Designing vehicle platooning implementation for single-vehicle autonomous driving systems

Abstract

Disclosed are methods, devices, and systems for implementing platooning between two autonomous vehicles. According to one embodiment, at a second ADV, a vehicle state and a first perception result of a first ADV are received via a vehicle-to-vehicle (V2V) link. The first perception result includes one or more obstacles perceived by the first ADV. The second ADV performs a perception process to perceive a driving environment associated with the second ADV, and the second ADV generates a second perception result. The first perception result performed by the first ADV and the second perception result performed by the second ADV are merged to generate a third perception result. A trajectory is planned based on the vehicle state of the first ADV and the third perception result, so that the second ADV follows the first ADV in a platoon manner.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to operating autonomous vehicles. More specifically, embodiments of the present disclosure relate to implementing platooning at autonomous vehicles designed for single-vehicle operation. Background Art

[0002] A vehicle operating in an automated driving mode (e.g., without human control) can relieve occupants, particularly the driver, from some driving-related responsibilities. When operating in an automated driving mode, the vehicle can navigate to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers.

[0003] Grouping vehicles into platoons, or lines, also known as platooning, is one way to increase road capacity. Platooning uses electronic and possibly mechanical connections to reduce the distance between vehicles. This capability would allow many vehicles to accelerate or brake at the same time. Such systems could also allow for closer headway between vehicles by eliminating the reaction distance required for human response.

[0004] The control systems of autonomous vehicles designed for single-vehicle operation need to be modified to enable these autonomous vehicles to operate in platoon mode. Summary of the invention

[0005] In one aspect of the present disclosure, there is provided a computer-implemented method for operating an autonomous vehicle (ADV) to follow another ADV in a platoon, the method comprising:

[0006] At a second ADV, receiving a vehicle state and a first perception result from a first ADV via a vehicle-to-vehicle (V2V) link, the first perception result including one or more obstacles perceived by the first ADV;

[0007] executing a perception process at the second ADV to perceive a driving environment associated with the second ADV, generating a second perception result;

[0008] combining the first perception result and the second perception result performed by the second ADV to generate a third perception result; and

[0009] A second trajectory is planned based on the vehicle state of the first ADV and the third perception result, so that the second ADV follows the first ADV in a platoon manner.

[0010] In another aspect of the present disclosure, a non-transitory machine-readable medium storing instructions is provided, wherein the instructions, when executed by a processor, cause the processor to perform operations, the operations comprising:

[0011] At a second ADV, receiving a vehicle state and a first perception result from a first ADV via a vehicle-to-vehicle (V2V) link, the first perception result including one or more obstacles perceived by the first ADV;

[0012] executing a perception process at the second ADV to perceive a driving environment associated with the second ADV, generating a second perception result;

[0013] combining the first perception result and the second perception result performed by the second ADV to generate a third perception result; and

[0014] A second trajectory is planned based on the vehicle state of the first ADV and the third perception result, so that the second ADV follows the first ADV in a platoon manner.

[0015] In another aspect of the present disclosure, there is provided a data processing system, comprising:

[0016] Processor; and

[0017] a memory coupled to the processor to store instructions that, when executed by the processor, cause the processor to perform operations comprising:

[0018] At a second ADV, receiving a vehicle state and a first perception result from a first ADV via a vehicle-to-vehicle (V2V) link, the first perception result including one or more obstacles perceived by the first ADV;

[0019] executing a perception process at the second ADV to perceive a driving environment associated with the second ADV, generating a second perception result;

[0020] combining the first perception result and the second perception result performed by the second ADV to generate a third perception result; and

[0021] A second trajectory is planned based on the vehicle state of the first ADV and the third perception result, so that the second ADV follows the first ADV in a platoon manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Embodiments of the present disclosure are illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate similar elements.

[0023] Figure 1 is a block diagram illustrating a networked system according to one embodiment.

[0024] Figure 2 is a block diagram illustrating an example of an autonomous driving vehicle according to one embodiment.

[0025] FIG. 3A to FIG. 3B is a block diagram illustrating an example of a perception and planning system for use with an autonomous vehicle, according to one embodiment.

[0026] Figure 4 is a block diagram illustrating an exemplary environment in which embodiments of the present disclosure may be implemented.

[0027] Figure 5 is a block diagram illustrating various modules used with embodiments of the present disclosure.

[0028] Figure 6 is a flow chart illustrating an example of a process for queue processing according to one embodiment.

[0029] Figure 7 is a flow chart illustrating an example method for implementing platooning between two autonomous vehicles according to one embodiment. DETAILED DESCRIPTION

[0030] Various embodiments and aspects of the present disclosure will be described with reference to the details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative of the present disclosure and should not be construed as limiting the present disclosure. Many specific details are described to provide a comprehensive understanding of the various embodiments of the present disclosure. However, in some cases, well-known or conventional details are not described to provide a concise discussion of the embodiments of the present disclosure.

[0031] References in this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present disclosure. The appearances of the phrase "in one embodiment" in various places in this specification are not necessarily all referring to the same embodiment.

[0032] Embodiments of the present disclosure relate to methods, devices, and systems for implementing platooning between two autonomous vehicles. According to one embodiment, at a second ADV, a vehicle state and a first perception result of a first ADV are received via a vehicle-to-vehicle (V2V) link. The first perception result includes one or more obstacles perceived by the first ADV. The second ADV performs a perception process to perceive a driving environment associated with the second ADV, and the second ADV generates a second perception result. The first perception result performed by the first ADV and the second perception result performed by the second ADV are merged to generate a third perception result. A trajectory is planned based on the vehicle state of the first ADV and the third perception result, so that the second ADV follows the first ADV in a platoon manner.

[0033] According to an embodiment, a path planning result performed by the first ADV is also received from the first ADV via the V2V link. The path planning result includes a trajectory planned by the first ADV. The movement of the first ADV is predicted based on the path planning result of the first ADV. A predicted trajectory of the first ADV is generated based on the prediction. The predicted trajectory represents the trajectory of the first ADV predicted by the second ADV. The first perception result represents the front angle perception perceived from the front view of the first ADV.

[0034] In one embodiment, when merging the first perception result and the second perception result, the received vehicle state is used to replace the perception data of the first ADV in the second perception result of the second ADV. The minimum distance buffer is reduced to a predetermined queue distance between the first ADV and the second ADV. The vehicle state of the first ADV includes the position of the first ADV in the global coordinate system, the speed of the first ADV and / or the forward direction of the first ADV. The minimum distance buffer between the second ADV and the obstacle remains relatively unchanged.

[0035] In one embodiment, if there is a third ADV configured to follow the second ADV, the vehicle status, perception results and / or planning information of the second ADV can be transmitted from the second ADV to the third ADV via the V2V link to enable the third ADV to plan and follow the second ADV in a platoon manner.

[0036] Figure 1 is a block diagram showing a network configuration of an autonomous driving vehicle according to one embodiment of the present disclosure. Figure 1 , network configuration 100 includes an autonomous vehicle 101 that can be communicatively connected to one or more servers 103 to 104 via a network 102. Although one autonomous vehicle is shown, multiple autonomous vehicles can be connected to each other and / or to servers 103 to 104 via network 102. Network 102 can be any type of network, for example, a wired or wireless local area network (LAN), a wide area network (WAN) such as the Internet, a cellular network, a satellite network, or a combination thereof. Servers 103 to 104 can be any type of server or server cluster, such as a web or cloud server, an application server, a back-end server, or a combination thereof. Servers 103 to 104 can be data analysis servers, content servers, traffic information servers, maps and points of interest (MPOI) servers, or location servers, etc.

[0037] An autonomous vehicle refers to a vehicle that is configurable to be in an autonomous mode in which the vehicle navigates through an environment with little or no input from a driver. Such an autonomous vehicle may include a sensor system having one or more sensors configured to detect information related to the environment in which the vehicle is operating. The vehicle and its associated controller use the detected information to navigate through the environment. The autonomous vehicle 101 may operate in a manual mode, in a fully autonomous mode, or in a partially autonomous mode.

[0038] In one embodiment, the autonomous vehicle 101 includes, but is not limited to, a perception and planning system 110, a vehicle control system 111, a wireless communication system 112, a user interface system 113, and a sensor system 115. The autonomous vehicle 101 may also include certain common components included in an ordinary vehicle, such as: an engine, wheels, a steering wheel, a transmission, etc., which may be controlled by the vehicle control system 111 and / or the perception and planning system 110 using a variety of communication signals and / or commands, such as acceleration signals or commands, deceleration signals or commands, steering signals or commands, braking signals or commands, etc.

[0039] Components 110 to 115 may be communicatively coupled to each other via an interconnect, a bus, a network, or a combination thereof. For example, components 110 to 115 may be communicatively coupled to each other via a controller area network (CAN) bus. The CAN bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host. It is a message-based protocol originally designed for multiplexed electrical wiring within an automobile, but is also used in many other environments.

[0040] Reference now Figure 2In one embodiment, the sensor system 115 includes, but is not limited to, one or more cameras 211, a global positioning system (GPS) unit 212, an inertial measurement unit (IMU) 213, a radar unit 214, and a light detection and ranging (LIDAR) unit 215. The GPS unit 212 may include a transceiver operable to provide information about the location of the autonomous vehicle. The IMU unit 213 may sense the position and orientation changes of the autonomous vehicle based on inertial acceleration. The radar unit 214 may represent a system that uses radio signals to sense objects within the local environment of the autonomous vehicle. In some embodiments, in addition to sensing objects, the radar unit 214 may additionally sense the speed and / or heading of the object. The LIDAR unit 215 may use lasers to sense objects in the environment of the autonomous vehicle. The LIDAR unit 215 may also include, among other system components, one or more laser sources, a laser scanner, and one or more detectors. The camera 211 may include one or more devices for capturing images of the environment surrounding the autonomous vehicle. The camera 211 may be a still camera and / or a video camera. The camera may be mechanically movable, for example, by mounting the camera on a rotating and / or tilting platform.

[0041] The sensor system 115 may also include other sensors, such as: a sonar sensor, an infrared sensor, a steering sensor, a throttle sensor, a brake sensor, and an audio sensor (e.g., a microphone). The audio sensor may be configured to collect sound from the environment around the autonomous vehicle. The steering sensor may be configured to sense the steering angle of the steering wheel, the wheels of the vehicle, or a combination thereof. The throttle sensor and the brake sensor sense the throttle position and the brake position of the vehicle, respectively. In some cases, the throttle sensor and the brake sensor may be integrated into an integrated throttle / brake sensor.

[0042] In one embodiment, the vehicle control system 111 includes, but is not limited to, a steering unit 201, a throttle unit 202 (also referred to as an acceleration unit), and a brake unit 203. The steering unit 201 is used to adjust the direction or forward direction of the vehicle. The throttle unit 202 is used to control the speed of the motor or engine, which in turn controls the speed and acceleration of the vehicle. The brake unit 203 decelerates the vehicle by providing friction to decelerate the wheels or tires of the vehicle. It should be noted that, as Figure 2 The components shown may be implemented in hardware, software or a combination thereof.

[0043] Return to reference Figure 1, the wireless communication system 112 allows communication between the autonomous driving vehicle 101 and external systems such as devices, sensors, other vehicles, etc. For example, the wireless communication system 112 can communicate directly with one or more devices wirelessly, or communicate wirelessly via a communication network, such as communicating with servers 103 to 104 through the network 102. The wireless communication system 112 can use any cellular communication network or wireless local area network (WLAN), for example, using WiFi, to communicate with another component or system. The wireless communication system 112 can communicate directly with a device (e.g., a passenger's mobile device, a display device, a speaker in the vehicle 101) using, for example, an infrared link, Bluetooth, etc. The user interface system 113 can be part of a peripheral device implemented in the vehicle 101, including, for example, a keyboard, a touch screen display device, a microphone, and a speaker.

[0044] Some or all of the functions of the autonomous vehicle 101 may be controlled or managed by the perception and planning system 110, particularly when operating in an autonomous mode. The perception and planning system 110 includes the necessary hardware (e.g., processor, memory, storage) and software (e.g., operating system, planning and routing sequences) to receive information from the sensor system 115, the control system 111, the wireless communication system 112, and / or the user interface system 113, process the received information, plan a route or path from a starting point to a destination point, and then drive the vehicle 101 based on the planning and control information. Alternatively, the perception and planning system 110 may be integrated with the vehicle control system 111.

[0045] For example, a user as a passenger may specify a starting location and a destination for a trip, for example, via a user interface. The perception and planning system 110 obtains trip-related data. For example, the perception and planning system 110 may obtain location and route information from an MPOI server, which may be part of the servers 103 to 104. The location server provides location services, and the MPOI server provides map services and POIs for certain locations. Alternatively, such location and MPOI information may be locally cached in a permanent storage device of the perception and planning system 110.

[0046] As the autonomous vehicle 101 moves along the route, the perception and planning system 110 may also obtain real-time traffic information from a traffic information system or server (TIS). It should be noted that the servers 103 to 104 may be operated by a third-party entity. Alternatively, the functionality of the servers 103 to 104 may be integrated with the perception and planning system 110. Based on the real-time traffic information, MPOI information, and location information, as well as real-time local environmental data (e.g., obstacles, objects, nearby vehicles) detected or sensed by the sensor system 115, the perception and planning system 110 may plan an optimal route and drive the vehicle 101 according to the planned route, for example, via the control system 111, to safely and efficiently reach a designated destination.

[0047] The server 103 may be a data analysis system, thereby performing data analysis services for various clients. In one embodiment, the data analysis system 103 includes a data collector 121 and a machine learning engine 122. The data collector 121 collects driving statistics 123 from various vehicles (autonomous driving vehicles or conventional vehicles driven by human drivers). The driving statistics 123 include information indicating the driving instructions issued (e.g., throttle, brake, steering instructions) and the responses of the vehicle (e.g., speed, acceleration, deceleration, direction) captured by the vehicle's sensors at different points in time. The driving statistics 123 may also include information describing the driving environment at different points in time, such as routes (including starting and destination locations), MPOI, road conditions, weather conditions, etc.

[0048] Based on driving statistics 123, the machine learning engine 122 generates or trains a set of rules, algorithms, and / or predictive models 124 for various purposes. In one embodiment, the algorithms 124 may include methods that enable the autonomous vehicles 101 to operate in a platoon mode.

[0049] The algorithm 124 may then be uploaded to the ADV for use in real time during autonomous driving.

[0050] Figure 3A and Figure 3B is a block diagram illustrating an example of a perception and planning system for use with an autonomous vehicle according to one embodiment. System 300 may be implemented as Figure 1 A portion of an autonomous vehicle 101 includes, but is not limited to, a perception and planning system 110, a control system 111, and a sensor system 115. FIG. 3A to FIG. 3B The perception and planning system 110 includes but is not limited to a positioning module 301 , a perception module 302 , a prediction module 303 , a decision module 304 , a planning module 305 , a control module 306 , a route queue module 307 and a queue module 308 .

[0051] Some or all of the modules 301 to 308 may be implemented in software, hardware, or a combination thereof. For example, these modules may be installed in permanent storage 352, loaded into memory 351, and executed by one or more processors (not shown). It should be noted that some or all of these modules may be communicatively coupled to Figure 2 Some or all modules of the vehicle control system 111 of the embodiment of the present invention may be integrated with them. Some of the modules 301 to 308 may be integrated together into an integrated module.

[0052] The positioning module 301 determines the current location of the autonomous vehicle 300 (e.g., using the GPS unit 212) and manages any data related to the user's itinerary or route. The positioning module 301 (also referred to as the map and route module) manages any data related to the user's itinerary or route. The user can log in, for example, via a user interface and specify the starting location and destination of the itinerary. The positioning module 301 communicates with other components of the autonomous vehicle 300, such as the map and route information 311, to obtain itinerary related data. For example, the positioning module 301 can obtain location and route information from a location server and a map and POI (MPOI) server. The location server provides location services, and the MPOI server provides map services and POIs at certain locations, which can be cached as part of the map and route information 311. When the autonomous vehicle 300 moves along the route, the positioning module 301 can also obtain real-time traffic information from a traffic information system or server.

[0053] Based on the sensor data provided by the sensor system 115 and the positioning information obtained by the positioning module 301, the perception module 302 determines the perception of the surrounding environment. The perception information may represent what an average driver would perceive around the vehicle that the driver is driving. The perception may include, for example, lane configuration in the form of objects, traffic light signals, the relative position of another vehicle, pedestrians, buildings, crosswalks or other traffic-related signs (e.g., stop signs, yield signs), etc. The lane configuration includes information describing one or more lanes, such as, for example, the shape of the lane (e.g., straight or curved), the width of the lane, the number of lanes in the road, one-way or two-way lanes, merging or splitting lanes, exit lanes, etc.

[0054] The perception module 302 may include a computer vision system or the functionality of a computer vision system to process and analyze images captured by one or more cameras to identify objects and / or features in the autonomous vehicle environment. The objects may include traffic signals, road boundaries, other vehicles, pedestrians, and / or obstacles, etc. The computer vision system may use object recognition algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system may map the environment, track objects, and estimate the speed of objects, etc. The perception module 302 may also detect objects based on other sensor data provided by other sensors such as radar and / or LIDAR.

[0055] For each object, the prediction module 303 predicts how the object will behave in this situation. The prediction is performed based on the perception data, which perceives the driving environment at a point in time considering a set of map / route information 311 and traffic rules 312. For example, if the object is a vehicle in the opposite direction and the current driving environment includes an intersection, the prediction module 303 will predict whether the vehicle is likely to move straight ahead or turn. If the perception data indicates that there is no traffic light at the intersection, the prediction module 303 may predict that the vehicle may need to come to a complete stop before entering the intersection. If the perception data indicates that the vehicle is currently in a left-turn only lane or a right-turn only lane, the prediction module 303 may predict that the vehicle will be more likely to turn left or right, respectively.

[0056] For each object, the decision module 304 makes a decision about how to handle the object. For example, for a particular object (e.g., another vehicle in a crossing path) and metadata describing the object (e.g., speed, direction, turn angle), the decision module 304 decides how to encounter the object (e.g., overtake, yield, stop, pass). The decision module 304 may make such decisions based on a rule set such as traffic rules or driving rules 312, which may be stored in the persistent storage device 352.

[0057] The route arrangement module 307 is configured to provide one or more routes or paths from the starting point to the destination point. For a given trip from the starting position to the destination position, such as a given trip received from a user, the route arrangement module 307 obtains the route and map information 311 and determines all possible routes or paths from the starting position to the destination position. The route arrangement module 307 can generate a reference line in the form of a topographic map, which determines each route from the starting position to the destination position. The reference line refers to an ideal route or path that is not interfered by any other factors such as other vehicles, obstacles or traffic conditions. That is, if there are no other vehicles, pedestrians or obstacles on the road, the ADV should follow the reference line accurately or closely. Then, the topographic map is provided to the decision module 304 and / or the planning module 305. The decision module 304 and / or the planning module 305 check all possible routes to select and change one of the best routes based on other data provided by other modules, wherein other data such as traffic conditions from the positioning module 301, the driving environment perceived by the perception module 302, and the traffic conditions predicted by the prediction module 303. Depending on the particular driving environment at a point in time, the actual path or route used to control the ADV may be close to or different from the reference line provided by the route alignment module 307 .

[0058] Based on the decision for each of the perceived objects, the planning module 305 plans a path or route and driving parameters (e.g., distance, speed, and / or turning angle) for the autonomous vehicle using the reference lines provided by the route arrangement module 307 as a basis. In other words, for a given object, the decision module 304 decides what to do with the object, and the planning module 305 determines how to do it. For example, for a given object, the decision module 304 may decide to overtake the object, and the planning module 305 may determine whether to overtake on the left or right side of the object. Planning and control data are generated by the planning module 305 and include information describing how the vehicle 300 will move in the next movement cycle (e.g., the next route / path segment). For example, the planning and control data may indicate that the vehicle 300 moves at a speed of 30 miles per hour (mph) for 10 meters and then changes to the right lane at a speed of 25 mph.

[0059] Based on the planning and control data, the control module 306 controls and drives the autonomous vehicle according to the route or path defined by the planning and control data by sending appropriate commands or signals to the vehicle control system 111. The planning and control data includes sufficient information to drive the vehicle from a first point on the route or path to a second point using appropriate vehicle settings or driving parameters (e.g., throttle, brake, steering commands) at different points in time along the route or path.

[0060] In one embodiment, the planning phase is performed in multiple planning cycles (also referred to as driving cycles), for example, in each cycle with a time interval of 100 milliseconds (ms). For each of the planning cycles or driving cycles, one or more control commands will be issued based on the planning and control data. That is, for every 100ms, the planning module 305 plans the next route segment or path segment, for example, including the target position and the time required for the ADV to reach the target position. Alternatively, the planning module 305 can also specify a specific speed, direction and / or steering angle, etc. In one embodiment, the planning module 305 plans the route segment or path segment for the next predetermined period of time (such as 5 seconds). For each planning cycle, the planning module 305 plans the target position for the current cycle (e.g., the next 5 seconds) based on the target position planned in the previous cycle. The control module 306 then generates one or more control commands (e.g., throttle, brake, steering control commands) based on the planning and control data of the current cycle.

[0061] It should be noted that the decision module 304 and the planning module 305 can be integrated into an integrated module. The decision module 304 / planning module 305 may include a navigation system or the functionality of a navigation system to determine a driving path for an autonomous vehicle. For example, the navigation system may determine a series of speeds and directions of travel for influencing the autonomous vehicle to move along a path that substantially avoids perceived obstacles while allowing the autonomous vehicle to move along a roadway-based path to a final destination. The destination may be set based on user input via the user interface system 113. The navigation system may dynamically update the driving path while the autonomous vehicle is running. The navigation system may combine data from a GPS system and one or more maps to determine a driving path for the autonomous vehicle.

[0062] According to one embodiment, the queue module 308 is configured to perform queue processing to allow the ADV to follow another vehicle in a queue. The queue module 308 can be implemented as part of the perception module 302. Alternatively, the queue module 308 can be a separate module that is communicatively connected to the perception module 302, the prediction module 303, and the planning module 305. The queue module 308 can receive and utilize at least a portion of the perception, prediction, and planning information from the leading vehicle, combined with its own perception, prediction, and planning information, to plan the ADV 101 to follow the leading vehicle in a queue.

[0063] refer to Figure 4, a block diagram illustrating an exemplary environment 400 in which embodiments of the present disclosure may be implemented is shown. A number of autonomous vehicles are traveling in a platoon on a highway 430. In the direction of travel, a first vehicle 410 is a leading vehicle and a second vehicle 420 is a following vehicle directly behind the first vehicle. The platoon may include one or more additional following vehicles behind the second vehicle 420. The first vehicle 410 may transmit the following to the second vehicle 420 via a vehicle-to-vehicle data link: (1) the current vehicle state of the first vehicle 410, which may include one or more of the following: position, speed, heading, etc. in a global coordinate system, or a combination thereof; 2) front angle perception results (i.e., perception of objects in front of the first vehicle 410; and 3) autonomous driving path planning results of the vehicle 410.

[0064] Providing the second vehicle 420 with the front corner perception results of the first vehicle 410 will improve the perception of the second vehicle 420 because this effectively allows the second vehicle 420 to "see through" the first vehicle 410. In one embodiment, the first vehicle 410 may not send the raw point cloud or image input associated with the front corner perception to reduce the communication message size. Instead, the vehicle 410 only sends the results of the perception process, which includes at least information describing one or more obstacles detected by the perception module of the vehicle 410. For example, the perception results may include the position (x, y), speed, and heading of each obstacle detected. For static obstacles, the speed and heading may be zero, which can be used as an indication of a static object. The vehicle 410 may include autonomous driving functionality as described above, and may include information such as Figure 3A-3B At least some of the autonomous driving modules shown.

[0065] Reference Figure 5 , a block diagram 500 showing various modules used with embodiments of the present disclosure is shown. A vehicle state 501 of the first vehicle, a front corner perception result 502 of the first vehicle, and a path planning result 503 of the first vehicle are received from the first vehicle at the second vehicle. The vehicle state 501 of the first vehicle and the front corner perception result 502 of the first vehicle are forwarded to the perception module 302 of the second vehicle. In this example, the queue module 308 is implemented as part of the perception module 302. There, the front corner perception result 502 of the first vehicle is merged with the perception result of the second vehicle, and the received vehicle state 501 of the first vehicle is replaced with the perception data associated with the first vehicle in the list of recognized objects in the perception result of the second vehicle.

[0066] The path planning result 503 of the first vehicle is forwarded to the prediction module 303 of the second vehicle, wherein the trajectory of the first vehicle is predicted based at least in part on the path planning result 503 of the first vehicle. It should be understood that when predicting the trajectory of the first vehicle, each trajectory point received from the first vehicle needs to be matched with its timestamp. The perception result, the predicted trajectory of the first vehicle, and the reduced minimum distance buffer 504 between the second vehicle and the first vehicle are forwarded to the planning module 305 of the second vehicle. Thereafter, the trajectory of the second vehicle is planned in the planning module 305 based at least in part on the received vehicle state 501 of the first vehicle, the predicted trajectory of the first vehicle, and the reduced minimum distance buffer 504. Then, a control signal for automatically driving the second vehicle is generated in the control module 306 based on the planned trajectory of the second vehicle. Therefore, the first and second vehicles operate in a platoon mode and constitute at least part of the platoon.

[0067] In one embodiment, the received vehicle state 501 of the first vehicle includes one or more of the following: the position of the first vehicle in the global coordinate system, the speed of the first vehicle, the heading of the first vehicle, or any combination thereof. In one embodiment, one or more minimum distance buffers between the second vehicle and one or more identified objects other than the first vehicle remain unchanged on the second vehicle (i.e., a default minimum buffer is used).

[0068] In one embodiment, the platoon includes one or more additional following vehicles behind the second vehicle. A third vehicle in the platoon directly behind the second vehicle receives the vehicle state, front angle perception results, and path planning results of the second vehicle from the second vehicle. The third vehicle plans its trajectory based at least in part on the received vehicle state of the second vehicle, the predicted trajectory of the second vehicle, and the minimum distance buffer between the third vehicle and the second vehicle. Of course, it should be understood that the number of vehicles in the platoon does not limit the present disclosure, and it is within the skill of those skilled in the art that the techniques disclosed herein can be adjusted to scenarios involving different numbers of vehicles in the platoon.

[0069] Figure 6 600 is a flow chart illustrating an example of a queue process according to one embodiment. Process 600 may be performed by processing logic that may include software, hardware, or a combination thereof. Figure 6, in box 601, the processing logic receives a vehicle state and a first perception result from a first ADV via a vehicle-to-vehicle (V2V) link at a second ADV. The first perception result includes one or more obstacles perceived by the first ADV. In box 602, the processing logic performs a perception process at the second ADV to perceive the driving environment associated with the second ADV and generate a second perception result. In box 603, the first perception result performed by the first ADV and the second perception result performed by the second ADV are merged to generate a third perception result. In box 604, a trajectory is planned based on the vehicle state of the first ADV and the third perception result so that the second ADV can follow the first ADV in a platoon manner.

[0070] refer to Figure 7 , a flowchart showing an exemplary method 700 for implementing platooning between two autonomous vehicles is shown. The method can be implemented in software, hardware, or a combination of both. At box 710, at the second vehicle, a vehicle state, a front angle perception result, and a path planning result of the first vehicle are received from the first vehicle. At box 720, at the second vehicle, the front angle perception result of the first vehicle is merged with the perception result of the second vehicle. At box 730, at the second vehicle, the received vehicle state of the first vehicle is replaced with the perception data associated with the first vehicle in the list of recognized objects in the perception result of the second vehicle. At box 740, at the second vehicle, a trajectory of the first vehicle is predicted based at least in part on the received path planning result of the first vehicle. At box 750, at the second vehicle, a minimum distance buffer between the second vehicle and the first vehicle is reduced (from a default value) to a predetermined platoon distance. At box 760, at the second vehicle, a trajectory of the second vehicle is planned based at least in part on the received vehicle state of the first vehicle, the predicted trajectory of the first vehicle, and the reduced minimum distance buffer between the second vehicle and the first vehicle. At block 770 , at the second vehicle, control signals are generated to autonomously drive the second vehicle based on the planned trajectory of the second vehicle.

[0071] Therefore, embodiments of the present disclosure are directed to a method, apparatus, and system that are simple modifications to an autonomous driving system designed for single-vehicle operation and enable the autonomous driving system to operate in a platoon mode. Thus, the benefits associated with a platoon can be obtained.

[0072] It should be noted that some or all of the components shown and described above may be implemented in software, hardware, or a combination thereof. For example, such components may be implemented as software installed and stored in a permanent storage device, which may be loaded into a memory by a processor (not shown) and executed in the memory to implement the processes or operations described throughout the present application. Alternatively, such components may be implemented as executable code programmed or embedded into dedicated hardware (such as an integrated circuit (e.g., an application-specific integrated circuit or ASIC), a digital signal processor (DSP), or a field programmable gate array (FPGA)), which may be accessed via a corresponding driver and / or operating system from an application. In addition, such components may be implemented as specific hardware logic in a processor or processor core as part of an instruction set that may be accessed by a software component through one or more specific instructions.

[0073] Some portions of the foregoing detailed description have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is generally considered herein to be a self-consistent sequence of operations leading to a desired result. These operations are those requiring physical manipulation of physical quantities.

[0074] It should be borne in mind, however, that all of these and similar terms are intended to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless otherwise expressly indicated in the above discussion, it should be understood that throughout the specification, discussions utilizing terms such as those set forth in the appended claims refer to the actions and processes of a computer system or similar electronic computing device that manipulates data represented as physical (electronic) quantities within the computer system's registers and memories and transforms the data into other data similarly represented as physical quantities within the computer system's memories or registers or other such information storage, transmission, or display devices.

[0075] Embodiments of the present disclosure also relate to devices for performing the operations herein. Such a computer program is stored in a non-transitory computer-readable medium. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., computer) readable storage medium (e.g., a read-only memory ("ROM"), a random access memory ("RAM"), a magnetic disk storage medium, an optical storage medium, a flash memory device).

[0076] The processes or methods depicted in the foregoing figures may be performed by processing logic including hardware (e.g., circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer-readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be understood that some of the operations may be performed in a different order. Furthermore, some operations may be performed in parallel rather than sequentially.

[0077] The embodiments of the present disclosure are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages ​​may be used to implement the teachings of the embodiments of the present disclosure as described herein.

[0078] In the above description, embodiments of the present disclosure have been described with reference to specific exemplary embodiments of the present disclosure. It will be apparent that various modifications may be made to the present invention without departing from the broader spirit and scope of the present disclosure as set forth in the appended claims. Therefore, the present description and the accompanying drawings should be understood in an illustrative sense rather than a restrictive sense.

Claims

1. A computer-implemented method for operating an autonomous vehicle to follow another autonomous vehicle in a platoon, the method comprising: At a second autonomous vehicle, receiving a vehicle state and a first perception result from a first autonomous vehicle via a vehicle-to-vehicle link, the first perception result comprising one or more obstacles perceived by the first autonomous vehicle; executing a perception process at the second autonomous driving vehicle to perceive a driving environment associated with the second autonomous driving vehicle, generating a second perception result; Merging a front angle perception result in the first perception result with the second perception result, and replacing the vehicle state of the first autonomous driving vehicle with the perception data associated with the first autonomous driving vehicle perceived in the second perception result; planning a second trajectory based on the vehicle state of the first autonomous driving vehicle and the updated second perception result, so that the second autonomous driving vehicle follows the first autonomous driving vehicle in a platoon; receiving a path planning result from the first autonomous vehicle via the vehicle-to-vehicle link, the path planning result comprising a first trajectory of the autonomous first autonomous vehicle planned by the first autonomous vehicle; predicting the movement of the first autonomous driving vehicle based on the first trajectory provided by the path planning result; and Generate a predicted trajectory based on the predicted movement of the first autonomous vehicle, the predicted trajectory representing the trajectory of the first autonomous vehicle predicted by the second autonomous vehicle, wherein the second trajectory is planned according to the predicted trajectory of the first autonomous vehicle.

2. The method according to claim 1, wherein: The method also includes receiving a path planning result from the first autonomous driving vehicle through the vehicle-to-vehicle link, the path planning result being a plan from a start point to a destination and including a first trajectory planned by the first autonomous driving vehicle for autonomous driving of the first autonomous driving vehicle in a future time period, and Planning a second trajectory based on the vehicle state of the first autonomous driving vehicle and the updated second perception result so that the second autonomous driving vehicle follows the first autonomous driving vehicle in a platoon manner includes: based on the vehicle state of the first autonomous driving vehicle, the updated second perception result and the trajectory of the first autonomous driving vehicle predicted based on the trajectory planning result of the first autonomous driving vehicle, the second autonomous driving vehicle plans the second trajectory for the second autonomous driving vehicle in the future time period, so that the second autonomous driving vehicle follows the first autonomous driving vehicle in a platoon manner during the future time period.

3. The method of claim 1 , further comprising reducing a minimum distance buffer between the second autonomous vehicle and the first autonomous vehicle to a predetermined platoon distance, wherein: The second trajectory is planned according to a predetermined platoon distance between the first autonomous driving vehicle and the second autonomous driving vehicle.

4. The method according to claim 1, wherein: The received vehicle state of the first autonomous driving vehicle includes one or more of the following: the position of the first autonomous driving vehicle in the global coordinate system, the speed of the first autonomous driving vehicle, or the forward direction of the first autonomous driving vehicle.

5. The method of claim 1 further comprising maintaining one or more minimum distance buffers between the second autonomous vehicle and one or more identified obstacles relatively unchanged.

6. The method according to claim 1 also includes transmitting a second vehicle state, the second perception result, and metadata describing the second trajectory of the second autonomous driving vehicle to a third autonomous driving vehicle directly behind the second autonomous driving vehicle, and the third autonomous driving vehicle utilizes the second vehicle state, the second perception result, and the metadata to follow the second autonomous driving vehicle in a platoon manner.

7. A non-transitory machine-readable medium storing instructions, which, when executed by a processor, cause the processor to perform operations comprising: At a second autonomous vehicle, receiving a vehicle state and a first perception result from a first autonomous vehicle via a vehicle-to-vehicle link, the first perception result comprising one or more obstacles perceived by the first autonomous vehicle; executing a perception process at the second autonomous driving vehicle to perceive a driving environment associated with the second autonomous driving vehicle, generating a second perception result; Merging a front angle perception result in the first perception result with the second perception result, and replacing the vehicle state of the first autonomous driving vehicle with the perception data associated with the first autonomous driving vehicle perceived in the second perception result; planning a second trajectory based on the vehicle state of the first autonomous driving vehicle and the updated second perception result, so that the second autonomous driving vehicle follows the first autonomous driving vehicle in a platoon; receiving a path planning result from the first autonomous vehicle via the vehicle-to-vehicle link, the path planning result comprising a first trajectory of the autonomous first autonomous vehicle planned by the first autonomous vehicle; predicting the movement of the first autonomous driving vehicle based on the first trajectory provided by the path planning result; and Generate a predicted trajectory based on the predicted movement of the first autonomous vehicle, the predicted trajectory representing the trajectory of the first autonomous vehicle predicted by the second autonomous vehicle, wherein the second trajectory is planned according to the predicted trajectory of the first autonomous vehicle.

8. The machine-readable medium of claim 7, wherein: The operations also include receiving a path planning result from the first autonomous driving vehicle through the vehicle-to-vehicle link, the path planning result being a plan from a start point to a destination and including a first trajectory planned by the first autonomous driving vehicle for autonomous driving of the first autonomous driving vehicle in a future time period, and Planning a second trajectory based on the vehicle state of the first autonomous driving vehicle and the updated second perception result so that the second autonomous driving vehicle follows the first autonomous driving vehicle in a platoon manner includes: based on the vehicle state of the first autonomous driving vehicle, the updated second perception result and the trajectory of the first autonomous driving vehicle predicted based on the trajectory planning result of the first autonomous driving vehicle, the second autonomous driving vehicle plans the second trajectory for the second autonomous driving vehicle in the future time period, so that the second autonomous driving vehicle follows the first autonomous driving vehicle in a platoon manner during the future time period.

9. The machine-readable medium of claim 7, wherein: The operation also includes reducing a minimum distance buffer between the second autonomous vehicle and the first autonomous vehicle to a predetermined queue distance, wherein the second trajectory is planned according to the predetermined queue distance between the first autonomous vehicle and the second autonomous vehicle.

10. The machine-readable medium of claim 7, wherein: The received vehicle state of the first autonomous driving vehicle includes one or more of the following: the position of the first autonomous driving vehicle in the global coordinate system, the speed of the first autonomous driving vehicle, or the forward direction of the first autonomous driving vehicle.

11. The machine-readable medium of claim 7, wherein: The operations also include maintaining one or more minimum distance buffers between the second automated driving vehicle and one or more identified obstacles relatively unchanged.

12. The machine-readable medium of claim 7, wherein: The operation also includes transmitting a second vehicle state, the second perception result, and metadata describing the second trajectory of the second autonomous vehicle to a third autonomous vehicle directly behind the second autonomous vehicle, and the third autonomous vehicle utilizes the second vehicle state, the second perception result, and the metadata to follow the second autonomous vehicle in a platoon manner.

13. A data processing system comprising: processor; as well as a memory coupled to the processor to store instructions that, when executed by the processor, cause the processor to perform operations comprising: At a second autonomous vehicle, receiving a vehicle state and a first perception result from a first autonomous vehicle via a vehicle-to-vehicle link, the first perception result comprising one or more obstacles perceived by the first autonomous vehicle; executing a perception process at the second autonomous driving vehicle to perceive a driving environment associated with the second autonomous driving vehicle, generating a second perception result; Merging a front angle perception result in the first perception result with the second perception result, and replacing the vehicle state of the first autonomous driving vehicle with the perception data associated with the first autonomous driving vehicle perceived in the second perception result; and planning a second trajectory based on the vehicle state of the first autonomous driving vehicle and the updated second perception result, so that the second autonomous driving vehicle follows the first autonomous driving vehicle in a platoon; receiving a path planning result from the first autonomous vehicle via the vehicle-to-vehicle link, the path planning result comprising a first trajectory of the autonomous first autonomous vehicle planned by the first autonomous vehicle; predicting the movement of the first autonomous driving vehicle based on the first trajectory provided by the path planning result; and Generate a predicted trajectory based on the predicted movement of the first autonomous vehicle, the predicted trajectory representing the trajectory of the first autonomous vehicle predicted by the second autonomous vehicle, wherein the second trajectory is planned according to the predicted trajectory of the first autonomous vehicle.

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