Safe Driving Envelopes for Obstacle Avoidance”, Ph.D Dissertation, Dept. of Mechanical Engineering (Stanford University, Stanford, CA, Mar. 2015), 182 pages.
Friedrichs, Andreas et al., “A Generic Software Architecture for a Driver Information System to Organize and Operate Truck Platoons”, International Conference on Heavy Vehicles, Paris, France, vol. 10, May 2008, pp. 250-259.
Geiger, Andreas et al., “Team AnnieWay's Entry to the 2011 Grand Cooperative Driving Challenge”, IEEE Transactions on Intelligent Transportation Systems, vol. 13, No. 3, Sep. 2012, pp. 1008-1017.
Gerdes, J. Christian & Hedrick, J. Karl, “Brake System Requirements for Platooning on an Automated Highway”, Proceedings of the American Control Conference, Seattle, WA, Jun. 1995, pp. 165-169.
Gerdes, J. Christian & Hedrick, J. Karl, “Vehicle Speed and Spacing Control via Coordinated Throttle and Brake Actuation”, Control Eng. Practice, vol. 5, No. 11, Sep. 1997, pp. 1607-1614.
Hallé, Simon, “Automated Highway Systems: Platoons of Vehicles Viewed as a Multiagent System”, M.Sc. Dissertation, Facultédes Sciences et de Gënie (Univ. Laval, Quebec, Canada, Jun. 2005), 194 pages.
Hellström, Magnus, “Engine Speed Based Estimation of the indicated Engine Torque”, Master's Thesis, Dept. of Electrical Engineering, Reg # LiTH-ISY-EX-3569-2005 (Linköpings Universitet, Linköping, Sweden, Feb. 2005), 59 pages.
Holm, Erik Jonsson, “Vehicle Mass and Road Grade Estimation Using Kalman Filter”, Master's Thesis, Dept. of Electrical Engineering, LiTH-ISY-EX—11/4491—SE (Linköpings Universitet, Linköping, Sweden, Aug. 2011), 50 pages.
Jacobson, Jan et al. “Functional Safety in Systems of Road Vehicles”, SP Report 2010:07 (SP Technical Research Institute of Sweden, Borås, Sweden, Jul. 2010), 50 pages.
Kidambi, Narayanan et al. “Methods in Vehicle Mass and Road Grade Estimation”, SAE Int. J. Passeng. Cars—Mech. Syst, vol. 7 No. 3, Apr. 2014, 2014-01-0111, 11 pages.
Korean Intellectual Property Office, ISA, “International Search Report and Written Opinion” in PCT Application No. PCT/US2012/045830, Jan. 7, 2013, 9 pages.
Kozan, Recep et al., “An Experimental Approach for Engine Mapping”, Modem Applied Science, vol. 3, No. 3, Mar. 2009, pp. 3-9.
Kunze, Ralph et al. “Efficient Organization of Truck Platoons by Means of Data Mining”, ICINCO 2010, Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, vol. 1, Funchal, Madeira, Portugal, Jan. 2010, pp. 104-113.
Kunze, Ralph et al., “Organization and Operation of Electronically Coupled Truck Platoons on German Motorways”, International Conference on Intelligent Robotics and Applications, Conference Proceedings ICIRA2009, Singapore, Dec. 2009, pp. 135-146.
Larson, Jeffrey et al., “Coordinated Route Optimization for Heavy-duty Vehicle Platoons”, Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, The Netherlands, Oct. 2013, pp. 1196-1202.
Li, Shengbo Eben & Peng, Huei, “Strategies to Minimize Fuel Consumption of Passenger Cars During Car—Following Scenarios”, 2011 American Control Conference, San Francisco, CA, Jun. 2011, pp. 2107-2112.
Lu, Xiao-Yun & Shladover, Steven E., “Automated Truck Platoon Control and Field Test”, in Road Vehicle Automation, Lecture Notes in Mobility, G. Meyer & S. Beiker (eds) (Springer Intl. Publishing, Switzeriand, Jul. 2014), pp. 247-261.
Meisen, Philipp et al. “A Data-Mining Technique for the Planning and Organization of Truck Platoons”, International Conference on Heavy Vehicles, Paris, France, vol. 10, May 2008, pp. 270-279.
Michaelian, Mark & Browand, Fred, “Field Experiments Demonstrate Fuel Savings for Close-Following”, California PATH Research Report UCB-ITS-PRR-2000-14, (U.C. Berkeley, Berkeley, CA, Sep. 2000), 28 pages.
Micheau, Philippe & Oddo, Rëmy, “Revolution Speed Limiter for Engine Subjected to Large Load Variation”,IFAC Advances in Automotive Control, Salerno, Italy, Apr. 2004, pp. 221-226.
Nowakowski, Christopher et al. “Cooperative Adaptive Cruise Control: Testing Drivers' Choices of Following Distances”, California Path Research Report UCB-ITS-PRR-2011-01 (U.C. Berkeley, Jan. 2011), 171 pages.
Nowakowski, Christopher et al., “Cooperative Adaptive Cruise Control (CACC) for Truck Platooning: Operational Concept Alternatives”, Research Report under Cooperative Agreement No. DTFH61-13-H-00012 Task 1.2, California PATH Program, (U.C. Berkeley, Berkeley, CA, Mar. 2015), 50 pages; Retrieved Aug. 25, 2017 from http://escholarship.org/uc/item/7jf9n5wm.
Nowakowski, Christopher et al., “Heavy vehicle automation: Human factors lessons learned”, Procedia Manufacturing vol. 3, Jul. 2015, pp. 2945-2952.
Packard, Andrew et al., “Section 5, Simple Cruise Control”, Class notes, ME 132:Dynamic Systems and Feedback, Dept. of Mechanical Engineering (U.C. Berkeley, Spring 2005), pp. 24-52.
Paulsson, E. & Asman, L., “Vehicle Mass and Road Grade Estimation using Recursive Least Squares”, M.Sc. Thesis, Department of Automatic Control, ISRN LUTFD2/TFRT-6009-SE (Lund University, Lund, Sweden, Jun. 2016), 51 pages.
Porche, Isaac et al., “Real Time Task Manager for Communications and Control in Multicar Platoons”, in Proceedings of the Intelligent Vehicles'92 Symposium, Detroit, MI, Jun. 29-Jul. 1, 1992, pp. 409-414.
Ramakers, Richard et al., “Electronically coupled truck platoons on German highways”, Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, TX, Oct. 2009, pp. 2409-2414.
Roeth, Michael, “CR England Peloton Technology Platooning Test Nov. 2013”, (North American Council on Freight Efficiency (NACFE.org), Fort Wayne, IN, Dec. 2013);Retrieved Aug. 23, 2018 at https://nacfe.org/wp-content/uploads/2018/02/Peloton-NACFE-Fuel-Test-Report-120213.pdf.
Sheikholeslam, Shahab & Desoer, Charles A. “Longitudinal Control of a Platoon of Vehicles; III: Nonlinear Model”, UCB Path Report UCB-ITS-PRR-90-1 (U.C. Berkeley, Berkeley, CA, Apr. 1990), 25 pages.
Sheikholeslam, Shahab & Desoer, Charles A., “A System Level Study of the Longitudinal Control of a Platoon of Vehicles”, Transactions of the ASME, vol. 114, Jun. 1992, pp. 286-292.
Sheikholeslam, Shahab & Desoer, Charles A., “Longitudinal Control of a Platoon of Vehicles”, Proceedings of the American Control Conference, San Diego, CA, May 23-25, 1990, pp. 291-296.
Shladover, Steven E. et al. “Development and Evaluation of Selected Mobility Applications for VII: Concept of Operations”, California PATH Working Paper UCB-ITS-PWP-2009-3 (U.C. Berkeley, Berkeley, CA, Mar. 2009), 14 pages.
Shladover, Steven E. et al., “Cooperative Adaptive Cruise Control: Definitions and Operating Concepts”, Transportation Research Record: Journal of the Transportation Research Board, vol. 2489, Nov. 2015, pp. 145-152.
Shladover, Steven E. et al., “Demonstration of Automated Heavy-Duty Vehicles”, California PATH Research Report UCB-ITS-PRR-2005-23 (U.C. Berkeley, Berkeley, CA, Jun. 2005), 459 pages.
Shladover, Steven E., “Development and Evaluation of Selected Mobility Applications for VII (a.k.a. IntelliDrive)”, California PATH PowerPoint Presentation (U.C. Berkeley, Berkeley, CA, Jul. 2009), 17 pages; Retrieved Jul. 21, 2017 from http://slideplayer.com/slide/6981587/.
Sugimachi, Toshiyuki et al. “Development of Autonomous Platooning System for Heavy-duty Trucks”, Proceedings of the 7th IFAC Symposium on Advances in Automotive Control, Tokyo, Japan, IFAC Proceedings vol. 16, Issue 21, Sep. 2013, pp. 52-57.
Tsugawa, Sadayuki et al., “A Review of Truck Platooning Projects for Energy Savings”, IEEE Transactions on Intelligent Vehicles, vol. 1 No. 1, Mar. 2016, pp. 68-77.
Tsugawa, Sadayuki et al., “An Automated Truck Platoon for Energy Saving”, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, Sep. 2011, pp. 4109-4114.
Tsugawa, Sadayuki et al., “An Overview on an Automated Truck Platoon within the Energy ITS Project”, Proceedings of the 7th IFAC Symposium on Advances in Automotive Control, Tokyo, Japan, IFAC Proceedings vol. 46, Issue 21, Sep. 2013, pp. 41-46.
U.S. Appl. No. 61/167,121.
Wille, Matthias et al., “KONVOI: Electronically coupled truck convoys”, in Human Factors for Assistance and Automation, D. de Waard et al. (Eds.) (Shaker Publishing, Maastricht, the Netherlands, Jan. 2008), pp. 243-256.
Zabat, Michael et al., “The Aerodynamic Performance of Platoons: Final Report”, California PATH Research Report UCB-ITS-PRR-95-35 (U.C. Berkeley, Berkeley, CA, Oct. 1995), 172 pages.
Zhao, Siyang et al., “Vehicle to Vehicle Communication and Platooning for EV with Wireless Sensor Network”, SICE Annual Conference 2015, Hangzhou, China, Jul. 2015, pp. 1435-1440.

Related Publications (1)

	Number	Date	Country
	20200324763 A1	Oct 2020	US

Systems and methods for managing tractor-trailers

Information

Patent Number

Date Filed

Date Issued

Inventors

Original Assignees

Examiners

Agents

CPC

Field of Search

CPC