Patent Application
20220289179
This description relates to trajectory checking of an autonomous vehicle.
Autonomous vehicles (AVs) typically calculate a number of possible trajectories that may be used to traverse a given space or environment.
In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, that the present disclosure can be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present disclosure.
In the drawings, specific arrangements or orderings of schematic elements, such as those representing devices, modules, systems, instruction blocks, and data elements, are shown for ease of description. However, it should be understood by those skilled in the art that the specific ordering or arrangement of the schematic elements in the drawings is not meant to imply that a particular order or sequence of processing, or separation of processes, is required. Further, the inclusion of a schematic element in a drawing is not meant to imply that such element is required in all embodiments or that the features represented by such element may not be included in or combined with other elements in some embodiments.
Further, in the drawings, where connecting elements, such as solid or dashed lines or arrows, are used to illustrate a connection, relationship, or association between or among two or more other schematic elements, the absence of any such connecting elements is not meant to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not shown in the drawings so as not to obscure the disclosure. In addition, for ease of illustration, a single connecting element is used to represent multiple connections, relationships or associations between elements. For example, where a connecting element represents a communication of signals, data, or instructions, it should be understood by those skilled in the art that such element represents one or multiple signal paths (e.g., a bus), as may be needed, to affect the communication.
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.
Several features are described hereafter that can each be used independently of one another or with any combination of other features. However, any individual feature may not address any of the problems discussed above or might only address one of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Although headings are provided, information related to a particular heading, but not found in the section having that heading, may also be found elsewhere in this description. Embodiments are described herein according to the following outline:
1. General Overview
2. System Overview
3. Autonomous Vehicle Architecture
4. Autonomous Vehicle Inputs
5. Autonomous Vehicle Planning
6. Autonomous Vehicle Control
7. Trajectory Checker
8. Example of Trajectory Safety Checking
9. Example Process for Trajectory Safety Checking
In an embodiment, an electronic device that is a component in a vehicle, such as an autonomous vehicle (AV), performs one or more safety checks for planned trajectories of the vehicle to traverse a route. The electronic device is referred to as a trajectory checker (TC) in this disclosure. In an embodiment, the TC is a component of a motion planning subsystem of the vehicle, for example, planning system 404 described in the following sections. The TC addresses higher-level functional safety requirements pertaining to the motion planning for the vehicle.
In an embodiment, as part of planning a trajectory of the vehicle, the TC is presented with a set of candidate AV trajectories and a set of perceived tracked objected identified by a perception system (for example, perception system 402 described below), along with a set of predicted future trajectories for each tracked object over a certain time horizon. The TC checks and ensures that the present trajectory being followed by the vehicle (also referred to as the “ego vehicle trajectory”) does not collide with any of the perceived tracked objects identified by the perception system. In particular, the TC determines whether a minimum safe distance can be maintained between the vehicle and the tracked objects, which can be moving along respective predicted trajectories. In addition, in some cases, the TC performs plausibility checks on the vehicle's candidate trajectory, for example, ensuring that the candidate trajectory is physically executable or “followable” by the vehicle, that is, the candidate trajectory does not have discontinuities that would render following the trajectory physically impossible. In an embodiment, operations of the TC are specified using a set of technical safety requirements (TSRs). A TSR describes one or more safety checks that the TC performs and actions taken by the TC in response to results of various checks against various input scenarios.
The subject matter described herein can provide several technical benefits. For example, the safety checks performed by the TC result in safe movement of the vehicle along a route. Risk of collisions with objects along the route is reduced due to evasive maneuvers that can be triggered by the checks performed by the TC.
As used herein, the term “autonomous capability” refers to a function, feature, or facility that enables a vehicle to be partially or fully operated without real-time human intervention, including without limitation fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles.
As used herein, an autonomous vehicle (AV) is a vehicle that possesses autonomous capability.
As used herein, “vehicle” includes means of transportation of goods or people. For example, cars, buses, trains, airplanes, drones, trucks, boats, ships, submersibles, dirigibles, etc. A driverless car is an example of a vehicle.
As used herein, “trajectory” refers to a path or route to navigate an AV from a first spatiotemporal location to second spatiotemporal location. In an embodiment, the first spatiotemporal location is referred to as the initial or starting location and the second spatiotemporal location is referred to as the destination, final location, goal, goal position, or goal location. In some examples, a trajectory is made up of one or more segments (e.g., sections of road) and each segment is made up of one or more blocks (e.g., portions of a lane or intersection). In an embodiment, the spatiotemporal locations correspond to real world locations. For example, the spatiotemporal locations are pick up or drop-off locations to pick up or drop-off persons or goods.
As used herein, “sensor(s)” includes one or more hardware components that detect information about the environment surrounding the sensor. Some of the hardware components can include sensing components (e.g., image sensors, biometric sensors), transmitting and/or receiving components (e.g., laser or radio frequency wave transmitters and receivers), electronic components such as analog-to-digital converters, a data storage device (such as a RAM and/or a nonvolatile storage), software or firmware components and data processing components such as an ASIC (application-specific integrated circuit), a microprocessor and/or a microcontroller.
As used herein, a “scene description” is a data structure (e.g., list) or data stream that includes one or more classified or labeled objects detected by one or more sensors on the AV vehicle or provided by a source external to the AV.
As used herein, a “road” is a physical area that can be traversed by a vehicle, and may correspond to a named thoroughfare (e.g., city street, interstate freeway, etc.) or may correspond to an unnamed thoroughfare (e.g., a driveway in a house or office building, a section of a parking lot, a section of a vacant lot, a dirt path in a rural area, etc.). Because some vehicles (e.g., 4-wheel-drive pickup trucks, sport utility vehicles, etc.) are capable of traversing a variety of physical areas not specifically adapted for vehicle travel, a “road” may be a physical area not formally defined as a thoroughfare by any municipality or other governmental or administrative body.
As used herein, a “lane” is a portion of a road that can be traversed by a vehicle. A lane is sometimes identified based on lane markings. For example, a lane may correspond to most or all of the space between lane markings, or may correspond to only some (e.g., less than 50%) of the space between lane markings. For example, a road having lane markings spaced far apart might accommodate two or more vehicles between the markings, such that one vehicle can pass the other without traversing the lane markings, and thus could be interpreted as having a lane narrower than the space between the lane markings, or having two lanes between the lane markings. A lane could also be interpreted in the absence of lane markings. For example, a lane may be defined based on physical features of an environment, e.g., rocks and trees along a thoroughfare in a rural area or, e.g., natural obstructions to be avoided in an undeveloped area. A lane could also be interpreted independent of lane markings or physical features. For example, a lane could be interpreted based on an arbitrary path free of obstructions in an area that otherwise lacks features that would be interpreted as lane boundaries. In an example scenario, an AV could interpret a lane through an obstruction-free portion of a field or empty lot. In another example scenario, an AV could interpret a lane through a wide (e.g., wide enough for two or more lanes) road that does not have lane markings. In this scenario, the AV could communicate information about the lane to other AVs so that the other AVs can use the same lane information to coordinate path planning among themselves.
The term “over-the-air (OTA) client” includes any AV, or any electronic device (e.g., computer, controller, IoT device, electronic control unit (ECU)) that is embedded in, coupled to, or in communication with an AV.
The term “over-the-air (OTA) update” means any update, change, deletion or addition to software, firmware, data or configuration settings, or any combination thereof, that is delivered to an OTA client using proprietary and/or standardized wireless communications technology, including but not limited to: cellular mobile communications (e.g., 2G, 3G, 4G, 5G), radio wireless area networks (e.g., Wi-Fi) and/or satellite Internet.
The term “edge node” means one or more edge devices coupled to a network that provide a portal for communication with AVs and can communicate with other edge nodes and a cloud based computing platform, for scheduling and delivering OTA updates to OTA clients.
The term “edge device” means a device that implements an edge node and provides a physical wireless access point (AP) into enterprise or service provider (e.g., VERIZON, AT&T) core networks. Examples of edge devices include but are not limited to: computers, controllers, transmitters, routers, routing switches, integrated access devices (IADs), multiplexers, metropolitan area network (MAN) and wide area network (WAN) access devices.
“One or more” includes a function being performed by one element, a function being performed by more than one element, e.g., in a distributed fashion, several functions being performed by one element, several functions being performed by several elements, or any combination of the above.
It will also be understood that, although the terms first, second, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the various described embodiments. The first contact and the second contact are both contacts, but they are not the same contact.
The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this description, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
As used herein, the term “if” is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.
As used herein, an AV system refers to the AV along with the array of hardware, software, stored data, and data generated in real-time that supports the operation of the AV. In an embodiment, the AV system is incorporated within the AV. In an embodiment, the AV system is spread across several locations. For example, some of the software of the AV system is implemented on a cloud computing environment similar to cloud computing environment 300 described below with respect to
In general, this document describes technologies applicable to any vehicles that have one or more autonomous capabilities including fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles, such as so-called Level 5, Level 4 and Level 3 vehicles, respectively (see SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, which is incorporated by reference in its entirety, for more details on the classification of levels of autonomy in vehicles). The technologies described in this document are also applicable to partially autonomous vehicles and driver assisted vehicles, such as so-called Level 2 and Level 1 vehicles (see SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems). In an embodiment, one or more of the Level 1, 2, 3, 4 and 5 vehicle systems can automate certain vehicle operations (e.g., steering, braking, and using maps) under certain operating conditions based on processing of sensor inputs. The technologies described in this document can benefit vehicles in any levels, ranging from fully autonomous vehicles to human-operated vehicles.
Autonomous vehicles have advantages over vehicles that require a human driver. One advantage is safety. For example, in 2016, the United States experienced 6 million automobile accidents, 2.4 million injuries, 40,000 fatalities, and 13 million vehicles in crashes, estimated at a societal cost of $910+ billion. U.S. traffic fatalities per 100 million miles traveled have been reduced from about six to about one from 1965 to 2015, in part due to additional safety measures deployed in vehicles. For example, an additional half second of warning that a crash is about to occur is believed to mitigate 60% of front-to-rear crashes. However, passive safety features (e.g., seat belts, airbags) have likely reached their limit in improving this number. Thus, active safety measures, such as automated control of a vehicle, are the likely next step in improving these statistics. Because human drivers are believed to be responsible for a critical pre-crash event in 95% of crashes, automated driving systems are likely to achieve better safety outcomes, e.g., by reliably recognizing and avoiding critical situations better than humans; making better decisions, obeying traffic laws, and predicting future events better than humans; and reliably controlling a vehicle better than a human.
Referring to
In an embodiment, the AV system 120 includes devices 101 that are instrumented to receive and act on operational commands from one or more computer processors 146. We use the term “operational command” to mean an executable instruction (or set of instructions) that causes a vehicle to perform an action (e.g., a driving maneuver). Operational commands can, without limitation, including instructions for a vehicle to start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate, decelerate, perform a left turn, and perform a right turn. In an embodiment, computer processor 146 is similar to the processor 304 described below in reference to
In an embodiment, the AV system 120 includes sensors 121 for measuring or inferring properties of state or condition of the vehicle 100, such as the AV's position, linear and angular velocity and acceleration, and heading (e.g., an orientation of the leading end of vehicle 100). Example of sensors 121 are GPS, inertial measurement units (IMU) that measure both vehicle linear accelerations and angular rates, wheel speed sensors for measuring or estimating wheel slip ratios, wheel brake pressure or braking torque sensors, engine torque or wheel torque sensors, and steering angle and angular rate sensors.
In an embodiment, the sensors 121 also include sensors for sensing or measuring properties of the AV's environment. For example, the sensors 121 include monocular or stereo video cameras 122 in the visible light, infrared or thermal (or both) spectra, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, speed sensors, temperature sensors, humidity sensors, and precipitation sensors.
In an embodiment, the AV system 120 includes a data storage unit 142 and memory 144 for storing machine instructions associated with computer processor 146 or data collected by sensors 121. In an embodiment, the data storage unit 142 is similar to the ROM 308 or storage device 310 described below in relation to
In an embodiment, the AV system 120 includes communications devices 140 for communicating measured or inferred properties of other vehicles' states and conditions, such as positions, linear and angular velocities, linear and angular accelerations, and linear and angular headings to the vehicle 100. These devices include Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication devices and devices for wireless communications over point-to-point or ad hoc networks or both. In an embodiment, the communications devices 140 communicate across the electromagnetic spectrum (including radio and optical communications) or other media (e.g., air and acoustic media). A combination of Vehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) communication (and, in some embodiments, one or more other types of communication) is sometimes referred to as Vehicle-to-Everything (V2X) communication. V2X communication typically conforms to one or more communications standards for communication with, between, and among autonomous vehicles.
In an embodiment, the communication devices 140 include communication interfaces. For example, wired, wireless, WiMAX, Wi-Fi, Bluetooth, satellite, cellular, optical, near field, infrared, or radio interfaces. The communication interfaces transmit data from a remotely located database 134 to AV system 120. In an embodiment, the remotely located database 134 is embedded in a cloud computing environment 200 as described in
In an embodiment, the remotely located database 134 also stores and transmits digital data (e.g., storing data such as road and street locations). Such data is stored on the memory 144 on the vehicle 100, or transmitted to the vehicle 100 via a communications channel from the remotely located database 134.
In an embodiment, the remotely located database 134 stores and transmits historical information about driving properties (e.g., speed and acceleration profiles) of vehicles that have previously traveled along trajectory 198 at similar times of day. In one implementation, such data may be stored on the memory 144 on the vehicle 100, or transmitted to the vehicle 100 via a communications channel from the remotely located database 134.
Computer processors 146 located on the vehicle 100 algorithmically generate control actions based on both real-time sensor data and prior information, allowing the AV system 120 to execute its autonomous driving capabilities.
In an embodiment, the AV system 120 includes computer peripherals 132 coupled to computer processors 146 for providing information and alerts to, and receiving input from, a user (e.g., an occupant or a remote user) of the vehicle 100. In an embodiment, computer peripherals 132 are similar to the display 312, input device 314, and cursor controller 316 discussed below in reference to
In an embodiment, the AV system 120 receives and enforces a privacy level of a passenger, e.g., specified by the passenger or stored in a profile associated with the passenger. The privacy level of the passenger determines how particular information associated with the passenger (e.g., passenger comfort data, biometric data, etc.) is permitted to be used, stored in the passenger profile, and/or stored on the cloud server 136 and associated with the passenger profile. In an embodiment, the privacy level specifies particular information associated with a passenger that is deleted once the ride is completed. In an embodiment, the privacy level specifies particular information associated with a passenger and identifies one or more entities that are authorized to access the information. Examples of specified entities that are authorized to access information can include other AVs, third party AV systems, or any entity that could potentially access the information.
A privacy level of a passenger can be specified at one or more levels of granularity. In an embodiment, a privacy level identifies specific information to be stored or shared. In an embodiment, the privacy level applies to all the information associated with the passenger such that the passenger can specify that none of her personal information is stored or shared. Specification of the entities that are permitted to access particular information can also be specified at various levels of granularity. Various sets of entities that are permitted to access particular information can include, for example, other AVs, cloud servers 136, specific third party AV systems, etc.
In an embodiment, the AV system 120 or the cloud server 136 determines if certain information associated with a passenger can be accessed by the vehicle 100 or another entity. For example, a third-party AV system that attempts to access passenger input related to a particular spatiotemporal location must obtain authorization, e.g., from the AV system 120 or the cloud server 136, to access the information associated with the passenger. For example, the AV system 120 uses the passenger's specified privacy level to determine whether the passenger input related to the spatiotemporal location can be presented to the third-party AV system, the vehicle 100, or to another AV. This enables the passenger's privacy level to specify which other entities are allowed to receive data about the passenger's actions or other data associated with the passenger.
The cloud computing environment 200 includes one or more cloud data centers. In general, a cloud data center, for example the cloud data center 204a shown in
The cloud 202 includes cloud data centers 204a, 204b, and 204c along with the network and networking resources (for example, networking equipment, nodes, routers, switches, and networking cables) that interconnect the cloud data centers 204a, 204b, and 204c and help facilitate the computing systems' 206a-f access to cloud computing services. In an embodiment, the network represents any combination of one or more local networks, wide area networks, or internetworks coupled using wired or wireless links deployed using terrestrial or satellite connections. Data exchanged over the network, is transferred using any number of network layer protocols, such as Internet Protocol (IP), Multiprotocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM), Frame Relay, etc. Furthermore, in embodiments where the network represents a combination of multiple sub-networks, different network layer protocols are used at each of the underlying sub-networks. In some embodiments, the network represents one or more interconnected internetworks, such as the public Internet.
The computing systems 206a-f or cloud computing services consumers are connected to the cloud 202 through network links and network adapters. In an embodiment, the computing systems 206a-f are implemented as various computing devices, for example servers, desktops, laptops, tablet, smartphones, Internet of Things (IoT) devices, autonomous vehicles (including, cars, drones, shuttles, trains, buses, etc.) and consumer electronics. In an embodiment, the computing systems 206a-f are implemented in or as a part of other systems.
In an embodiment, the computer system 300 includes a bus 302 or other communication mechanism for communicating information, and a processor 304 coupled with a bus 302 for processing information. The processor 304 is, for example, a general-purpose microprocessor. The computer system 300 also includes a main memory 306, such as a random-access memory (RAM) or other dynamic storage device, coupled to the bus 302 for storing information and instructions to be executed by processor 304. In one implementation, the main memory 306 is used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 304. Such instructions, when stored in non-transitory storage media accessible to the processor 304, render the computer system 300 into a special-purpose machine that is customized to perform the operations specified in the instructions.
In an embodiment, the computer system 300 further includes a read only memory (ROM) 308 or other static storage device coupled to the bus 302 for storing static information and instructions for the processor 304. A storage device 310, such as a magnetic disk, optical disk, solid-state drive, or three-dimensional cross point memory is provided and coupled to the bus 302 for storing information and instructions.
In an embodiment, the computer system 300 is coupled via the bus 302 to a display 312, such as a cathode ray tube (CRT), a liquid crystal display (LCD), plasma display, light emitting diode (LED) display, or an organic light emitting diode (OLED) display for displaying information to a computer user. An input device 314, including alphanumeric and other keys, is coupled to bus 302 for communicating information and command selections to the processor 304. Another type of user input device is a cursor controller 316, such as a mouse, a trackball, a touch-enabled display, or cursor direction keys for communicating direction information and command selections to the processor 304 and for controlling cursor movement on the display 312. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x-axis) and a second axis (e.g., y-axis), that allows the device to specify positions in a plane.
According to one embodiment, the techniques herein are performed by the computer system 300 in response to the processor 304 executing one or more sequences of one or more instructions contained in the main memory 306. Such instructions are read into the main memory 306 from another storage medium, such as the storage device 310. Execution of the sequences of instructions contained in the main memory 306 causes the processor 304 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry is used in place of or in combination with software instructions.
The term “storage media” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media includes non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, solid-state drives, or three-dimensional cross point memory, such as the storage device 310. Volatile media includes dynamic memory, such as the main memory 306. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NV-RAM, or any other memory chip or cartridge.
Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 302. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infrared data communications.
In an embodiment, various forms of media are involved in carrying one or more sequences of one or more instructions to the processor 304 for execution. For example, the instructions are initially carried on a magnetic disk or solid-state drive of a remote computer. The remote computer loads the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the computer system 300 receives the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector receives the data carried in the infrared signal and appropriate circuitry places the data on the bus 302. The bus 302 carries the data to the main memory 306, from which processor 304 retrieves and executes the instructions. The instructions received by the main memory 306 can optionally be stored on the storage device 310 either before or after execution by processor 304.
The computer system 300 also includes a communication interface 318 coupled to the bus 302. The communication interface 318 provides a two-way data communication coupling to a network link 320 that is connected to a local network 322. For example, the communication interface 318 is an integrated service digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface 318 is a local area network (LAN) card to provide a data communication connection to a compatible LAN. In some implementations, wireless links are also implemented. In any such implementation, the communication interface 318 sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.
The network link 320 typically provides data communication through one or more networks to other data devices. For example, the network link 320 provides a connection through the local network 322 to a host computer 324 or to a cloud data center or equipment operated by an Internet Service Provider (ISP) 326. The ISP 326 in turn provides data communication services through the world-wide packet data communication network now commonly referred to as the “Internet” 328. The local network 322 and Internet 328 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 320 and through the communication interface 318, which carry the digital data to and from the computer system 300, are example forms of transmission media. In an embodiment, the network 320 contains the cloud 202 or a part of the cloud 202 described above.
The computer system 300 sends messages and receives data, including program code, through the network(s), the network link 320, and the communication interface 318. In an embodiment, the computer system 300 receives code for processing. The received code is executed by the processor 304 as it is received, and/or stored in storage device 310, or other non-volatile storage for later execution.
In use, the planning system 404 receives data representing a destination 412 and determines data representing a trajectory 414 (sometimes referred to as a route) that can be traveled by the vehicle 100 to reach (e.g., arrive at) the destination 412. In order for the planning system 404 to determine the data representing the trajectory 414, the planning system 404 receives data from the perception system 402, the localization system 408, and the database system 410.
The perception system 402 identifies nearby physical objects using one or more sensors 121, e.g., as also shown in
The planning system 404 also receives data representing the AV position 418 from the localization system 408. The localization system 408 determines the AV position by using data from the sensors 121 and data from the database system 410 (e.g., a geographic data) to calculate a position. For example, the localization system 408 uses data from a GNSS (Global Navigation Satellite System) sensor and geographic data to calculate a longitude and latitude of the AV. In an embodiment, data used by the localization system 408 includes high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations of them), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types. In an embodiment, the high-precision maps are constructed by adding data through automatic or manual annotation to low-precision maps.
The control system 406 receives the data representing the trajectory 414 and the data representing the AV position 418 and operates the control functions 420a-c (e.g., steering, throttling, braking, ignition) of the AV in a manner that will cause the vehicle 100 to travel the trajectory 414 to the destination 412. For example, if the trajectory 414 includes a left turn, the control system 406 will operate the control functions 420a-c in a manner such that the steering angle of the steering function will cause the vehicle 100 to turn left and the throttling and braking will cause the vehicle 100 to pause and wait for passing pedestrians or vehicles before the turn is made.
Another input 502b is a RADAR system. RADAR is a technology that uses radio waves to obtain data about nearby physical objects. RADARs can obtain data about objects not within the line of sight of a LiDAR system. A RADAR system 502b produces RADAR data as output 504b. For example, RADAR data are one or more radio frequency electromagnetic signals that are used to construct a representation of the environment 190.
Another input 502c is a camera system. A camera system uses one or more cameras (e.g., digital cameras using a light sensor such as a charge-coupled device [CCD]) to obtain information about nearby physical objects. A camera system produces camera data as output 504c. Camera data often takes the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). In some examples, the camera system has multiple independent cameras, e.g., for the purpose of stereopsis (stereo vision), which enables the camera system to perceive depth. Although the objects perceived by the camera system are described here as “nearby,” this is relative to the AV. In some embodiments, the camera system is configured to “see” objects far, e.g., up to a kilometer or more ahead of the AV. Accordingly, in some embodiments, the camera system has features such as sensors and lenses that are optimized for perceiving objects that are far away.
Another input 502d is a traffic light detection (TLD) system. A TLD system uses one or more cameras to obtain information about traffic lights, street signs, and other physical objects that provide visual navigation information. A TLD system produces TLD data as output 504d. TLD data often takes the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). A TLD system differs from a system incorporating a camera in that a TLD system uses a camera with a wide field of view (e.g., using a wide-angle lens or a fish-eye lens) in order to obtain information about as many physical objects providing visual navigation information as possible, so that the vehicle 100 has access to all relevant navigation information provided by these objects. For example, the viewing angle of the TLD system may be about 120 degrees or more.
In some embodiments, outputs 504a-d are combined using a sensor fusion technique. Thus, either the individual outputs 504a-d are provided to other systems of the vehicle 100 (e.g., provided to a planning system 404 as shown in
In addition to the route 902, a planning system also outputs lane-level route planning data 908. The lane-level route planning data 908 is used to traverse segments of the route 902 based on conditions of the segment at a particular time. For example, if the route 902 includes a multi-lane highway, the lane-level route planning data 908 includes trajectory planning data 910 that the vehicle 100 can use to choose a lane among the multiple lanes, e.g., based on whether an exit is approaching, whether one or more of the lanes have other vehicles, or other factors that vary over the course of a few minutes or less. Similarly, in some implementations, the lane-level route planning data 908 includes speed constraints 912 specific to a segment of the route 902. For example, if the segment includes pedestrians or un-expected traffic, the speed constraints 912 may limit the vehicle 100 to a travel speed slower than an expected speed, e.g., a speed based on speed limit data for the segment.
In an embodiment, the inputs to the planning system 404 includes database data 914 (e.g., from the database system 410 shown in
As indicated previously, in an embodiment, the planning system 404 includes a Trajectory Checker (TC) component. In an embodiment, the TC is realized as a hardware electronic device that is part of the planning system 404. For example, the TC can be a microcomputer, a microcontroller, a general purpose processor or special purpose processor (e.g., FPGA or ASIC) that execute instructions to realize the safety check operations of the TC. In another embodiment, software routines corresponding to the safety check operations of the TC are programmed in memory of the planning system 404, and executed by the planning system hardware, such as processors. Examples of the safety check operations performed by the TC are described in detail in a following section.
In an embodiment, the directed graph 1000 has nodes 1006a-d representing different locations between the start point 1002 and the end point 1004 that could be occupied by a vehicle 100. In some examples, e.g., when the start point 1002 and end point 1004 represent different metropolitan areas, the nodes 1006a-d represent segments of roads. In some examples, e.g., when the start point 1002 and the end point 1004 represent different locations on the same road, the nodes 1006a-d represent different positions on that road. In this way, the directed graph 1000 includes information at varying levels of granularity. In an embodiment, a directed graph having high granularity is also a subgraph of another directed graph having a larger scale. For example, a directed graph in which the start point 1002 and the end point 1004 are far away (e.g., many miles apart) has most of its information at a low granularity and is based on stored data, but also includes some high granularity information for the portion of the graph that represents physical locations in the field of view of the vehicle 100.
The nodes 1006a-d are distinct from objects 1008a-b which cannot overlap with a node. In an embodiment, when granularity is low, the objects 1008a-b represent regions that cannot be traversed by automobile, e.g., areas that have no streets or roads. When granularity is high, the objects 1008a-b represent physical objects in the field of view of the vehicle 100, e.g., other automobiles, pedestrians, or other entities with which the vehicle 100 cannot share physical space. In an embodiment, some or all of the objects 1008a-b are a static objects (e.g., an object that does not change position such as a street lamp or utility pole) or dynamic objects (e.g., an object that is capable of changing position such as a pedestrian or other car).
The nodes 1006a-d are connected by edges 1010a-c. If two nodes 1006a-b are connected by an edge 1010a, it is possible for a vehicle 100 to travel between one node 1006a and the other node 1006b, e.g., without having to travel to an intermediate node before arriving at the other node 1006b. (When we refer to a vehicle 100 traveling between nodes, we mean that the vehicle 100 travels between the two physical positions represented by the respective nodes.) The edges 1010a-c are often bidirectional, in the sense that and vehicle 100 travels from a first node to a second node, or from the second node to the first node. In an embodiment, edges 1010a-c are unidirectional, in the sense that an vehicle 100 can travel from a first node to a second node, however the vehicle 100 cannot travel from the second node to the first node. Edges 1010a-c are unidirectional when they represent, for example, one-way streets, individual lanes of a street, road, or highway, or other features that can only be traversed in one direction due to legal or physical constraints.
In an embodiment, the planning system 404 uses the directed graph 1000 to identify a path 1012 made up of nodes and edges between the start point 1002 and end point 1004.
An edge 1010a-c has an associated cost 1014a-b. The cost 1014a-b is a value that represents the resources that will be expended if the vehicle 100 chooses that edge. A typical resource is time. For example, if one edge 1010a represents a physical distance that is twice that as another edge 1010b, then the associated cost 1014a of the first edge 1010a may be twice the associated cost 1014b of the second edge 1010b. Other factors that affect time include expected traffic, number of intersections, speed limit, etc. Another typical resource is fuel economy. Two edges 1010a-b may represent the same physical distance, but one edge 1010a may require more fuel than another edge 1010b, e.g., because of road conditions, expected weather, etc.
When the planning system 404 identifies a path 1012 between the start point 1002 and end point 1004, the planning system 404 typically chooses a path optimized for cost, e.g., the path that has the least total cost when the individual costs of the edges are added together.
In an embodiment, the controller 1102 receives data representing a desired output 1104. The desired output 1104 typically includes a velocity, e.g., a speed and a heading. The desired output 1104 can be based on, for example, data received from a planning system 404 (e.g., as shown in
In an embodiment, the controller 1102 receives feedback that is used in adjusting the inputs provided to the throttle and steering. For example, if the vehicle 100 encounters a disturbance 1110, such as a hill, the measured speed 1112 of the vehicle 100 is lowered below the desired output speed. In an embodiment, any measured output 1114 is provided to the controller 1102 so that the necessary adjustments are performed, e.g., based on the differential 1113 between the measured speed and desired output. The measured output 1114 includes measured position 1116, measured velocity 1118, (including speed and heading), measured acceleration 1120, and other outputs measurable by sensors of the vehicle 100.
In an embodiment, information about the disturbance 1110 is detected in advance, e.g., by a sensor such as a camera or LiDAR sensor, and provided to a predictive feedback system 1122. The predictive feedback system 1122 then provides information to the controller 1102 that the controller 1102 can use to adjust accordingly. For example, if the sensors of the vehicle 100 detect (“see”) a hill, this information can be used by the controller 1102 to prepare to engage the throttle at the appropriate time to avoid significant deceleration.
The controller 1102 also has a lateral tracking controller 1208 which affects the operation of a steering controller 1210. For example, the lateral tracking controller 1208 instructs the steering controller 1204 to adjust the position of the steering angle actuator 1212 depending on, e.g., feedback received by the controller 1102 and processed by the lateral tracking controller 1208.
The controller 1102 receives several inputs used to determine how to control the throttle/brake 1206 and steering angle actuator 1212. A planning system 404 provides information used by the controller 1102, for example, to choose a heading when the vehicle 100 begins operation and to determine which road segment to traverse when the vehicle 100 reaches an intersection. A localization system 408 provides information to the controller 1102 describing the current location of the vehicle 100, for example, so that the controller 1102 can determine if the vehicle 100 is at a location expected based on the manner in which the throttle/brake 1206 and steering angle actuator 1212 are being controlled. In an embodiment, the controller 1102 receives information from other inputs 1214, e.g., information received from databases, computer networks, etc.
As previously noted, in an embodiment, an AV includes a Trajectory Checker (TC) component that performs safety checks on candidate trajectories for the vehicle, including the present ego vehicle trajectory. In some embodiments, the TC is a component of the planning system 404.
In an embodiment, the planning system 404 provides, to the TC, a set of perceived tracked objects (for example, a pedestrian, another vehicle, a bicycle, among others) identified by the perception system 402, along with a set of predicted future trajectories for each tracked object over a specified time horizon. The TC performs operations, described below, to ensure that the movement of the AV following the predicted trajectory along its route does not lead to a collision with any of the tracked objects moving along their respective predicted trajectories. In particular, the TC determines whether a certain distance, for example, a minimum safe distance, can be maintained between the AV and every tracked object along their respective predicted trajectories. In an embodiment, the determination made by the TC causes the planning system 404 to adjust the driving behavior of the AV to maintain a safe distance. This can include, for example, changing the speed of the AV (e.g., slowing down or speeding up); changing the trajectory to circumvent a tracked object on the path; or causing the AV to stop moving to avoid a collision, among other adjustments.
In addition to driving in a safe manner, the AV also abides by legal driving rules, for example, obeying laws governing traffic lights, stop signs, yield signs, among others. From a functional perspective, in an embodiment, the planning/control architecture (for example, the planning system 404 or the control system 406, or both) are configured such that the safety checks for trajectories is done by the TC, while checking conformance with legal rules is performed by a trajectory ranker component before the safety checks by the TC. The trajectory ranker is also a component of the planning system 404, and it checks whether each candidate trajectory respects legal driving rules that are formulated as formal rulebook-based expressions with well-defined quantitative metrics that measure the “degree” of driving law violation of a trajectory.
In an embodiment, the trajectory ranker component is connected to the TC, e.g., directly preceding the TC in the planning system 404. In such cases, a set of candidate trajectories are fed into the trajectory ranker before they are provided to the TC. The trajectory ranker produces an ordering of its input trajectories such that trajectories that violate legal rules less are ranked higher compared to trajectories with more violations. These ranked candidate trajectories are then fed into the TC, which outputs the highest ranked (for example, a trajectory that violates the legal driving rules the least in the set of candidate trajectories) trajectory that is safe, if any. In some cases, the TC outputs more than one candidate trajectory that the TC determines are safe, and are also ranked higher than the other candidate trajectories with respect to conformance with legal driving rules.
As an example, of the interplay between the trajectory ranker and the TC, a candidate trajectory may be specified such that the AV crosses a red traffic light on a road where it is safe to cross the red light (e.g., there are no pedestrians or other vehicles whose predicted trajectories can contribute to the occurrence of a collision with the AV). The TC may determine that this trajectory is safe, and add it to the list of safe trajectories for output. However, the trajectory ranker will rank this trajectory among the lowest of the candidate trajectories because of the clear legal rule violation (e.g., driving through a red light). Since the TC will output safe trajectories ordered such that the most law-abiding trajectories (highest ranked trajectories by the trajectory ranker) are preferred, the TC is unlikely to output this trajectory as a top selection. Accordingly, it is highly unlikely that planning system will use a red light-crossing trajectory as the safe trajectory that the AV will follow.
In an embodiment, the operations of the trajectory ranker and the TC are combined in a single component in the planning system 404. In such cases, the legal driving-rule conformance check and the safety checks are performed by the same component. However, in the following sections, the description of operations performed by the TC is with respect to embodiments in which the trajectory ranker is a separate component from the TC, as described above. It is to be understood that the disclosure would also apply in some cases, as appropriate, to embodiments in which the trajectory ranker and the TC are combined in a single component.
In an embodiment, the candidate AV trajectories that are provided as input to the TC are in two categories:
The output of the TC can be specified as follows:
The following sections describe formal rules related to trajectory-checking by the TC. These sections include references to an agent or an ego vehicle with respect to which trajectory checking is performed. In an embodiment, the AV is an example of an agent or an ego vehicle.
As used herein, a “trajectory” is defined as a function τ:+→
k, where, at time instant t>0 seconds from the current time, τ(t) is a k-vector consisting of the trajectory information such as the position coordinates x, y, heading, steering angle δ, etc.
Assumption 1: It is assumed that the trajectory is discretized and is given as a finite sequence τ(t0), τ(t1), . . . , τ(tN) for some non-negative real numbers 0<t0<t1< . . . <tN and integer N≥0.
Implication of Assumption 1: Assumption 1 has the implication that the agent state does not change the state between consecutive epochs ti−1 and ti. More precisely, a trajectory τ is a step function that is right continuous with left limits, with discontinuities at t0, t1, . . . , tN. In particular, limsμτ(s̆){tilde over (=)}τ(t) so that for any time instant sϵ+, τ(s)=τ(min(max(tn(s), t0), tN)), where n(s)=max{n ϵ{0, . . . , N}: tn≤s}.
Let B(τ(t)) denote a “bounding set” that entirely contains a road agent along a trajectory τ at time t, where B(τ(t)) is given in the coordinate system under consideration. For example, B(τ(t)) may be the minimal circle that inscribes the road agent and whose center is the centroid of the agent's geometry.
Assumption 2: It is assumed that all agent positional information, as well as road geometry, occlusions, etc., are given with respect to a common metric space with a well-defined distance function d.
Assumption 3: While the implementation-level specification of the bounding set B(τ(t)) is, in some embodiments, immaterial with respect to the level of requirements presented herein, it is assumed that the bounding set satisfies both of the following conditions:
Let ts(τ)=max{t ϵ{t0, . . . , tN}: ∥τ(t)·p′∥>0}, where τ(t)·p ϵ2 where τ(t)·p ϵ
2 denotes the position (sub) vector of waypoint τ(t), and τ|(t)·p′ is the Jacobian (velocity vector) of the position of the agent at time t along trajectory τ; i.e., ts(τ) is the first time at which the agent will arrive to a full stop, where ts(τ)=tN if no such n exists. The norm in the definition is that induced by the metric d.
In various embodiments, each requirement will have at least one of the following attributes: (1) Unique identification; (2) a status of “proposed,” “assumed,” “accepted,” “reviewed,” “delivered,” or “verified;” and (3) an identified automotive safety integrity level (ASIL). For example, it is assumed that all requirements herein are technical safety requirements (TSRs) with an integrity level ASIL B(D) that contribute to the achievement of an ASIL D→ASIL B(D)+ASIL B(D) decomposition of the Trajectory Checking safety function. Generally, for the purposes of discussion of requirements below, the key words “MUST,” “MUST NOT,” “REQUIRED,” “SHALL,” “SHALL NOT,” “SHOULD,” “SHOULD NOT,” “RECOMMENDED,” “MAY,” and “OPTIONAL” are interpreted as described in Network Working Group Request for Comments 2119 (RFC2119), published March 1997, at: https://tools.ietf.org/html/rfc2119.
The TC requirements generally fall into one of the following categories:
Timing
Ego vehicle trajectory plausibility checks;
Ego vehicle road boundary checks;
Collision detection between ego vehicle and other perceived road object trajectories
Traffic Control related checks
In some cases, the dynamic model of vehicle motion used for the AV is the pure dynamic bicycle model. The following includes discussion of various AV trajectory plausibility checks:
[SAF-TC-0001][proposed][ASIL B(D)]: The TC SHALL ensure that an input AV trajectory is physically executable by the AV. For an AV trajectory τ and given checking horizon h>0 with h=∞ if τ is an SSA, let H(τ)≡max {n ϵ{0, . . . , N}: tn≤h} with H(τ)=0 if no such n exists. Trajectory τ is physically executable if all of the following holds:
For every n ϵ{1, . . . , H(τ)} the following SHALL hold for waypoint τ(tn):
a. The forward acceleration is at most 0.8 m/s2
b. The longitudinal acceleration is at most 3.5 m/s2
c. The lateral acceleration is at most 6.0 m/s2
d. The jerk is at most 50.0 m/s3
e. The steering rate is at most 0.3
f. The steering acceleration is at most 10
The stability check in the foregoing requirement may be decomposed into checking every component in a waypoint individual with a parameter-specific deviation tolerances. More precisely, given maximum deviation tolerances (Δ1, . . . Δk)ϵ+k and for every n ϵ{1, . . . , H(τ)}, ∥τi(tn)—NextStatei(τ(tn−1), un−1)∥≤Δi SHALL hold for every i ϵ{1, . . . , k}.
Requirement SAF-TC-0001 handles both nominal and SSA trajectories. An SSA trajectory needs to be checked entirely up to its given horizon tN irrespective of the given checking horizon. This is specified by setting the given checking horizon h to ∞ if the trajectory under check is an SSA.
[SAF-TC-0004][proposed][ASIL B(D)]: Among the set of input AV trajectories, the TC SHALL drop the trajectories that are generated prior to the latest trajectory generator execution cycle.
[SAF-TC-0005][proposed][ASIL B(D)]: The TC SHALL output an empty AV trajectory set in response to an empty set of input AV trajectories.
[SAF-TC-0006][proposed][ASIL B(D)]: The TC SHALL produce its output trajectory set at or before the initiation of its next execution cycle.
[SAF-TC-0007][proposed][QM]: The trajectory set output by the TC SHALL be ordered by non-decreasing ranking according to the ranking specified by the trajectory ranker for the input trajectory set; i.e., the trajectory with the least legal rule violation SHALL be output first.
The following includes discussion of an AV vehicle road boundary check:
[SAF-TC-0008][proposed][ASIL B(D)]: The TC SHALL ensure that the AV stays within a predefined minimum separation distance away from the provided drivable area bounds for every waypoint on a given AV trajectory.
If the drivable area is specified as a set of (at least one) path-minimum separation pairs {(fi, αi)}, fi: [ai, bi]→2, then an ego trajectory τ is said to be within the drivable area if for every i and every t≤min(h, tN), d(τ(t), fi)≡min{d(B(τ(t)), fi(x)): x ϵ[ai, bi]}>αi holds.
The following includes discussion of collision detection between an AV and other perceived road objects. For the purpose of discussion herein, let d(X, Y) denote the (minimum) distance between two subsets X, Y of a metric space equipped with distance (metric) d; i.e., d(X, Y)=min{d(x, y): x ϵ X, y ϵ Y:
[SAF-TC-0002][proposed][ASIL B(D)]: For current input AV state sego and minimum clearance ϵ>0 distance, the TC SHALL ensure that d(B(sego), B(o))>ϵ for the most recently perceived tracked objects o.
For two trajectories τ1 and τ2 with stopping times ts1≡ts(τ1) and ts2≡ts(τ2), and for given clearance distance ϵ>0, safe angle θ>0, and collision checking horizon h>0 with h=∞ if either τ1 or τ2 is an SSA, trajectories τ1 and τ2 are NOT colliding over the interval spanning current time until h ahead of the current time if either of the following holds:
Generally, the statement “trajectory τ1 does not collide with trajectory τ2” is expressed as τ1 ∩ τ2=∅. Note: The collision operator ∩ is not commutative. That is: τ1 ∩ τ2=∅ does not necessarily imply that τ2 ∩ τ1=∅.
Two trajectory sets and
2 are not colliding, denoted as
1 ∩
2=∅, if τ1 ∩ τ2=∅ for every τ1 ϵ
1 and τ2 ϵ
2. A trajectory τ does not collide with a trajectory set
, denoted as τ ∩
=∅, if τ ∩ τ′=518 for every τ′ ϵ
.
[SAF-TC-0003][proposed][ASIL B(D)]: For perceived agents a1, . . . , am with predicted trajectory sets a
a
a
a
In the example of
Upon receiving the information about the object 1306, the TC predicts, as part of the safety check for the trajectory 1302, that at a random time instant the object 1306 may turn hard right and start crossing the road 1304 in front of the vehicle 100. As described by the equations in the example below, the TC determines that, given maximum acceleration, it will take 1.2 seconds (from the present time) for the object 1306 to get in front of the vehicle (at its present position) (this is referred to as the crossing time, tcross). The TC determines that the vehicle 100 will have moved past the object 1306 (passing time, tpass) in 2.6 seconds from its present position at its current velocity. For the vehicle to move past the object 1306 without collision, the vehicle has to be at the location indicated by line 1312 by at most 1.4 seconds (referred to as the “Safe Time,” ST) before tpass. Since the Safe Time is greater than tcross, the vehicle 100 has to brake to avoid collision. The TC computes that it will take the vehicle 1.4 seconds (and 97 feet) to come to a stop before reaching the location where the object 1306 crosses the road 1304. Accordingly, the vehicle 100 has to start braking in at most 1.2 seconds from the present time, at the location indicated by the line 1310. This time is referred to as the “Latest Brake Time” (LBT).
Assumptions:
Time it takes the vehicle 100 to pass the object 1306 (tpass)
t
pass
=y
ped/(vped+vego)
Time it takes the object 1306 to be on the road 1304 in front of the vehicle 100 (tcross)
t
cross=SQRT(2*xped/aped)
Time before passing at which braking must start (tbrake)
t
brake
=b
min
/v
ego
=v
ego/(2*μ*g), where:
In the illustrated example, the TC determines, based on information from the perception system 402, that:
Upon performing computations with the above equations using the values above, the TC determines that tpass is 2.6 seconds, while the time (from the present instant) at which the object 1306 is expected to be in the path of the vehicle 100, tcross is 1.2 seconds. The vehicle 100 -cross will have avoided the collision if it has crossed the line 1312 before the object 1306 gets to that location, and the corresponding Safe Time (ST) is 1.4 seconds. The TC computes tbrake to be 1.4 seconds, and Latest Brake Time (LBT) is 1.2 seconds. Accordingly, the TC determines that there exists a danger zone 1308 between the ST/location indicated by line 1312 and the LBT/location indicated by line 1310, where:
Following determination of the danger zone 1308, the TC concludes that the ego vehicle trajectory 1302 is no longer safe, and takes actions to ensure that collision with the object 1306 is avoided. For example, in an embodiment, the TC updates the trajectory 1302 by adjusting the velocity of the vehicle 100 as described below, causing the planning system 404 to send the update to the control system 406, which in turn adjusts the driving behavior of the vehicle 100 to avoid collision with the object 1306.
The TC determines that the danger zone 1308 can be eliminated, or the size of the danger zone 1308 can be limited, by applying a speed (velocity) constraint to the vehicle 100, reducing the velocity of the vehicle 100. As described by the equations in the example below, the TC computes a speed constraint that will set the danger zone to zero.
Speed Constraint to avoid danger zone→must have tbrake<tcross at minimum
v
ego<2*mu*g*SQRT(2*xped/aped)*(1−SF) where
Applying the above equation using the example values mentioned previously, the TC determines that the velocity of the vehicle 100 has to be reduced, and computes the reduced velocity to be 34 mph=50 ft/s. This is illustrated in
While the TC determines to reduce the velocity of the vehicle in the example embodiment above, in some embodiments, the TC may determine, using the above computations, to increase the velocity of the vehicle, such that the vehicle accelerates and quickly moves past the object 1306 before the object crosses the path 1304, thus avoiding a collision. Additionally or alternatively, in an embodiment, the TC avoids a collision by modifying the trajectory 1302 such that the path travelled by the vehicle 100 changes. For example, the TC may determine to heading of the vehicle 100 (in addition or as an alternative to adjusting the velocity) such that the vehicle swerves and moves around the object 1306.
In an embodiment, the TC re-computes values of the various variables using the above equations every cycle to update the distance of the object 1306, the danger zone 1308, and the speed constraint.
In an embodiment, the perception system 402 detects multiple objects in the environment of the vehicle's trajectory 1302, and the information about these objects is provided to the TC. Upon receiving this information, the TC selects one of the objects that is determined to be closest to the trajectory 1302 (that is, closest to the road 1304) as the object most likely to cause a collision with the vehicle 100, and determines a distance of the object from the present location of the vehicle. The TC uses the determined distance to calculate the size of the danger zone. In the above example, assuming multiple objects are detected, the object 1306 is taken to be the object that is closest to the trajectory 1302, and the danger zone 1308 is determined accordingly.
In the process 1400, the TC component identifies a proposed trajectory of a vehicle (1402). For example, the TC component device obtains the trajectory of the vehicle 100 from the planning system 404. As described with respect to
The TC determines a predicted trajectory of an object external to the vehicle (1404). For example, the TC obtains information about one or more objects detected by the perception system 402, including the object 1306. The TC determines that the object 1306 is closest to the trajectory of the vehicle 100, and predicts a trajectory of the object 1306 using the obtained information, as described with respect to
The TC obtains a velocity of the vehicle (1406). For example, the TC obtains the velocity of the vehicle 100, vego.
The TC predicts a likelihood of collision between the vehicle and the object based on the proposed vehicle trajectory and velocity and the predicted object trajectory (1408). For example, with knowledge about the trajectories, velocities, and positions of the vehicle 100 and the object 1306, the TC performs the computations as described with respect to
The TC determines a change to a parameter of the proposed trajectory of the vehicle (1410). For example, upon determining the likelihood of collision between the vehicle 100 and the object 1306, the TC applies a safety constraint to the vehicle 100. As described with respect to
The TC adjusts the proposed trajectory based on the change to the parameter (1412). For example, the TC slows down the speed of the vehicle 100 along its trajectory 1302 by applying the constrained velocity vcon. By doing so, the TC eliminates the danger zone 1308 as described with respect to
In the foregoing description, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. The description and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. In addition, when we use the term “further comprising,” in the foregoing description or following claims, what follows this phrase can be an additional step or entity, or a sub-step/sub-entity of a previously-recited step or entity.
This application claims the benefit of priority to U.S. Provisional Patent Application No. 63/161,412, filed Mar. 15, 2021, the entire contents of which are incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 63161412 | Mar 2021 | US |
