Post-collision damage mitigation braking system

Abstract

A first obstacle is detected that collides with the ADV. A minimum deceleration required for the ADV to avoid colliding with a second obstacle within a predetermined proximity of the moving direction is determined. A braking command is generated based on the minimum deceleration. The braking command is then applied to the ADV so that the ADV avoids colliding with the second obstacle and mitigates the impact of colliding with the first obstacle.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to operating an autonomous vehicle. More specifically, embodiments of the present disclosure relate to post-crash damage reduction for an autonomous vehicle (ADV). 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] Motion planning and control are key operations in autonomous driving. During a collision between an ADV and an obstacle, it is important to reduce damage to the ADV and the obstacle, and to avoid damage to other obstacles. However, currently, ADVs are configured to keep braking during a collision, which may not always be the best solution. For example, if an ADV is hit from behind, the ADV may not be able to absorb the energy from the collision if the ADV simply keeps braking. Summary of the invention

[0004] According to one aspect of the present disclosure, there is provided a computer-implemented method for operating an autonomous vehicle, the method comprising:

[0005] Detecting a first obstacle that collides with the autonomous vehicle;

[0006] determining a minimum deceleration required for the autonomous vehicle to avoid a collision with a second obstacle within a predetermined proximity of the direction of movement;

[0007] generating a braking command based on the minimum deceleration; and

[0008] The braking command is applied to the autonomous driving vehicle so that the autonomous driving vehicle avoids collision with the second obstacle and mitigates impact of collision with the first obstacle.

[0009] According to certain exemplary embodiments of the present application, detecting a first obstacle that collides with the autonomous driving vehicle is based on detecting that an acceleration or deceleration of the autonomous driving vehicle is greater than a predetermined acceleration or deceleration threshold.

[0010] According to some exemplary embodiments of the present application, the method further comprises determining a nearest obstacle within the predetermined proximity of the moving direction, wherein the second obstacle is the nearest obstacle.

[0011] According to certain exemplary embodiments of the present application, the method further includes determining whether a duration for which the autonomous driving vehicle will collide with the second obstacle is greater than a predetermined time threshold.

[0012] According to certain exemplary embodiments of the present application, the minimum deceleration is determined to be zero, and wherein, in response to determining that the duration for which the autonomous driving vehicle will collide with the second obstacle is greater than the predetermined time threshold, the braking command is zero percent of the maximum braking command.

[0013] According to certain exemplary embodiments of the present application, the minimum deceleration is calculated based on a difference between a speed of the autonomous driving vehicle and a speed of the second obstacle and a distance between the autonomous driving vehicle and the second obstacle.

[0014] According to certain exemplary embodiments of the present application, the method further includes selecting the gear position of the autonomous driving vehicle as a “neutral” position.

[0015] According to another aspect of the present disclosure, there is provided a non-transitory machine-readable medium having instructions stored therein, the instructions, when executed by a processor, causing the processor to perform operations, the operations comprising:

[0016] Detecting a first obstacle that collides with the autonomous vehicle;

[0017] determining a minimum deceleration required for the autonomous vehicle to avoid a collision with a second obstacle within a predetermined proximity of the direction of movement;

[0018] generating a braking command based on the minimum deceleration; and

[0019] The braking command is applied to the autonomous driving vehicle so that the autonomous driving vehicle avoids collision with the second obstacle and mitigates impact of collision with the first obstacle.

[0020] According to certain exemplary embodiments of the present application, detecting a first obstacle that collides with the autonomous driving vehicle is based on detecting that an acceleration or deceleration of the autonomous driving vehicle is greater than a predetermined acceleration or deceleration threshold.

[0021] According to some exemplary embodiments of the present application, the operation further includes determining a nearest obstacle within the predetermined proximity of the moving direction, wherein the second obstacle is the nearest obstacle.

[0022] According to certain exemplary embodiments of the present application, the operation also includes determining whether the duration that the autonomous driving vehicle will collide with the second obstacle is greater than a predetermined time threshold.

[0023] According to certain exemplary embodiments of the present application, the minimum deceleration is determined to be zero, and wherein, in response to determining that the duration for which the autonomous driving vehicle will collide with the second obstacle is greater than the predetermined time threshold, the braking command is zero percent of the maximum braking command.

[0024] According to certain exemplary embodiments of the present application, the minimum deceleration is calculated based on a difference between a speed of the autonomous driving vehicle and a speed of the second obstacle and a distance between the autonomous driving vehicle and the second obstacle.

[0025] According to certain exemplary embodiments of the present application, the operation also includes selecting the gear position of the autonomous driving vehicle as a "neutral" position.

[0026] According to another aspect of the present disclosure, there is provided a data processing system, comprising:

[0027] Processor; and

[0028] a memory coupled to the processor to store instructions that, when executed by the processor, cause the processor to perform operations comprising:

[0029] Detecting a first obstacle that collides with the autonomous vehicle;

[0030] determining a minimum deceleration required for the autonomous vehicle to avoid a collision with a second obstacle within a predetermined proximity of the direction of movement;

[0031] generating a braking command based on the minimum deceleration; and

[0032] The braking command is applied to the autonomous driving vehicle so that the autonomous driving vehicle avoids collision with the second obstacle and mitigates impact of collision with the first obstacle.

[0033] According to certain exemplary embodiments of the present application, detecting a first obstacle that collides with the autonomous driving vehicle is based on detecting that an acceleration or deceleration of the autonomous driving vehicle is greater than a predetermined acceleration or deceleration threshold.

[0034] According to some exemplary embodiments of the present application, the operation further includes determining a nearest obstacle within the predetermined proximity of the moving direction, wherein the second obstacle is the nearest obstacle.

[0035] According to certain exemplary embodiments of the present application, the operation also includes determining whether the duration that the autonomous driving vehicle will collide with the second obstacle is greater than a predetermined time threshold.

[0036] According to certain exemplary embodiments of the present application, the minimum deceleration is determined to be zero, and wherein, in response to determining that the duration for which the autonomous driving vehicle will collide with the second obstacle is greater than the predetermined time threshold, the braking command is zero percent of the maximum braking command.

[0037] According to certain exemplary embodiments of the present application, the minimum deceleration is calculated based on a difference between a speed of the autonomous driving vehicle and a speed of the second obstacle and a distance between the autonomous driving vehicle and the second obstacle.

[0038] According to certain exemplary embodiments of the present application, the operation also includes selecting the gear position of the autonomous driving vehicle as a "neutral" position. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] 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.

[0040] Figure 1 is a block diagram illustrating a networked system according to one embodiment.

[0041] Figure 2 is a block diagram illustrating an example of an autonomous driving vehicle according to one embodiment.

[0042] 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.

[0043] Figure 4 is a block diagram illustrating an example of a perception module, a positioning module, and a control module according to one embodiment.

[0044] Figure 5 A collision scenario is shown.

[0045] Figure 6 is a process flow diagram illustrating an example of reducing post-collision damage according to one embodiment.

[0046] Figure 7 is a flow chart illustrating an example of a process for reducing post-collision damage according to one embodiment. DETAILED DESCRIPTION

[0047] 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.

[0048] 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.

[0049] According to some embodiments, the collision condition is determined by the ADV, for example, an inertial measurement unit (IMU) can monitor sudden changes in acceleration (>1G). The current direction of movement of the ADV and the nearest obstacle in the direction of movement can be determined. The gear of the ADV can be set to a "neutral" position. The duration of the second collision with the nearest obstacle can be determined. If the duration to the second collision is greater than a predetermined time threshold, such as 8 seconds, deceleration may not be required. Therefore, the minimum deceleration can be determined to be 0. Therefore, a "zero" braking command can be generated. The ADV may not need to brake. If the duration to the second collision is less than or equal to a predetermined time threshold, the minimum deceleration required to avoid the second collision can be calculated. For example, the minimum deceleration can be calculated based on the difference between the speed of the ADV and the speed of the second obstacle and the distance between the ADV and the second obstacle. The deceleration command can then be applied to the ADV.

[0050] According to one embodiment, a first obstacle that collides with the ADV is detected. A minimum deceleration required for the ADV to avoid colliding with a second obstacle within a predetermined proximity of the direction of movement is determined. A braking command is generated based on the minimum deceleration. The braking command is then applied to the ADV so that the ADV avoids colliding with the second obstacle and mitigates the impact of colliding with the first obstacle.

[0051] In one embodiment, a first obstacle colliding with the ADV is detected based on detecting that an acceleration or deceleration of the ADV is greater than a predetermined acceleration or deceleration threshold. In one embodiment, a nearest obstacle within a predetermined proximity of the direction of movement is determined, wherein the second obstacle is the nearest obstacle. In one embodiment, the gear of the ADV is selected to a "neutral" position.

[0052] In one embodiment, it is determined whether the duration that the ADV will collide with the second obstacle is greater than a predetermined time threshold.

[0053] In one embodiment, in response to determining that the duration of time for which the ADV will collide with the second obstacle is greater than a predetermined time threshold, the minimum deceleration is determined to be zero and the braking command is a “zero” braking command. The ADV is allowed to slide to mitigate the impact of the collision with the first obstacle.

[0054] In one embodiment, in response to determining that the duration the ADV will collide with the second obstacle is less than or equal to a predetermined time threshold, a minimum deceleration is calculated based on a difference between a speed of the ADV and a speed of the second obstacle and a distance between the ADV and the second obstacle.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] Reference now Figure 2 In 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 that is 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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 programs) 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] Based on the driving statistics 123, the machine learning engine 122 generates or trains a set of rules, algorithms, and / or prediction models 124 for various purposes. In one embodiment, the algorithms 124 may include algorithms or models for sensing the driving environment (including detecting the first obstacle that collides with the ADV), algorithms or models for determining the direction of movement, algorithms or models for determining the minimum deceleration, algorithms or models for generating braking commands, and / or algorithms or models for applying braking commands to the ADV, which will be described in detail below. The algorithms 124 may then be uploaded to the ADV for use in real time during autonomous driving.

[0068] 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 scheduling module 307, a collision module I 308, a collision module II 309 and a braking module 310.

[0069] Some or all of the modules 301 to 310 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 310 may be integrated together into an integrated module.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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 location to the destination location, 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 location to the destination location. The route arrangement module 307 can generate a reference line in the form of a topographic map, which determines each route from the starting location to the destination location. 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 examine all possible routes to select and change one of the best routes based on other data provided by other modules, wherein the other data is 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 circumstances 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 routing module 307 .

[0076] 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 line provided by the routing 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] Although not shown, the perception and planning system 110 also includes a data recorder or data collector configured to collect data processed by the components 301 to 310 and sensors over a period of time, which data can be stored as part of the data log 313. For example, the data log 313 includes any predictions, decisions, and paths planned or made in each of the driving / planning cycles. The data log 313 may also include control commands issued and the response or state of the vehicle captured at different time points during each driving cycle (e.g., speed, acceleration, heading, position, etc.). The data log 313 may also include obstacles or moving objects perceived over a period of time and their behavior (e.g., previous positions, speeds, headings, etc. captured in past cycles). The data log 313 can then be used to plan and control the vehicle, or alternatively, the data log 313 can be analyzed offline to improve the driving algorithm or prediction model. The perception and planning system 110 may also include a set of braking rules / models.

[0081] Figure 4 4 is a block diagram 400 showing an example of a perception module, a positioning module, and a control module of an ADV according to one embodiment. Figure 4 , the collision module I 308 in the perception module 302 includes but is not limited to the obstacle module 401 and the direction module 402. Modules 401 to 402 can be integrated into a single module. The collision module II 309 in the positioning module 301 includes but is not limited to the detection module 403 and the direction module 404. Modules 403 to 404 can be integrated into a single module. The braking module 310 in the control module 306 includes but is not limited to the time module 405, the deceleration module 406, the gear module 407 and the braking command module 408, which work together using the braking rules or models 314 to control the ADV to avoid collisions and reduce the impact of collisions with obstacles. It should be noted that modules 405 to 408 can be integrated into a smaller number of modules or a single module.

[0082] According to one embodiment, the driving environment is identified. The driving environment can be identified by the perception module 302 based on various sensors installed on the ADV (such as Figure 2 ) obtained by the ADV. For example, the obstacle module 401 may be configured to detect a first obstacle in contact with the ADV. The detection module 403 in the positioning module 301 may be configured to detect a first obstacle that collides with the ADV. For example, the detection module 403 may use an IMU to monitor sudden changes in acceleration (>1G m / s 2 ). Because ADV cannot generate more than 1G m / s without being in a collision situation. 2 The perception module 302 and the positioning module 301 may work together to detect a first obstacle that collides with the ADV. Based on the collision angle from the first obstacle, the direction module 402 or 404 determines the current moving direction of the ADV. The direction module 402 or 404 may be included in the perception module 302 or the positioning module 301. The obstacle module 401 may also be configured to determine a second obstacle that is the closest obstacle within a predetermined proximity of the moving direction.

[0083] In one embodiment, the time module 405 is configured to determine whether the duration that the ADV will collide with the second obstacle is less than or equal to a predetermined time threshold. The deceleration module 406 is configured to determine the minimum deceleration required for the ADV to avoid colliding with the second obstacle within a predetermined proximity in the direction of movement. If the duration that the ADV will collide with the second obstacle is greater than the predetermined time threshold, the minimum deceleration is zero. Otherwise, the minimum deceleration is calculated based on the difference between the speed of the ADV and the speed of the second obstacle and the distance between the ADV and the second obstacle. The gear module 407 is configured to set the gear of the ADV to a "neutral" position. The braking command module 408 is configured to generate a braking command based on the minimum deceleration and apply the braking command to the ADV.

[0084] Figure 5 Scenario 500 is shown where an ADV 501 collides with a first obstacle 502. During a collision, it is important to reduce damage to the ADV 501 and the first obstacle 502 and avoid damage to other obstacles (e.g., 503). The obstacle may be a vehicle, motorcycle, bicycle, or pedestrian, etc. Currently, ADVs are configured to apply brakes during a collision, which may not always be a good solution. Figure 5 As shown in , when ADV 501 is struck from behind by first obstacle 502, ADV 501 may not be able to absorb the impact or energy from the collision by applying brakes. It may be necessary to develop a post-crash damage mitigation method.

[0085] A post-collision damage mitigation method by manipulating the brake system of an ADV is disclosed herein. Through this method, the ADV can absorb energy from the collision to mitigate the impact of the collision with the first obstacle and avoid collisions with other obstacles (e.g., 503). For example, when the ADV 501 is hit from behind by the first obstacle 502, it may be beneficial to release the brakes so that the ADV 501 can slide accordingly to absorb the energy from the collision.

[0086] First, a collision condition may be determined by the ADV 501. For example, the obstacle module 401 may detect that the first obstacle 502 is in contact with the ADV 501. The detection module 403 in the positioning module 301 may be configured to detect the first obstacle 502 colliding with the ADV. For example, the IMU in the positioning module 301 of the ADV 501 may monitor the sudden change in acceleration or deceleration of the ADV 501. Because the ADV 501 cannot generate more than 1G m / s when not in a collision condition, the ADV 501 may be detected by the positioning module 301. 2 When the IMU detects an acceleration or deceleration greater than 1G m / s 2 When the acceleration or deceleration of the first obstacle 502 and / or the first obstacle 502 contacts the ADV 501, it can be determined that the first obstacle 502 collides with the ADV 501.

[0087] The current moving direction 504 of the ADV may be determined. For example, the direction module 402 in the perception module 302 or the direction module 404 in the positioning module 301 may calculate the current moving direction 504 of the ADV 501 based on the collision angle with the first obstacle 502. The collision angle may refer to the angle between the moving direction of the ADV 501 and the moving direction of the first obstacle 502 at the time of the collision. The current moving direction may be calculated according to the law of conservation of momentum. The nearest obstacle 503 within a predetermined proximity of the moving direction 504 may also be determined. For example, the nearest obstacle 503 within a predetermined proximity of the moving direction 504 may be determined by the obstacle module 401. For example, the predetermined proximity may be 0.1, 0.5, 1, 5, 10, 20, 30, 45 degrees or any value therebetween. The distance 505 between the ADV 501 and the second obstacle 503 may also be determined by the obstacle module 401. The gear of the ADV may be set to a "neutral" position, for example, by the gear module 407.

[0088] The duration of the second collision with the nearest obstacle 503 may be determined. The minimum deceleration to avoid the second collision with the nearest obstacle 503 may be determined. If the duration to the second collision is greater than a predetermined time threshold, such as 8 seconds, the nearest obstacle 503 is not within the proximity of the ADV. Therefore, the minimum deceleration may be determined to be 0. It may not be necessary to decelerate or determine a "zero" minimum deceleration. Therefore, a zero braking command may be generated. The ADV may not need to have brakes. In this case, it is advantageous to generate a "zero" braking command so that the ADV 501 can slide accordingly to absorb the energy from the collision and mitigate the impact of the collision with the first obstacle 502.

[0089] If the duration to the second collision is less than or equal to a predetermined time threshold, such as 8 seconds, the minimum deceleration required to avoid the second collision may be calculated. For example, the minimum deceleration may be calculated based on the difference between the speed of the ADV 501 and the speed of the second obstacle 503 and the distance 505 between the ADV 501 and the second obstacle 503. The minimum deceleration may be calculated by the following equation:

[0090] a = v_difference^2 / (2*distance),

[0091] Wherein “a” is the deceleration of ADV 501 , “v_difference” is the difference between the speed of ADV 501 and the speed of second obstacle 503 , and “distance” is the distance 505 between ADV 501 and second obstacle 503 .

[0092] A deceleration command required to provide the minimum deceleration may be generated. The deceleration command may then be applied to the ADV 501 so that the ADV 501 may decelerate to avoid a second collision with the nearest obstacle 503.

[0093] Figure 6 is a process flow diagram 600 illustrating an example of reducing post-crash damage according to one embodiment.

[0094] At operation 601, a collision condition may be determined by the ADV (e.g., 501). For example, the obstacle module 401 may detect that a first obstacle (e.g., 502) is in contact with the ADV. The detection module 403 in the positioning module 301 may be configured to detect the first obstacle that collides with the ADV. For example, the IMU in the positioning module 301 of the ADV (e.g., 501) may monitor a sudden change in acceleration or deceleration of the ADV. When the IMU detects a speed greater than 1 G m / s 2 When the acceleration or deceleration and / or the first obstacle 502 contacts the ADV (eg, 501), it can be determined that the first obstacle (eg, 502) collides with the ADV (eg, 501).

[0095] At operation 602, the current moving direction (e.g., 504) of the ADV (e.g., 501) may be determined. For example, the direction module 402 in the perception module 302 or the direction module 404 in the positioning module 301 may calculate the current moving direction (e.g., 504) of the ADV based on the collision angle with the first obstacle. The current moving direction may be calculated according to the law of conservation of momentum.

[0096] At operation 603, the nearest obstacle (e.g., 503) within a predetermined proximity of the moving direction (e.g., 504) may also be determined. The distance (e.g., 505) between the ADV (e.g., 501) and the second obstacle (e.g., 503) may also be determined by the obstacle module 401. At operation 604, the gear of the ADV may be set to a "neutral" position, for example, by the gear module 407. At operation 605, it is determined whether the duration of the second collision to the nearest obstacle (e.g., 503) is greater than a predetermined time threshold, for example, 8 seconds.

[0097] At operation 606, in response to determining that the duration of the second collision is greater than a predetermined time threshold, the minimum deceleration may be determined to be zero. At operation 607, a braking command of "zero" may be generated. At operation 608, a braking command of "zero" may be applied to allow the ADV (e.g., 501) to slide accordingly, thereby absorbing energy from the collision and mitigating the impact of the collision with the first obstacle.

[0098] At operation 609, in response to determining that the duration to the second collision is less than or equal to a predetermined time threshold, such as 8 seconds, a minimum deceleration required to avoid the second collision may be calculated. For example, the minimum deceleration may be calculated based on the difference between the speed of the ADV (e.g., 501) and the speed of the second obstacle (e.g., 503) and the distance between the ADV and the second obstacle (e.g., 505).

[0099] At operation 610, a deceleration command required to provide a minimum deceleration may be generated.At operation 611, a deceleration command may be applied to the ADV (eg, 501) so that the ADV may decelerate to avoid a second collision with the nearest obstacle (eg, 503).

[0100] Figure 7 700 is a flowchart illustrating an example of a process for reducing post-collision damage according to one embodiment. Process 700 may be performed by processing logic, which may include software, hardware, or a combination thereof. For example, process 700 may be performed by positioning module 301, perception module 302, and / or control module 306. Figure 7 In operation 701, processing logic detects a first obstacle (e.g., a vehicle, motorcycle, bicycle) that collides with the ADV. In operation 703, processing logic determines a minimum deceleration required for the ADV to avoid colliding with a second obstacle within a predetermined proximity of the direction of movement. In operation 704, processing logic generates a braking command based on the minimum deceleration. In operation 705, processing logic applies the braking command to the ADV so that the ADV avoids colliding with the second obstacle and mitigates the impact of colliding with the first obstacle.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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, the method comprising: Detecting a first obstacle that collides with the autonomous vehicle; After detecting that the autonomous driving vehicle collides with the first obstacle, calculating the current moving direction of the autonomous driving vehicle based on a collision angle between the autonomous driving vehicle and the first obstacle, the collision angle being an angle between the moving direction of the autonomous driving vehicle and the moving direction of the first obstacle at the time of the collision; determining a minimum deceleration required for the autonomous vehicle to avoid a collision with a second obstacle within a predetermined proximity to the current direction of movement of the autonomous vehicle: determining whether a duration for which the autonomous vehicle will collide with the second obstacle is greater than a predetermined time threshold; In response to determining that the duration that the autonomous vehicle will collide with the second obstacle is greater than a predetermined time threshold, determining that the minimum deceleration is zero; in response to determining that the duration that the autonomous vehicle will collide with the second obstacle is less than or equal to the predetermined time threshold, calculating the minimum deceleration based on a difference between a speed of the autonomous vehicle and a speed of the second obstacle and a distance between the autonomous vehicle and the second obstacle; generating a braking command based on the minimum deceleration; as well as The braking command is applied to the autonomous driving vehicle so that the autonomous driving vehicle avoids collision with the second obstacle and mitigates impact of collision with the first obstacle.

2. The method according to claim 1, wherein: Detecting a first obstacle that collides with the autonomous vehicle is based on detecting that an acceleration or deceleration of the autonomous vehicle is greater than a predetermined acceleration or deceleration threshold.

3. The method of claim 1 , further comprising determining the nearest obstacle within the predetermined proximity of the current direction of movement of the autonomous vehicle, wherein: The second obstacle is the nearest obstacle.

4. The method according to claim 1 also includes selecting the gear of the autonomous driving vehicle as a "neutral" position.

5. A non-transitory machine-readable medium having stored therein instructions, the instructions, when executed by a processor, causing the processor to perform operations comprising: Detecting a first obstacle that collides with the autonomous vehicle; After detecting that the autonomous driving vehicle collides with the first obstacle, calculating the current moving direction of the autonomous driving vehicle based on a collision angle between the autonomous driving vehicle and the first obstacle, the collision angle being an angle between the moving direction of the autonomous driving vehicle and the moving direction of the first obstacle at the time of the collision; determining a minimum deceleration required for the autonomous vehicle to avoid a collision with a second obstacle within a predetermined proximity to the current direction of movement of the autonomous vehicle: determining whether a duration for which the autonomous vehicle will collide with the second obstacle is greater than a predetermined time threshold; In response to determining that the duration that the autonomous vehicle will collide with the second obstacle is greater than a predetermined time threshold, determining that the minimum deceleration is zero; in response to determining that the duration that the autonomous vehicle will collide with the second obstacle is less than or equal to the predetermined time threshold, calculating the minimum deceleration based on a difference between a speed of the autonomous vehicle and a speed of the second obstacle and a distance between the autonomous vehicle and the second obstacle; generating a braking command based on the minimum deceleration; as well as The braking command is applied to the autonomous driving vehicle so that the autonomous driving vehicle avoids collision with the second obstacle and mitigates impact of collision with the first obstacle.

6. The non-transitory machine-readable medium of claim 5, wherein: Detecting a first obstacle that collides with the autonomous vehicle is based on detecting that an acceleration or deceleration of the autonomous vehicle is greater than a predetermined acceleration or deceleration threshold.

7. The non-transitory machine-readable medium of claim 5, wherein: The operations also include determining a nearest obstacle within the predetermined proximity of the current direction of movement of the autonomous vehicle, wherein the second obstacle is the nearest obstacle.

8. The non-transitory machine-readable medium of claim 5, wherein: The operation also includes selecting the gear of the autonomous driving vehicle to a "neutral" position.

9. 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: Detecting a first obstacle that collides with the autonomous vehicle; After detecting that the autonomous driving vehicle collides with the first obstacle, calculating the current moving direction of the autonomous driving vehicle based on a collision angle between the autonomous driving vehicle and the first obstacle, the collision angle being an angle between the moving direction of the autonomous driving vehicle and the moving direction of the first obstacle at the time of the collision; determining a minimum deceleration required for the autonomous vehicle to avoid a collision with a second obstacle within a predetermined proximity of a current direction of movement of the autonomous vehicle: determining whether a duration for which the autonomous vehicle will collide with the second obstacle is greater than a predetermined time threshold; in response to determining that a duration for which the autonomous vehicle will collide with the second obstacle is greater than a predetermined time threshold, determining that the minimum deceleration is zero; in response to determining that a duration for which the autonomous vehicle will collide with the second obstacle is less than or equal to a predetermined time threshold, calculating the minimum deceleration based on a difference between a speed of the autonomous vehicle and a speed of the second obstacle and a distance between the autonomous vehicle and the second obstacle; generating a braking command based on the minimum deceleration; and The braking command is applied to the autonomous driving vehicle so that the autonomous driving vehicle avoids collision with the second obstacle and mitigates impact of collision with the first obstacle.

10. The data processing system according to claim 9, wherein: Detecting a first obstacle that collides with the autonomous vehicle is based on detecting that an acceleration or deceleration of the autonomous vehicle is greater than a predetermined acceleration or deceleration threshold.

11. The data processing system according to claim 9, wherein: The operations also include determining a nearest obstacle within the predetermined proximity of the current direction of movement of the autonomous vehicle, wherein the second obstacle is the nearest obstacle.

12. The data processing system according to claim 9, wherein: The operation also includes selecting the gear of the autonomous driving vehicle to a "neutral" position.

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