Decision method, device and equipment of autonomous vehicle and autonomous vehicle

Abstract

The disclosure provides a decision-making method and device of an autonomous vehicle, equipment and the autonomous vehicle, relates to the technical field of computers, and particularly relates to the technical field of autonomous driving. The implementation scheme is: obtaining an obstacle detection result of a current frame, the obstacle detection result including a first position of a target obstacle on a vehicle driving path; in response to a first distance between the target obstacle and the vehicle being less than a second threshold and the current frame not being a first frame in which the target obstacle is detected, obtaining a historical decision-making label sequence of the target obstacle; and based on the historical decision-making label sequence, determining whether the target obstacle is used to determine a current driving behavior of the vehicle. The disclosed scheme can screen close-range low-height obstacles, reduce frequent hard braking, and ensure safe and smooth driving of the vehicle.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more particularly to the field of autonomous driving technology, specifically to a decision-making method and apparatus for an autonomous vehicle, an electronic device, a computer-readable storage medium, a computer program product, and an autonomous vehicle. Background Technology

[0002] Autonomous driving technology involves multiple aspects, including environmental perception, behavioral decision-making, trajectory planning, and motion control. Relying on the collaborative efforts of sensors, vision computing systems, and positioning systems, vehicles with autonomous driving capabilities can operate automatically without driver intervention or with minimal driver input. To ensure safe driving, vehicles in motion need to detect obstacles in their surroundings and make driving decisions based on the detection results to determine their next driving actions.

[0003] The methods described in this section are not necessarily methods that had been previously conceived or adopted. Unless otherwise specified, no method described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be accepted in any prior art. Summary of the Invention

[0004] This disclosure provides a decision-making method and apparatus for autonomous vehicles, electronic devices, computer-readable storage media, computer program products, and autonomous vehicles.

[0005] According to one aspect of this disclosure, a decision-making method for an autonomous vehicle is provided, comprising: acquiring an obstacle detection result of a current frame, wherein the obstacle detection result includes a first position of a target obstacle on the vehicle's driving path, the height of the target obstacle being less than a first threshold; in response to a first distance between the target obstacle and the vehicle being less than a second threshold and the current frame not being the first frame in which the target obstacle was detected, acquiring a historical decision label sequence of the target obstacle, wherein the historical decision label sequence includes at least one decision label corresponding to at least one historical frame, each of the at least one decision label indicating whether the target obstacle in the corresponding historical frame is used to determine the driving behavior of the vehicle; and determining, based on the historical decision label sequence, whether the target obstacle is used to determine the current driving behavior of the vehicle.

[0006] According to another aspect of this disclosure, a decision-making device for an autonomous vehicle is provided, comprising: a first acquisition module configured to acquire an obstacle detection result of a current frame, wherein the obstacle detection result includes a first position of a target obstacle on the vehicle's driving path, and the height of the target obstacle is less than a first threshold; a second acquisition module configured to, in response to a first distance between the target obstacle and the vehicle being less than a second threshold and the current frame not being the first frame in which the target obstacle was detected, acquire a historical decision label sequence of the target obstacle, wherein the historical decision label sequence includes at least one decision label corresponding to at least one historical frame, each of the at least one decision label indicating whether the target obstacle in the corresponding historical frame is used to determine the driving behavior of the vehicle; and a judgment module configured to, based on the historical decision label sequence, determine whether the target obstacle is used to determine the current driving behavior of the vehicle.

[0007] According to one aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the decision-making method of the aforementioned autonomous vehicle.

[0008] According to one aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause a computer to execute the decision-making method of the aforementioned autonomous vehicle.

[0009] According to one aspect of this disclosure, a computer program product is provided, including computer program instructions, wherein the computer program instructions, when executed by a processor, implement the decision-making method of the aforementioned autonomous vehicle.

[0010] According to one aspect of this disclosure, an autonomous vehicle is provided, including the aforementioned electronic devices.

[0011] According to one or more embodiments of this disclosure, the safe and stable operation of the vehicle can be guaranteed.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] The accompanying drawings exemplify embodiments and form part of the specification, serving together with the textual description to explain exemplary implementations of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0014] Figure 1 A schematic diagram of an exemplary system in which the various methods described herein may be implemented according to embodiments of the present disclosure is shown;

[0015] Figure 2 A flowchart of a decision-making method for an autonomous vehicle according to an embodiment of the present disclosure is shown;

[0016] Figure 3 A structural block diagram of a decision-making device for an autonomous vehicle according to an embodiment of the present disclosure is shown; and

[0017] Figure 4 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0018] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0019] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0020] The terminology used in the description of the various examples described in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0021] In the field of autonomous driving technology, the collection and processing of information about the vehicle's surrounding environment and obstacles is the foundation and prerequisite for realizing autonomous driving. The vehicle's perception system can detect obstacles in the surrounding environment and report the obstacle detection results to the decision-making system, so that the decision-making system can formulate reasonable driving behaviors based on the obstacle detection results, such as braking or detouring.

[0022] In related technologies, when a vehicle's perception system reports obstacle detection results to the decision-making system, it only considers whether the obstacle exists. That is, it reports the obstacle to the decision-making system as soon as it is detected, without considering that some obstacles are obstacles that do not need to be avoided or are incorrectly detected obstacles. This leads to unreasonable emergency braking or detours by the vehicle, affecting the driving experience.

[0023] To address the aforementioned issues, this disclosure provides a decision-making method for autonomous vehicles that can avoid unnecessary emergency braking and ensure safe and stable vehicle operation.

[0024] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0025] Figure 1 A schematic diagram of an exemplary system 100 in which the various methods and apparatus described herein can be implemented according to embodiments of this disclosure is shown. Reference Figure 1 The system 100 includes a motor vehicle 110, a server 120, and one or more communication networks 130 that couple the motor vehicle 110 to the server 120.

[0026] In embodiments of this disclosure, the motor vehicle 110 may include electronic devices according to embodiments of this disclosure and / or be configured to perform decision-making methods for an autonomous vehicle according to embodiments of this disclosure.

[0027] Server 120 may run one or more services or software applications that enable the safe and proper functioning of autonomous vehicles. In some embodiments, server 120 may also provide other services or software applications, which may include non-virtual environments and virtual environments. Figure 1 In the configuration shown, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or combinations thereof that can be executed by one or more processors. A user of motor vehicle 110 may sequentially interact with server 120 using one or more client applications to utilize the services provided by these components. It should be understood that various different system configurations are possible and may differ from system 100. Therefore, Figure 1 This is an example of a system used to implement the various methods described herein, and is not intended to be limiting.

[0028] Server 120 may include one or more general-purpose computers, special-purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for servers). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0029] The computing unit in server 120 can run one or more operating systems, including any of the aforementioned operating systems and any commercially available server operating system. Server 120 can also run any of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0030] In some implementations, server 120 may include one or more applications to analyze and merge data feeds and / or event updates received from vehicle 110. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of vehicle 110.

[0031] Network 130 can be any type of network well known to those skilled in the art, and can support data communication using any of a variety of available protocols (including, but not limited to, TCP / IP, SNA, IPX, etc.). By way of example only, one or more networks 130 can be satellite communication networks, local area networks (LANs), Ethernet-based networks, token ring networks, wide area networks (WANs), the Internet, virtual networks, virtual private networks (VPNs), intranets, extranets, blockchain networks, public switched telephone networks (PSTNs), infrared networks, wireless networks (including, for example, Bluetooth, Wi-Fi), and / or any combination of these with other networks.

[0032] System 100 may also include one or more databases 150. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 150 may be used to store information such as audio files and video files. Databases 150 may reside in various locations. For example, a database used by server 120 may be local to server 120, or it may be located away from server 120 and may communicate with server 120 via a network-based or dedicated connection. Databases 150 may be of different types. In some embodiments, databases 150 used by server 120 may be relational databases. One or more of these databases may store, update, and retrieve data from and from the databases in response to commands.

[0033] In some embodiments, one or more of the databases 150 may also be used by an application to store application data. The databases used by the application may be of different types, such as key-value stores, object stores, or regular stores supported by a file system.

[0034] Motor vehicle 110 may include sensors 111 for sensing the surrounding environment. Sensors 111 may include one or more of the following sensors: a visual camera, an infrared camera, an ultrasonic sensor, a millimeter-wave radar, and a lidar (LiDAR). Different sensors can provide different detection accuracy and range. Cameras may be mounted in front of, behind, or at other locations on the vehicle. Visual cameras can capture the situation inside and outside the vehicle in real time and present it to the driver and / or passengers. In addition, by analyzing the images captured by the visual cameras, information such as static obstacles, dynamic obstacles, traffic light indications, intersection conditions, and the operating status of other vehicles can be obtained. Infrared cameras can capture objects in night vision conditions. Ultrasonic sensors may be mounted around the vehicle to measure the distance of objects outside the vehicle using the strong directionality of ultrasound. Millimeter-wave radar may be mounted in front of, behind, or at other locations on the vehicle to measure the distance of objects outside the vehicle using the characteristics of electromagnetic waves. LiDAR may be mounted in front of, behind, or at other locations on the vehicle to detect the edges and shape information of objects, thereby performing object recognition and tracking. Due to the Doppler effect, the radar device can also measure the speed changes of the vehicle and moving objects.

[0035] The motor vehicle 110 may also include a communication device 112. The communication device 112 may include a satellite positioning module capable of receiving satellite positioning signals (e.g., BeiDou, GPS, GLONASS, and GALILEO) from satellite 141 and generating coordinates based on these signals. The communication device 112 may also include a module for communicating with a mobile communication base station 142. The mobile communication network can implement any suitable communication technology, such as current or emerging wireless communication technologies (e.g., 5G technology) like GSM / GPRS, CDMA, and LTE. The communication device 112 may also have a vehicle-to-everything (V2X) module, configured to enable vehicle-to-the-world communication, for example, vehicle-to-vehicle (V2V) communication with other vehicles 143 and vehicle-to-infrastructure (V2I) communication with infrastructure 144. Furthermore, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to smartphones, tablets, or wearable devices such as watches) via, for example, a wireless local area network conforming to the IEEE 802.11 standard or Bluetooth. Using the communication device 112, the motor vehicle 110 can also access the server 120 via the network 130.

[0036] The motor vehicle 110 may also include a control unit 113. The control unit 113 may include a processor, such as a central processing unit (CPU) or a graphics processing unit (GPU), or other dedicated processors, that communicates with various types of computer-readable storage devices or media. The control unit 113 may include an autonomous driving system for automatically controlling various actuators in the vehicle. The autonomous driving system is configured to control the powertrain, steering system, and braking system of the motor vehicle 110 (not shown) via multiple actuators in response to inputs from multiple sensors 111 or other input devices to control acceleration, steering, and braking respectively, without human intervention or with limited human intervention. Some processing functions of the control unit 113 can be implemented via cloud computing. For example, some processing can be performed using an onboard processor while other processing can be performed using cloud computing resources. The control unit 113 may be configured to perform methods according to this disclosure. Furthermore, the control unit 113 may be implemented as an example of an electronic device on the motor vehicle side (client) according to this disclosure.

[0037] Figure 1 The system 100 can be configured and operated in various ways to enable the application of the various methods and apparatus described in this disclosure.

[0038] According to some embodiments, the motor vehicle 110 also includes a perception system and a decision system. The perception system includes sensors 111, which include one or more of the following sensors: a visual camera, an infrared camera, an ultrasonic sensor, a millimeter-wave radar, and a lidar (LiDAR) sensor, and at least one processor for processing data acquired by the one or more sensors. The decision system can acquire the data processed by the perception system and provide driving decisions for the vehicle based on the acquired data.

[0039] According to some embodiments, the decision-making method for autonomous vehicles in this disclosure can be provided by a client (e.g., Figure 1 The motor vehicle 110 shown can perform this action, or it can be performed by server 120 or other servers. Figure 1 (Not shown in the image) The obstacle information involved in the decision-making method of the autonomous vehicle in this embodiment of the present disclosure can be obtained by the perception system of the motor vehicle 110 or by other means, which is not limited here.

[0040] According to embodiments of this disclosure, a decision-making method for autonomous vehicles is provided. Figure 2 A flowchart of a decision-making method 200 for an autonomous vehicle according to an embodiment of the present disclosure is shown. The execution entity of each step of method 200 is typically an autonomous vehicle (e.g., Figure 1 The vehicle in question (110) can also be a server (e.g., Figure 1 The server 120 shown Figure 1 Other servers not shown in the image).

[0041] like Figure 2 As shown, method 200 includes steps S210-S230.

[0042] In step S210, the obstacle detection result of the current frame is obtained, wherein the obstacle detection result includes the first position of the target obstacle on the vehicle driving path, and the height of the target obstacle is less than a first threshold.

[0043] In step S220, in response to the first distance between the target obstacle and the vehicle being less than a second threshold and the current frame not being the first frame in which the target obstacle is detected, a historical decision label sequence of the target obstacle is obtained, wherein the historical decision label sequence includes at least one decision label corresponding to at least one historical frame, and each decision label in the at least one decision label indicates whether the target obstacle in the corresponding historical frame is used to determine the driving behavior of the vehicle.

[0044] In step S230, based on the historical decision label sequence, it is determined whether the target obstacle is used to determine the vehicle's current driving behavior.

[0045] According to embodiments of this disclosure, when a low obstacle (i.e., a target obstacle with a height less than the first threshold) is detected at a location relatively close to the vehicle (i.e., at a first distance less than a second threshold), instead of directly reporting the low obstacle to the vehicle's decision-making system for driving decisions, the system considers the historical reporting history of the low obstacle to determine its impact on the current driving decision, thereby determining whether to report it to the decision-making system. This allows for the filtering of nearby low obstacles, avoiding frequent emergency braking caused by reporting all detected nearby low obstacles to the decision-making system, and ensuring safe and stable vehicle operation.

[0046] The following details each step of method 200.

[0047] In step S210, the obstacle detection result of the current frame is obtained, wherein the obstacle detection result includes the first position of the target obstacle on the vehicle driving path, and the height of the target obstacle is less than a first threshold.

[0048] Autonomous vehicles (e.g., Figure 1 The vehicle 110 shown includes a perception system and a decision-making system. The perception system includes multiple sensors (e.g., lidar, millimeter-wave radar, and cameras). Lidar can acquire point cloud data of obstacles (including the obstacle's position coordinates and height information), and cameras can acquire image information of obstacles. The perception system can fuse and process the obstacle data (including point cloud data and image information) detected by multiple sensors to obtain obstacle detection results (e.g., information such as the obstacle's position, size, and category), and output the obstacle detection results to the decision-making system so that the decision-making system can make reasonable driving decisions based on the obstacle detection results (e.g., braking, detouring, running over, overtaking, etc.).

[0049] According to some embodiments, the height of the target obstacle can be determined based on point cloud data. For example, the height of the highest point in the point cloud data of the target obstacle can be determined as the height of the obstacle, or the height difference between the highest and lowest points in the point cloud data of the target obstacle can be determined as the height of the obstacle. If the height of the target obstacle is less than a first threshold (e.g., 20cm), the obstacle may be a crushable obstacle (e.g., fallen leaves, plastic bags, etc.). However, not all obstacles with a height less than the first threshold are crushable obstacles, so further judgment is needed based on more information (e.g., the obstacle's historical decision label sequence, the obstacle's position information in the previous frame, etc.). It is understood that the first threshold can be set according to actual conditions, such as based on the vehicle's chassis height, and is not limited here. The first position of the target obstacle can be obtained through multiple sensors of the perception system. The first position can be coordinate position information in the same coordinate system as the vehicle.

[0050] According to some embodiments, the obstacle detection result also includes the category to which the target obstacle belongs. In response to the category being non-collision-prone, the determination of the target obstacle is used to determine the vehicle's current driving behavior. For non-collision-prone obstacles such as pedestrians, motor vehicles, and bicycles, it is required to absolutely avoid collisions with these obstacles. Therefore, when the target obstacle is a non-collision-prone obstacle, there is no need to rely on more information to further determine whether it can be run over; it is directly reported to the decision-making system to determine the vehicle's current driving behavior, ensuring vehicle driving safety and the safety of other vehicles and pedestrians.

[0051] In step S220, in response to the first distance between the target obstacle and the vehicle being less than a second threshold and the current frame not being the first frame in which the target obstacle is detected, a historical decision label sequence of the target obstacle is obtained, wherein the historical decision label sequence includes at least one decision label corresponding to at least one historical frame, and each decision label in the at least one decision label indicates whether the target obstacle in the corresponding historical frame is used to determine the driving behavior of the vehicle.

[0052] It should be noted that, in the embodiments of this disclosure, using a target obstacle to determine the vehicle's driving behavior means reporting relevant information about the target obstacle to the vehicle's decision-making system so that the decision-making system can determine the vehicle's driving behavior based on the relevant information about the target obstacle. Similarly, not using a target obstacle to determine the vehicle's driving behavior means not reporting relevant information about the target obstacle to the vehicle's decision-making system, so that the decision-making system does not consider the target obstacle when determining the vehicle's driving behavior.

[0053] According to some embodiments, the first distance between the target obstacle and the vehicle can be calculated based on the vehicle's position coordinates and the position coordinates of the target obstacle's first position. If the first distance is less than a second threshold (e.g., 40m), the target obstacle is considered close to the vehicle. If the target obstacle is used to determine the vehicle's current driving behavior—that is, if the target obstacle is reported to the decision system—the driving behavior determined by the decision system based on the close-range target obstacle is likely to be sudden braking, resulting in unstable vehicle movement and poor driver or passenger comfort. In this case, it is necessary to determine whether the target obstacle is the first obstacle detected by the vehicle's perception system, i.e., whether the current frame is the first frame in which the target obstacle is detected. Different conditions can be used to determine whether to use the target obstacle to determine the vehicle's current driving behavior based on different determination results.

[0054] It should be noted that, since different sensors operate at different frequencies (for example, the camera operates at 10Hz and the lidar operates at 15Hz), data from multiple sensors needs to be combined to determine whether the current frame is the first frame in which the target obstacle is detected.

[0055] According to some embodiments, if the first distance between the target obstacle and the vehicle is less than a second threshold, and the current frame is not the first frame in which the target obstacle was detected (i.e., the obstacle was detected before the current frame), the decision labels of at least one historical frame in the historical decision label sequence can be combined to determine whether the target obstacle is used to determine the current driving behavior of the vehicle.

[0056] According to some embodiments, in response to a first distance being less than a second threshold and the current frame being the first frame in which the target obstacle is detected, it is determined that the target obstacle is not used to determine the vehicle's current driving behavior. Since the obstacle does not have historical information (i.e., historical decision label sequence) when it is first detected, target obstacles that are detected for the first time and are close to the vehicle (less than the second threshold) can be directly filtered out and not reported to the decision system. That is, the target obstacle is not used to determine the vehicle's driving behavior, thereby avoiding the vehicle from making sudden braking to avoid the target obstacle.

[0057] In some embodiments, the second threshold is determined based on the vehicle's speed. It is understood that the faster the vehicle travels, the greater its braking distance. The second threshold can be set to be the same as the braking distance. The braking distance (second threshold) varies with vehicle speed and can be calculated using the following formula:

[0058]

[0059] Where v is the vehicle's speed, and a is the vehicle's deceleration during braking, which is a preset constant. Typically, to avoid sudden braking, the peak deceleration needs to be controlled at 2 m / s². 2 Within this range, the corresponding average deceleration is generally 1-1.5 m / s². 2 Meanwhile, considering that the braking distance would be too short at excessively low speeds (e.g., 5 m / s), a minimum limit (e.g., 40 m) can be set for the second threshold. That is, at higher speeds, the second threshold increases with increasing speed; at lower speeds, the second threshold can be set to a fixed value without changing with speed. It is understandable that the deceleration during emergency braking can be set according to actual needs and is not restricted here.

[0060] In step S230, based on the historical decision label sequence, it is determined whether the target obstacle is used to determine the vehicle's current driving behavior.

[0061] According to some embodiments, in response to the ratio of a first quantity to a second quantity being less than a third threshold, it is determined that the target obstacle is not used to determine the vehicle's current driving behavior. The first quantity is the number of decision tags with a "yes" value in at least one decision tag, and the second quantity is the total number of at least one decision tag. A "yes" value in a decision tag indicates that the target obstacle was used to determine the vehicle's driving behavior in the corresponding historical frame, meaning the target obstacle was reported to the decision system in that historical frame. A "no" value in a decision tag indicates that the target obstacle was not used to determine the vehicle's driving behavior in the corresponding historical frame, meaning the target obstacle was not reported to the decision system in that historical frame. Alternatively, a sliding window method can be used to select decision tags from the historical decision tag sequence for the most recent frames (e.g., the most recent 10 frames), count the first quantity of decision tags with a "yes" value in the most recent frames, and calculate the ratio of this first quantity to the total number of decision tags in the most recent frames.

[0062] If the ratio of the first number to the second number is less than the third threshold, it means that the target obstacle is reported to the decision-making system at a low frequency. This indicates that the target obstacle has not been consistently reported to the decision-making system in historical frames, and the historical driving decisions made by the decision-making system may have taken the target obstacle less into account. If the target obstacle is reported to the decision-making system in the current frame, it is very likely to cause a sudden change in driving behavior, leading to emergency braking. Therefore, in order to ensure smooth vehicle driving, the target obstacle can be filtered out, and it can be determined that the target obstacle is not used to determine the current driving behavior of the vehicle; that is, it should not be reported to the decision-making system.

[0063] According to some embodiments, in response to a first distance between a target obstacle and a vehicle being less than a second threshold and the current frame not being the first frame in which the target obstacle was detected, the second position of the target obstacle in the previous frame is obtained; wherein, determining whether the target obstacle is used to determine the current driving behavior of the vehicle based on the historical decision label sequence includes: determining whether the target obstacle is used to determine the current driving behavior of the vehicle based on the second distance between the first position and the second position and the historical decision label sequence.

[0064] If the target obstacle is close to the vehicle and is not being detected for the first time in the current frame, the determination of whether the target obstacle is used to determine the vehicle's current driving behavior can be made based on a second distance between the obstacle's position in the current frame (first position) and its position in the previous frame (second position), as well as whether the target obstacle has been consistently reported in historical frames. The second distance between the first and second positions is used to determine if the target obstacle has changed position. If the second distance is too large, the target obstacle has changed position and is likely to have been falsely detected. To avoid the vehicle braking suddenly due to a falsely detected target obstacle, the obstacle is filtered out, and it is determined that the target obstacle is not used to determine the vehicle's current driving behavior.

[0065] According to some embodiments, in response to satisfying any of the following conditions, a target obstacle is determined not to be used to determine the vehicle's current driving behavior: a second distance is greater than or equal to a fourth threshold; or the ratio of a first quantity to a second quantity is less than a third threshold, wherein the first quantity is the number of decision labels with a value of "yes" in at least one decision label, and the second quantity is the total number of at least one decision label. If the target obstacle belongs to either a situation where its position changes or it has not been consistently reported to the decision system in historical frames, the target obstacle is determined not to be used to determine the vehicle's current driving behavior, and the obstacle is filtered out. This avoids the vehicle from suddenly braking due to incorrectly detected obstacles or obstacles that have not been consistently reported to the decision system in historical frames, ensuring smooth vehicle operation.

[0066] According to some embodiments, when the first distance between the target obstacle and the vehicle is less than a second threshold, more conditions can be combined to determine whether to report the target obstacle to the decision system: If the current frame is not the first frame in which the target obstacle was detected, the decision tags in historical frames are used to determine whether the target obstacle has been consistently reported to the decision system. If the target obstacle has been consistently reported to the decision system and has not undergone a positional change, then the target obstacle is reported to the decision system in the current frame. Only when the above conditions are met will the target obstacle be reported to the decision system; otherwise, the target obstacle will be filtered out.

[0067] According to some embodiments, in response to determining that a target obstacle is not useful for determining the vehicle's current driving behavior, the information of the target obstacle is reported to a cloud server. If it is determined that a target obstacle is not useful for determining the vehicle's current driving behavior, the information of the target obstacle will not be reported to the decision-making system. For the sake of vehicle driving safety, while filtering out target obstacles, the target obstacle is also reported to the cloud server so that relevant personnel can perform cloud-based remote driving of the vehicle and make timely driving decisions based on the target obstacle information to deal with emergencies.

[0068] According to embodiments of this disclosure, a decision-making device for an autonomous vehicle is provided. Figure 3 A structural block diagram of a decision-making device 300 for an autonomous vehicle according to an embodiment of the present disclosure is shown. Figure 3 As shown, the device 300 includes a first acquisition module 310, a second acquisition module 320, and a judgment module 330.

[0069] The first acquisition module 310 is configured to acquire the obstacle detection result of the current frame, wherein the obstacle detection result includes the first position of the target obstacle on the vehicle's driving path and the height of the target obstacle is less than a first threshold.

[0070] The second acquisition module 320 is configured to acquire a historical decision label sequence of the target obstacle in response to a first distance between the target obstacle and the vehicle being less than a second threshold and the current frame not being the first frame in which the target obstacle is detected. The historical decision label sequence includes at least one decision label corresponding to at least one historical frame, and each decision label in the at least one decision label indicates whether the target obstacle in the corresponding historical frame is used to determine the driving behavior of the vehicle.

[0071] The judgment module 330 is configured to determine whether a target obstacle is used to determine the vehicle’s current driving behavior based on a historical decision label sequence.

[0072] According to some embodiments, the determination module 330 includes: the determination unit is configured to determine that the target obstacle is not used to determine the current driving behavior of the vehicle in response to a ratio of a first quantity to a second quantity being less than a third threshold, wherein the first quantity is the number of decision labels with a value of "yes" in at least one decision label, and the second quantity is the total number of at least one decision label.

[0073] According to some embodiments, the device 300 further includes: a third acquisition module configured to acquire the second position of the target obstacle in the previous frame in response to a first distance between the target obstacle and the vehicle being less than a second threshold and the current frame not being the first frame in which the target obstacle was detected; wherein the judgment module 330 further includes: a judgment unit configured to determine whether the target obstacle is used to determine the current driving behavior of the vehicle based on the second distance between the first position and the second position and a historical decision label sequence.

[0074] According to some embodiments, the determination unit includes: a determination subunit configured to determine that a target obstacle is not used to determine the current driving behavior of a vehicle in response to any of the following conditions: a second distance is greater than or equal to a fourth threshold; or the ratio of a first quantity to a second quantity is less than a third threshold, wherein the first quantity is the number of decision labels with a value of "yes" in at least one decision label, and the second quantity is the total number of at least one decision label.

[0075] According to some embodiments, the device 300 further includes: a first determination module configured to determine that the target obstacle is not used to determine the current driving behavior of the vehicle in response to a first distance being less than a second threshold and the current frame being a first frame in which the target obstacle is detected.

[0076] According to some embodiments, the obstacle detection result also includes the category to which the target obstacle belongs, and the device 300 further includes: a second determination module configured to determine the target obstacle in response to the category being non-collisionable to determine the current driving behavior of the vehicle.

[0077] According to some embodiments, the device 300 further includes a reporting module configured to report information about the target obstacle to a cloud server in response to determining that the target obstacle is not used to determine the current driving behavior of the vehicle.

[0078] According to some embodiments, the second threshold is determined based on the vehicle's speed.

[0079] It should be understood that Figure 3 The various modules or units of the device 300 shown can be connected with Figure 2 The steps in method 200 described correspond to each other. Therefore, the operations, features, and advantages described in method 200 are also applicable to apparatus 300 and its constituent modules and units. For the sake of brevity, some operations, features, and advantages will not be repeated here.

[0080] Although specific functions have been discussed with reference to specific modules above, it should be noted that the functions of the various modules discussed in this article can be divided into multiple modules, and / or at least some functions of multiple modules can be combined into a single module.

[0081] It should also be understood that the various techniques described herein can be implemented in software, hardware, components, or program modules. The above... Figure 3 The various modules described herein may be implemented in hardware or in hardware in combination with software and / or firmware. For example, these modules may be implemented as computer program code / instructions configured to execute in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules may be implemented as hardware logic / circuit. For example, in some embodiments, one or more modules of 310-330 may be implemented together in a System on Chip (SoC). The SoC may include an integrated circuit chip (which includes a processor (e.g., a Central Processing Unit (CPU), microcontroller, microprocessor, digital signal processor (DSP), etc.), memory, one or more communication interfaces, and / or one or more components of other circuitry) and may optionally execute received program code and / or include embedded firmware to perform functions.

[0082] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0083] According to embodiments of the present disclosure, an electronic device is also provided, including: at least one processor; and a memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the decision-making method of an autonomous vehicle according to embodiments of the present disclosure.

[0084] According to embodiments of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute a decision-making method for an autonomous vehicle according to embodiments of the present disclosure is also provided.

[0085] According to embodiments of the present disclosure, a computer program product is also provided, including computer program instructions that, when executed by a processor, implement the decision-making method for an autonomous vehicle according to embodiments of the present disclosure.

[0086] According to embodiments of this disclosure, an autonomous vehicle is also provided, including the aforementioned electronic equipment.

[0087] refer to Figure 4 The present invention describes a structural block diagram of an electronic device 400 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0088] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0089] Multiple components in electronic device 400 are connected to I / O interface 405, including: input unit 406, output unit 407, storage unit 408, and communication unit 409. Input unit 406 can be any type of device capable of inputting information to electronic device 400. Input unit 406 can receive input digital or character information and generate key signal input related to user settings and / or function control of electronic device, and may include, but is not limited to, a mouse, keyboard, touchscreen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 407 can be any type of device capable of presenting information, and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 408 may include, but is not limited to, disk and optical disk. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth. TM Equipment, 802.11 equipment, Wi-Fi equipment, WiMAX equipment, cellular communication equipment and / or the like.

[0090] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform method 200 by any other suitable means (e.g., by means of firmware).

[0091] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0093] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0094] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0095] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0096] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0097] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0098] While embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of this disclosure is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as technology evolves, many elements described herein can be replaced by equivalents that appear after this disclosure.

Claims

1. A decision-making method for an autonomous vehicle, comprising: Obtain the obstacle detection result of the current frame, wherein the obstacle detection result includes the first position of the target obstacle on the vehicle's driving path, and the height of the target obstacle is less than a first threshold. In response to a first distance between the target obstacle and the vehicle being less than a second threshold and the current frame not being the first frame in which the target obstacle was detected, a historical decision label sequence of the target obstacle is obtained, wherein the historical decision label sequence includes at least one decision label corresponding to at least one historical frame, and each of the at least one decision label indicates whether the target obstacle in the corresponding historical frame is used to determine the driving behavior of the vehicle; and Based on the historical decision label sequence, determining whether the target obstacle is used to determine the vehicle's current driving behavior includes: In response to the ratio of the first quantity to the second quantity being less than a third threshold, it is determined that the target obstacle is not used to determine the current driving behavior of the vehicle, wherein the first quantity is the number of decision labels with a value of "yes" among the at least one decision label, and the second quantity is the total number of the at least one decision label.

2. The method according to claim 1, further comprising: In response to a first distance between the target obstacle and the vehicle being less than a second threshold and the current frame not being the first frame in which the target obstacle was detected, the second position of the target obstacle in the previous frame is obtained; Specifically, determining whether the target obstacle is used to determine the vehicle's current driving behavior based on the historical decision label sequence includes: Based on the second distance between the first position and the second position and the historical decision label sequence, it is determined whether the target obstacle is used to determine the current driving behavior of the vehicle.

3. The method according to claim 1, further comprising: In response to the first distance being less than the second threshold and the current frame being the first frame in which the target obstacle is detected, it is determined that the target obstacle is not used to determine the current driving behavior of the vehicle.

4. The method according to any one of claims 1-3, wherein, The obstacle detection result also includes the category to which the target obstacle belongs, and the method further includes: In response to the category being non-collisionable, the target obstacle is determined to ascertain the vehicle's current driving behavior.

5. The method according to any one of claims 1-3, further comprising: In response to determining that the target obstacle is not used to determine the current driving behavior of the vehicle, the information of the target obstacle is reported to the cloud server.

6. The method according to any one of claims 1-3, wherein, The second threshold is determined based on the vehicle's speed.

7. A decision-making device for an autonomous vehicle, comprising: The first acquisition module is configured to acquire the obstacle detection result of the current frame, wherein the obstacle detection result includes the first position of the target obstacle on the vehicle's driving path, and the height of the target obstacle is less than a first threshold. The second acquisition module is configured to, in response to a first distance between the target obstacle and the vehicle being less than a second threshold and the current frame not being the first frame in which the target obstacle was detected, acquire a historical decision label sequence of the target obstacle, wherein the historical decision label sequence includes at least one decision label corresponding to at least one historical frame, and each of the at least one decision label indicates whether the target obstacle in the corresponding historical frame is used to determine the driving behavior of the vehicle; and The determination module is configured to determine, based on the historical decision label sequence, whether the target obstacle is used to determine the current driving behavior of the vehicle, including: The determination unit is configured to determine that the target obstacle is not used to determine the current driving behavior of the vehicle in response to a ratio of a first quantity to a second quantity being less than a third threshold, wherein the first quantity is the number of decision labels with a value of "yes" among the at least one decision label, and the second quantity is the total number of the at least one decision label.

8. The apparatus according to claim 7, further comprising: The third acquisition module is configured to acquire the second position of the target obstacle in the previous frame in response to a first distance between the target obstacle and the vehicle being less than a second threshold and the current frame not being the first frame in which the target obstacle was detected; The judgment module further includes: The judgment unit is configured to determine whether the target obstacle is used to determine the current driving behavior of the vehicle based on the second distance between the first position and the second position and the historical decision label sequence.

9. The apparatus according to claim 7, further comprising: The first determination module is configured to determine that the target obstacle is not used to determine the current driving behavior of the vehicle in response to the first distance being less than the second threshold and the current frame being the first frame in which the target obstacle is detected.

10. The apparatus according to any one of claims 7-9, wherein, The obstacle detection result also includes the category to which the target obstacle belongs, and the device further includes: The second determination module is configured to determine the target obstacle in response to the category being non-collision-free to determine the current driving behavior of the vehicle.

11. The apparatus according to any one of claims 7-9, further comprising: The reporting module is configured to report the information of the target obstacle to the cloud server in response to determining that the target obstacle is not used to determine the current driving behavior of the vehicle.

12. The apparatus according to any one of claims 7-9, wherein, The second threshold is determined based on the vehicle's speed.

13. An electronic device, comprising: At least one processor; as well as A memory that is communicatively connected to the at least one processor; in The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

15. A computer program product comprising computer program instructions, wherein, The computer program instructions, when executed by a processor, implement the method of any one of claims 1-6.

16. An autonomous vehicle, including the electronic equipment as claimed in claim 13.

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