Patent Application
20210078580
This description relates to modifying a vehicle route to improve vehicle location information.
Autonomous vehicles can travel without requiring a human driver. There are various technologies involved in controlling the vehicle to follow a route to an intended destination. One aspect of controlling the vehicle includes determining the vehicle location along the route. Existing technologies for determining the vehicle location are based on, for example, information from GNSS satellite signals, map data, and on-vehicle sensor observation of the environment near the vehicle. While such technologies are useful, there are situations in which the availability of such information is limited resulting in potentially decreased precision in vehicle location information.
An illustrative example embodiment of a system for controlling a vehicle includes at least one sensor configured to detect at least one localization reference and at least one processor configured to determine a location of the vehicle with a first precision based on an indication from the at least one sensor while the vehicle is traveling in a first lane of a roadway. The processor is configured to determine that at least one characteristic of the first precision is below a threshold and, based on the at least one characteristic being below the threshold, maneuver the vehicle to a second lane of the roadway.
An illustrative example embodiment of a computer-implemented method includes determining a location of a vehicle with a first precision based on at least one localization reference while the vehicle, which includes at least one processor and at least one sensor, is traveling in a first lane of a roadway. The method includes determining that the first precision has at least one characteristic that is below a threshold and, based on the first precision being below the threshold, maneuvering the vehicle, using the at least one processor, to a second lane of the roadway.
Another illustrative example embodiment of a system for controlling a vehicle includes at least one sensor configured to detect at least one localization reference. A processor is configured to determine that at least one obstruction near the vehicle is preventing the at least one sensor from detecting the at least one localization reference while the vehicle is traveling in a lane of a roadway and, based on the determination, alter a speed of the vehicle while in the lane to change a position of the vehicle relative to the obstruction until the obstruction no longer prevents the sensor from detecting the at least one localization.
These and other aspects, features, and implementations can be expressed as methods, apparatus, systems, components, program products, means or steps for performing a function, and in other ways.
These and other aspects, features, and implementations will become apparent from the following descriptions, including the claims.
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 invention. It will be apparent, however, that the present invention may 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 invention.
In the drawings, specific arrangements or orderings of schematic elements, such as those representing devices, modules, 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 effect 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. Autonomous Vehicle Route Modification
Embodiments disclosed in this description provide improved vehicle location by, for example, maneuvering the vehicle into a different lane on a roadway to increase the amount of localization reference information available for determining the location of the vehicle. Some example embodiments include maneuvering the vehicle out of a lane that lacks sufficient lane markings to demarcate the lane into another lane where better lane markings are present. Other example embodiments include maneuvering the vehicle out of a lane where an obstruction hinders reception or detection of a GPS satellite signal into another lane where the signal is detectable.
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.
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. 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 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., WiFi) 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 A300 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 may 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 A120 includes devices A101 that are instrumented to receive and act on operational commands from the computer processors A146. In an embodiment, computing processors A146 are similar to the processor A304 described below in reference to
In an embodiment, the AV system A120 includes sensors A121 for measuring or inferring properties of state or condition of the AV A100, such as the AV's position, linear and angular velocity and acceleration, and heading (e.g., an orientation of the leading end of AV A100). Example of sensors A121 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 A121 also include sensors for sensing or measuring properties of the AV's environment. For example, monocular or stereo video cameras A122 in the visible light, infrared or thermal (or both) spectra, LiDAR A123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, speed sensors, temperature sensors, humidity sensors, and precipitation sensors.
In an embodiment, the AV system A120 includes a data storage unit A142 and memory A144 for storing machine instructions associated with computer processors A146 or data collected by sensors A121. In an embodiment, the data storage unit A142 is similar to the ROM A308 or storage device A310 described below in relation to
In an embodiment, the AV system A120 includes communications devices A140 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 AV A100. 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 A140 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 A140 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 A134 to AV system A120. In an embodiment, the remotely located database A134 is embedded in a cloud computing environment A200 as described in
In an embodiment, the remotely located database A134 also stores and transmits digital data (e.g., storing data such as road and street locations). Such data is stored on the memory A144 on the AV A100, or transmitted to the AV A100 via a communications channel from the remotely located database A134.
In an embodiment, the remotely located database A134 stores and transmits historical information about driving properties (e.g., speed and acceleration profiles) of vehicles that have previously traveled along trajectory A198 at similar times of day. In one implementation, such data may be stored on the memory A144 on the AV A100, or transmitted to the AV A100 via a communications channel from the remotely located database A134.
Computing devices A146 located on the AV A100 algorithmically generate control actions based on both real-time sensor data and prior information, allowing the AV system A120 to execute its autonomous driving capabilities.
In an embodiment, the AV system A120 includes computer peripherals A132 coupled to computing devices A146 for providing information and alerts to, and receiving input from, a user (e.g., an occupant or a remote user) of the AV A100. In an embodiment, peripherals A132 are similar to the display A312, input device A314, and cursor controller A316 discussed below in reference to
The cloud computing environment A200 includes one or more cloud data centers. In general, a cloud data center, for example the cloud data center A204a shown in
The cloud A202 includes cloud data centers A204a, A204b, and A204c along with the network and networking resources (for example, networking equipment, nodes, routers, switches, and networking cables) that interconnect the cloud data centers A204a, A204b, and A204c and help facilitate the computing systems' A206a-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 A206a-f or cloud computing services consumers are connected to the cloud A202 through network links and network adapters. In an embodiment, the computing systems A206a-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 A206a-f are implemented in or as a part of other systems.
In an embodiment, the computer system A300 includes a bus A302 or other communication mechanism for communicating information, and a hardware processor A304 coupled with a bus A302 for processing information. The hardware processor A304 is, for example, a general-purpose microprocessor. The computer system A300 also includes a main memory A306, such as a random-access memory (RAM) or other dynamic storage device, coupled to the bus A302 for storing information and instructions to be executed by processor A304. In one implementation, the main memory A306 is used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor A304. Such instructions, when stored in non-transitory storage media accessible to the processor A304, render the computer system A300 into a special-purpose machine that is customized to perform the operations specified in the instructions.
In an embodiment, the computer system A300 further includes a read only memory (ROM) A308 or other static storage device coupled to the bus A302 for storing static information and instructions for the processor A304. A storage device A310, such as a magnetic disk, optical disk, solid-state drive, or three-dimensional cross point memory is provided and coupled to the bus A302 for storing information and instructions.
In an embodiment, the computer system A300 is coupled via the bus A302 to a display A312, 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 A314, including alphanumeric and other keys, is coupled to bus A302 for communicating information and command selections to the processor A304. Another type of user input device is a cursor controller A316, such as a mouse, a trackball, a touch-enabled display, or cursor direction keys for communicating direction information and command selections to the processor A304 and for controlling cursor movement on the display A312. 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 A300 in response to the processor A304 executing one or more sequences of one or more instructions contained in the main memory A306. Such instructions are read into the main memory A306 from another storage medium, such as the storage device A310. Execution of the sequences of instructions contained in the main memory A306 causes the processor A304 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 A310. Volatile media includes dynamic memory, such as the main memory A306. 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 A302. 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 A304 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 A300 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 A302. The bus A302 carries the data to the main memory A306, from which processor A304 retrieves and executes the instructions. The instructions received by the main memory A306 may optionally be stored on the storage device A310 either before or after execution by processor A304.
The computer system A300 also includes a communication interface A318 coupled to the bus A302. The communication interface A318 provides a two-way data communication coupling to a network link A320 that is connected to a local network A322. For example, the communication interface A318 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 A318 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 A318 sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.
The network link A320 typically provides data communication through one or more networks to other data devices. For example, the network link A320 provides a connection through the local network A322 to a host computer A324 or to a cloud data center or equipment operated by an Internet Service Provider (ISP) A326. The ISP A326 in turn provides data communication services through the world-wide packet data communication network now commonly referred to as the “Internet” A328. The local network A322 and Internet A328 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 A320 and through the communication interface A318, which carry the digital data to and from the computer system A300, are example forms of transmission media. In an embodiment, the network A320 contains the cloud A202 or a part of the cloud A202 described above.
The computer system A300 sends messages and receives data, including program code, through the network(s), the network link A320, and the communication interface A318. In an embodiment, the computer system A300 receives code for processing. The received code is executed by the processor A304 as it is received, and/or stored in storage device A310, or other non-volatile storage for later execution.
In use, the planning module B104 receives data representing a destination B112 and determines data representing a trajectory B114 (sometimes referred to as a route) that can be traveled by the AV A100 to reach (e.g., arrive at) the destination B112. In order for the planning module B104 to determine the data representing the trajectory B114, the planning module B104 receives data from the perception module B102, the localization module B108, and the database module B110.
The perception module B102 identifies nearby physical objects using one or more sensors A121, e.g., as also shown in FIG. Al. The objects are classified (e.g., grouped into types such as pedestrian, bicycle, automobile, traffic sign, etc.) and a scene description including the classified objects B116 is provided to the planning module B104.
The planning module B104 also receives data representing the AV position B118 from the localization module B108. The localization module B108 determines the AV position by using data from the sensors A121 and data from the database module B110 (e.g., a geographic data) to calculate a position. For example, the localization module B108 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 module B108 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 module B106 receives the data representing the trajectory B114 and the data representing the AV position B118 and operates the control functions B120a-c (e.g., steering, throttling, braking, ignition) of the AV in a manner that will cause the AV A100 to travel the trajectory B114 to the destination B112. For example, if the trajectory B114 includes a left turn, the control module B106 will operate the control functions B120a-c in a manner such that the steering angle of the steering function will cause the AV A100 to turn left and the throttling and braking will cause the AV A100 to pause and wait for passing pedestrians or vehicles before the turn is made.
Another input C102b 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 C102b produces RADAR data as output C104b. For example, RADAR data are one or more radio frequency electromagnetic signals that are used to construct a representation of the environment A190.
Another input C102c 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 C104c. 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 use, the camera system may be configured to “see” objects far, e.g., up to a kilometer or more ahead of the AV. Accordingly, the camera system may have features such as sensors and lenses that are optimized for perceiving objects that are far away.
Another input C102d 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 C104d. 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 AV A100 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 C104a-d are combined using a sensor fusion technique. Thus, either the individual outputs C104a-d are provided to other systems of the AV A100 (e.g., provided to a planning module B104 as shown in
In addition to the route D102, a planning module also outputs lane-level route planning data D108. The lane-level route planning data D108 is used to traverse segments of the route D102 based on conditions of the segment at a particular time. For example, if the route D102 includes a multi-lane highway, the lane-level route planning data D108 includes trajectory planning data D110 that the AV A100 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 D108 includes speed constraints D112 specific to a segment of the route D102. For example, if the segment includes pedestrians or un-expected traffic, the speed constraints D112 may limit the AV A100 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 module B104 includes database data D114 (e.g., from the database module B110 shown in
In an embodiment, the directed graph D200 has nodes D206a-d representing different locations between the start point D202 and the end point D204 that could be occupied by an AV A100. In some examples, e.g., when the start point D202 and end point D204 represent different metropolitan areas, the nodes D206a-d represent segments of roads. In some examples, e.g., when the start point D202 and the end point D204 represent different locations on the same road, the nodes D206a-d represent different positions on that road. In this way, the directed graph D200 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 D202 and the end point D204 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 AV A100.
The nodes D206a-d are distinct from objects D208a-b which cannot overlap with a node. In an embodiment, when granularity is low, the objects D208a-b represent regions that cannot be traversed by automobile, e.g., areas that have no streets or roads. When granularity is high, the objects D208a-b represent physical objects in the field of view of the AV A100, e.g., other automobiles, pedestrians, or other entities with which the AV A100 cannot share physical space. In an embodiment, some or all of the objects D208a-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 D206a-d are connected by edges D210a-c. If two nodes D206a-b are connected by an edge D210a, it is possible for an AV A100 to travel between one node D206a and the other node D206b, e.g., without having to travel to an intermediate node before arriving at the other node D206b. (When we refer to an AV A100 traveling between nodes, we mean that the AV A100 travels between the two physical positions represented by the respective nodes.) The edges D210a-c are often bidirectional, in the sense that an AV A100 travels from a first node to a second node, or from the second node to the first node. In an embodiment, edges D210a-c are unidirectional, in the sense that an AV A100 can travel from a first node to a second node, however the AV A100 cannot travel from the second node to the first node. Edges D210a-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 module B104 uses the directed graph D200 to identify a path D212 made up of nodes and edges between the start point D202 and end point D204.
An edge D210a-c has an associated cost D214a-b. The cost D214a-b is a value that represents the resources that will be expended if the AV A100 chooses that edge. A typical resource is time. For example, if one edge D210a represents a physical distance that is twice that as another edge D210b, then the associated cost D214a of the first edge D210a may be twice the associated cost D214b of the second edge D210b. Other factors that affect time include expected traffic, number of intersections, speed limit, etc. Another typical resource is fuel economy. Two edges D210a-b may represent the same physical distance, but one edge D210a may require more fuel than another edge D210b, e.g., because of road conditions, expected weather, etc.
When the planning module B104 identifies a path D212 between the start point D202 and end point D204, the planning module B104 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 E102 receives data representing a desired output E104. The desired output E104 typically includes a velocity, e.g., a speed and a heading. The desired output E104 can be based on, for example, data received from a planning module B104 (e.g., as shown in
In an embodiment, the controller E102 receives feedback that is used in adjusting the inputs provided to the throttle and steering. For example, if the AV A100 encounters a disturbance E110, such as a hill, the measured speed E112 of the AV A100 is lowered below the desired output speed. In an embodiment, any measured output E114 is provided to the controller E102 so that the necessary adjustments are performed, e.g., based on the differential E113 between the measured speed and desired output. The measured output E114 includes measured position E116, measured velocity E118, (including speed and heading), measured acceleration E120, and other outputs measurable by sensors of the AV A100.
In an embodiment, information about the disturbance E110 is detected in advance, e.g., by a sensor such as a camera or LiDAR sensor, and provided to a predictive feedback module E122. The predictive feedback module E122 then provides information to the controller E102 that the controller E102 can use to adjust accordingly. For example, if the sensors of the AV A100 detect (“see”) a hill, this information can be used by the controller E102 to prepare to engage the throttle at the appropriate time to avoid significant deceleration.
The controller E102 also has a lateral tracking controller E208 which affects the operation of a steering controller E210. For example, the lateral tracking controller E208 instructs the steering controller E210 to adjust the position of the steering angle actuator E212 depending on, e.g., feedback received by the controller E102 and processed by the lateral tracking controller E208.
The controller E102 receives several inputs used to determine how to control the throttle/brake E206 and steering angle actuator E212. A planning module B104 provides information used by the controller E102, for example, to choose a heading when the AV A100 begins operation and to determine which road segment to traverse when the AV A100 reaches an intersection. A localization module B108 provides information to the controller E102 describing the current location of the AV A100, for example, so that the controller E102 can determine if the AV A100 is at a location expected based on the manner in which the throttle/brake E206 and steering angle actuator E212 are being controlled. In an embodiment, the controller E102 receives information from other inputs E214, e.g., information received from databases, computer networks, etc.
One aspect of controlling the AV A100 includes automatically determining the location of the AV A100. Different driving scenarios may limit the availability of localization reference information, such as global navigation satellite system (GNSS) satellite signals or lane markings on a roadway. Controlling the AV A100 includes modifying the vehicle route by maneuvering the AV A100 in a way that increases the availability or detectability of such localization reference information, which increases the precision of the location determination.
A processor, such as the processor A146 or A304 mentioned above, uses at least one indication from at least one sensor, such as the sensor A121 mentioned above, to make the location determination and to maneuver the AV A100 if necessary or desired. The processor A146 will be included for discussion purposes but the processor A304 or a combination of such processors may be used in some embodiments. A single sensor A121 will be used for discussion purposes but more than one such sensor may be used in some embodiments. The sensor A121 is configured to detect the type of localization reference information used in a given scenario.
At F106, the processor A146 maneuvers the AV A100 to a second lane of the roadway based on the characteristic of the first precision being below the threshold. Maneuvering the AV A100 into the second lane allows the processor A146 to determine the location of the vehicle with a second precision while the AV A100 is traveling in the second lane. The second precision is above the threshold because of increased or improved availability of localization reference information while the vehicle is in the second lane compared to that which was available while the vehicle travels in the first lane.
The processor A146 obtains information regarding at least one localization reference from at least one of the sensors A121 that is configured to provide such information. For example, when the localization reference comprises lane markings sufficient to demarcate a lane on a roadway, the sensor A121 comprises a LIDAR sensor or vision system that is capable of detecting lane markings. In embodiments where the localization reference comprises a GNSS satellite signal, the sensor A121 is configured to detect such signals and the sensor A121, the processor A146, or both are configured to determine the location of the AV A100 based on such signals.
The processor A146 utilizes information regarding the lane markings to control the vehicle steering and speed, for example, to stay centered in the lane while traveling along a route to a destination. When traveling along the segment F118, the processor A146 will not have sufficient lane marking information from the sensor A121 to make an accurate or precise location determination. In other words, when relying upon lane marking indications from the sensor A121 while traveling along the segment F118 in the first lane F112, the determined location of the AV A100 will have a first precision that is below a threshold corresponding to a desired level of precision. In this example, the characteristic of the precision and the threshold correspond to whether lane markings are present to sufficiently demarcate the lane. Since the lane markings F114 are not present along the segment F118, the precision of a location determination while the vehicle A100 is traveling in the lane F112 based on lane markings as the localization reference will not satisfy the first threshold. Under that circumstance, the processor A146 maneuvers the AV A100 into a second, different lane of the roadway F110.
As shown in
The sensor A121 has a field of vision schematically shown at F140. The sensor A121 provides an indication to the processor A146 regarding the presence of lane markings within the field of vision F140. The processor A146 determines that there are insufficient lane markings along the segment F138 to determine the vehicle location along that segment with a desired level of precision. In some instances, the processor A146 makes such a determination based upon at least one location determination regarding the AV A100 on the segment F138.
The processor A146 maneuvers the AV A100 into a second, different lane of the roadway F130 to achieve a location determination having a second precision that is better compared to the precision available in the lane F132. In the scenario shown in
The roadway F150 includes lane markings F154 and F156 that demarcate the sides or edges of a lane F158. The lane markings F154 and F156 are not present underneath the structure F152. Another lane F160 is demarcated by lane markings F164 and F154. At least the lane marking F164 is available underneath the structure F152. Similarly, a lane marking F166 is available along the entire illustrated portion of the roadway F150 along one side of a lane F168. Under such a scenario, the processor A146 determines that there is more lane marking information available in the lanes F160 and F168 compared to the lane F158. Therefore, the processor A146 maneuvers the AV A100 as shown at A100′ into the lane F168 for purposes of traveling beneath the structure F152. Even though lane markings are not available on both sides of the lane F168, there is at least one lane marking available, which provides improved precision over that which would be available while traveling in the lane F158.
In the scenarios shown in
For example, as the AV A100 travels along the roadway, the sensor A121 provides the processor A146 an indication regarding the presence or absence of lane markings within the field of view 140 on an ongoing basis. The processor A146 dynamically responds to the sensor indication and maneuvers the AV A100 into different lanes as may be useful under the particular circumstances.
Predetermined map information, which includes locations where particular segments of lanes do not have sufficient lane markings, may be available from a variety of sources. The processor A146 in some embodiments has access to such information either stored in memory on the AV A100 or through a subscription, for example, to an external database or service that is accessible using wireless communication techniques. The processor A146 in such embodiments essentially keeps track of the map information regarding the vicinity of the AV A100 location and uses that to determine where a lane change will provide more or better lane marking information.
The processor A146 is configured in some embodiments to store information regarding lane markings during at least one trip along a roadway including locations where the sensor A121 indicates that lane markings are insufficient to demarcate a lane, for example. The processor A146 in some embodiments also stores information regarding locations where a lane change resulted in increasing the precision of a location determination. During a subsequent trip along the same roadway, the processor A146 uses the previously stored data and current vehicle location information to maneuver the AV A100 among lanes on the roadway to avoid traveling along a segment of a lane where the lane markings are unavailable or insufficient for adequately demarcating the lane.
In some situations, the first lane of the roadway that does not have lane markings sufficient to demarcate that lane along a segment of a route that the AV A100 is following is a preferred lane for overall route planning purposes. The processor A146 is configured to determine when to maneuver the AV A100 back into the first lane from the second lane once sufficient lane markings are available to demarcate the first lane. This occurs, for example, after travelling past a segment of the roadway on which the first lane does not have adequate lane markings. The processor A146 determines if lane markings are detectable by the sensor A121 in a nearby segment of the first lane while the AV A100 is traveling in the second lane. The processor A146 maneuvers the AV A100 from the second lane into the first lane when such lane markings are available in the nearby segment of the first lane.
The processor A146 may determine when lane markings are detectable by the sensor A121 in the first lane based on indications from the sensor A121 regarding the ability of the sensor A121 to currently detect such lane markings. In some situations, the processor A146 uses predetermined map information or information stored from a previous trip along that roadway to make such a determination. The processor A146 is therefore capable of causing the AV A100 to follow a preplanned, preferred route and maintain a desired level of precision for location determinations along that route by maneuvering the AV A100 into different lanes as may be needed along the preplanned route.
The processor A146, in some embodiments, utilizes GNSS satellite information as the localization reference information. When the localization reference comprises GNSS satellite signals, the characteristic of interest of the precision with which a location determination is made or can be made corresponds to a number of GNSS satellite signals available or detectable by one or more sensors A121 onboard the AV A100. The threshold corresponds to, for example, a desired minimum number of satellite signals simultaneously detectable by the sensor A121. The processor A146 maneuvers the AV A100 into a lane along a current segment of a roadway to increase the availability of satellite signals for purposes of making location determinations when the currently detected number of satellite signals is below the threshold.
As schematically shown in
As schematically shown in
The processor A146 selects the lane to maneuver the AV A100 into by determining a position or location of an obstruction and selecting a lane further from that obstruction to reduce the likelihood that the obstruction will interfere with satellite signal reception by the sensor A121. In some embodiments, the processor A146 makes this determination based on information from the sensor A121. In such embodiments, the sensor A121 is capable of detecting the position of an obstruction relative to the AV A100. For example, the sensor A121 may include RADAR, LiDAR, or ultrasound sensing technologies for detecting an obstruction in or nearby the roadway. The processor A146 determines a position of the obstruction relative to the vehicle and selects a lane based on that determined position. The processor A146 selects a second lane to increase a distance between the AV A100 and the obstruction. By doing so, the processor A146 increases a number of GNSS satellites detectable by the sensor A121 from a first number while the AV A100 is traveling in the first lane to a second, larger number while the AV A100 is traveling in the second lane.
Other types of data that may be used by the processor A146 for identifying or locating obstructions and selecting a second lane includes data stored by the processor A146 during previous trips along a roadway and predetermined map data that provides indications of locations of obstructions relative to one or more lanes of a roadway.
In some embodiments, the processor A146 selects a second lane for increasing the number of satellite signals available to the sensor A121 based upon ephemeris data regarding positions of GNSS satellites. Such data is available and may be provided to the processor A146 through a subscription service, for example.
For example, the processor A146 may determine that the AV A100 is situated between the vehicles F214 and F216 based upon information from the sensor A121 that detects the presence of such vehicles along the roadway F212. At the same time, the processor A146 recognizes that the number of satellite signals available to or detectable by the sensor A121 is below a desired number. The processor A146 alters a speed of the AV A100 to change the position of the AV A100 relative to or one or both of the obstructing vehicles F214 and F216 until those vehicles no longer prevent the sensor A121 from detecting GNSS satellite signals.
When the AV A100 is in either of the positions shown in
The processor A146 is configured in this example embodiment to determine when the obstructing vehicles F214 and F216 are on opposite sides of the AV A100 and that maneuvering the AV A100 into an adjacent lane on the roadway F212 is not possible. Under those circumstances, the processor A146 does not attempt a maneuver as described previously but, instead, accelerates or decelerates the AV A100 while staying in the lane F210. This is another example way in which the processor A146 controls movement of the AV A100 to ensure that adequate localization reference information is available for making a location determination at a desired precision level.
The techniques and system features used in the example scenarios discussed above are combined in some embodiments, such as determining the vehicle location based on a combination of GNSS satellite and lane marking information. The disclosed features and techniques may be combined in various ways to realize a variety of embodiments. [000140] Although GNSS satellite signal and lane markings are used as localization references in the example embodiments described above, those embodiments and others are not necessarily limited to such information. For example, other embodiments include other types of localization references, such as localization objects or buildings that have sufficiently detectable features to allow using an iterative closest point algorithm to determine position information.
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 applicant 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.
