Driving control strategy determination method and device, equipment and storage medium

Abstract

The invention discloses a driving control strategy determination method, device and equipment and a storage medium, and belongs to the technical field of automatic driving, and the method comprises the steps: determining a target monitoring range of a target vehicle; performing track fitting processing on the historical driving track of each monitoring vehicle in the target monitoring range in a preset time period to obtain a fitting track curve of each monitoring vehicle in the target monitoring range; acquiring a target driving intention of the target vehicle at the intersection without the signal lamp; screening out a target following vehicle from the monitoring vehicles in the target monitoring range according to the target driving intention, the lane where the target vehicle is located and the monitoring vehicle information of each monitoring vehicle in the target monitoring range at the intersection without the signal lamp; and determining a driving control strategy of the target vehicle at the intersection without the signal lamp according to the path association relationship and the collision time between the target vehicle and the target following vehicle and the fitting trajectory curve of the target following vehicle.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to a method, apparatus, device, and storage medium for determining driving control strategies. Background Technology

[0002] Existing methods for vehicle passage at intersections without traffic lights mainly rely on onboard sensors (such as cameras, radar, and lidar) to perceive the status of surrounding vehicles. These sensors then use predictive algorithms to estimate the future trajectories of the vehicles, thereby determining the driving strategy for vehicles at intersections without traffic lights.

[0003] However, for vehicles with low sensor configurations and memory navigation functions, these vehicles are usually not equipped with LiDAR, but instead use cameras as the main sensors, supplemented by a small number of millimeter-wave radars. In the scenario of intersections without traffic lights, due to the susceptibility of onboard sensors to obstruction and weather conditions, these vehicles may experience incomplete perception or reduced prediction accuracy when perceiving the surrounding environment, thereby increasing the risk of collisions at intersections without traffic lights. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for determining driving control strategies, in order to reduce the risk of collisions at intersections without traffic lights and improve the safe passage efficiency of vehicles at intersections without traffic lights.

[0005] According to one aspect of the present invention, a method for determining a driving control strategy is provided, the method comprising:

[0006] Determine the target monitoring range for the target vehicle;

[0007] The historical driving trajectory of each monitored vehicle within the target monitoring range is subjected to trajectory fitting processing within a preset time period to obtain the fitted trajectory curve of each monitored vehicle within the target monitoring range.

[0008] Obtain the target vehicle's driving intention at an intersection without traffic lights;

[0009] Based on the target vehicle's driving intention, the lane the target vehicle is in, and the monitoring vehicle information of each monitored vehicle within the target monitoring range at intersections without traffic lights, the target following vehicle is selected from the monitored vehicles within the target monitoring range.

[0010] Based on the path relationship and collision time between the target vehicle and the following vehicle, as well as the fitted trajectory curve of the following vehicle, the driving control strategy of the target vehicle at the intersection without traffic lights is determined.

[0011] According to another aspect of the present invention, a vehicle control strategy determination apparatus is provided, the apparatus comprising:

[0012] The target monitoring range determination module is used to determine the target monitoring range of the target vehicle.

[0013] The trajectory curve fitting module is used to perform trajectory fitting processing on the historical driving trajectory of each monitored vehicle within the target monitoring range within a preset time period to obtain the fitted trajectory curve of each monitored vehicle within the target monitoring range.

[0014] The target driving intent acquisition module is used to acquire the target driving intent of a target vehicle at an intersection without traffic lights;

[0015] The target following vehicle filtering module is used to filter out target following vehicles from the monitored vehicles within the target monitoring range based on the target's driving intention, the lane where the target vehicle is located, and the monitoring vehicle information of each monitored vehicle within the target monitoring range at intersections without traffic lights.

[0016] The driving control strategy determination module is used to determine the driving control strategy of the target vehicle at an intersection without traffic lights based on the path relationship and collision time between the target vehicle and the target following vehicle, as well as the fitted trajectory curve of the target following vehicle.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor;

[0019] and a memory communicatively connected to at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the vehicle control strategy determination method according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the vehicle control strategy determination method of any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the vehicle control strategy determination method of any embodiment of the present invention.

[0023] The technical solution of this invention involves: determining the target monitoring range of the target vehicle; performing trajectory fitting processing on the historical driving trajectory of each monitored vehicle within the target monitoring range over a preset time period to obtain the fitted trajectory curve of each monitored vehicle within the target monitoring range; acquiring the target driving intention of the target vehicle at an intersection without traffic lights; selecting target following vehicles from the monitored vehicles within the target monitoring range based on the target driving intention, the lane where the target vehicle is located, and the monitoring vehicle information of each monitored vehicle within the target monitoring range at the intersection without traffic lights; and determining the driving control strategy of the target vehicle at the intersection without traffic lights based on the path association and collision time between the target vehicle and the target following vehicle, as well as the fitted trajectory curve of the target following vehicle. The above technical solution comprehensively analyzes the historical driving trajectory of each monitored vehicle within a preset time period and the monitoring vehicle information of each monitored vehicle at intersections without traffic lights, as well as the target vehicle's driving intention at intersections without traffic lights, to determine the driving control strategy for the target vehicle at intersections without traffic lights. This improves the accuracy of the driving control strategy for the target vehicle at intersections without traffic lights, thereby reducing the collision risk of the target vehicle at intersections without traffic lights and improving the safe passage efficiency of the target vehicle at intersections without traffic lights.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a method for determining a driving control strategy according to Embodiment 1 of the present invention;

[0027] Figure 2A This is a flowchart of a method for determining a driving control strategy according to Embodiment 2 of the present invention;

[0028] Figure 2B This is a schematic diagram of vehicle numbers of monitored vehicles within a target monitoring range according to Embodiment 2 of the present invention;

[0029] Figure 3 This is a schematic diagram of a vehicle control strategy determination device according to Embodiment 3 of the present invention;

[0030] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the vehicle control strategy determination method of this invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "target," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] Example 1

[0034] Figure 1 This is a flowchart of a driving control strategy determination method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where vehicles with low sensor configuration and memory navigation function safely pass through intersections without traffic lights. This method can be executed by a driving control strategy determination device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0035] S101. Determine the target monitoring range for the target vehicle.

[0036] The target vehicle refers to a vehicle that needs to pass through an intersection without traffic lights, and whose sensor configuration is low-end, but which has a memory navigation function. For example, the target vehicle may be a vehicle without lidar, using cameras as the primary sensor, supplemented by a small amount of millimeter-wave radar. The target monitoring range refers to the area within a preset distance around the target vehicle.

[0037] Specifically, the target monitoring range of a target vehicle can be determined based on the physical coverage of each onboard sensor.

[0038] S102. Perform trajectory fitting processing on the historical driving trajectory of each monitored vehicle within the target monitoring range within a preset time period to obtain the fitted trajectory curve of each monitored vehicle within the target monitoring range.

[0039] The monitored vehicle refers to any vehicle falling within the target monitoring range. The preset time period can be determined through real-vehicle road testing; for example, the preset time period can be any integer value between 5 and 15 seconds. Historical driving trajectories are systematic information recording the past driving paths, times, speeds, and related environmental data of the monitored vehicles. Optionally, historical driving trajectories include the vehicle's latitude and longitude coordinates, heading angle, speed, and turn signal information at each historical point in time within the preset time period. It should be noted that for each monitored vehicle within the target monitoring range, its historical driving trajectory within the preset time period can be obtained through V2X communication technology.

[0040] Specifically, for each monitored vehicle within the target monitoring range, its historical driving trajectory within a preset time period is filtered to obtain its optimized historical driving trajectory within that time period. A local coordinate system is established with the target vehicle as the origin, and the optimized historical driving trajectory is transformed to obtain the historical trajectory points of the monitored vehicle in the local coordinate system. Curve fitting is then performed on these historical trajectory points in the local coordinate system to obtain the fitted trajectory curve of the monitored vehicle. Here, the optimized historical driving trajectory refers to the driving trajectory obtained after filtering the historical driving trajectory. Historical trajectory points refer to the vehicle's position coordinates at a specific historical moment in the local coordinate system with the target vehicle as the origin.

[0041] More specifically, for each monitored vehicle within the target monitoring range, a preset filtering algorithm, such as the Kalman filter, is used to filter the historical driving trajectory of the monitored vehicle within a preset time period to eliminate noise and outliers in the historical driving trajectory, thereby obtaining the optimized historical driving trajectory of the monitored vehicle within the preset time period. A local coordinate system is established with the rear axle center point of the target vehicle as the origin, and the coordinates of the optimized historical driving trajectory of the monitored vehicle within the preset time period are transformed to obtain the historical trajectory points of the monitored vehicle in the local coordinate system. A preset curve fitting algorithm, such as the least squares method or spline interpolation method, is used to perform cubic curve fitting on the historical trajectory points of the monitored vehicle in the local coordinate system to obtain the fitted trajectory curve of the monitored vehicle.

[0042] S103. Obtain the target vehicle's driving intention at an intersection without traffic lights.

[0043] The target driving intention refers to the driving intention of the target vehicle at an intersection without traffic lights; optionally, the target driving intention can be left turn, straight ahead, right turn, or U-turn. Specifically, the target driving intention of the target vehicle at an intersection without traffic lights can be obtained based on the driver behavior data, current vehicle status, and current surrounding environment information, using a trained driving intention recognition model.

[0044] Optionally, to improve the accuracy of the target vehicle's driving intention, the historical driving trajectory of the target vehicle within a preset time period can be fitted to obtain the vehicle's fitted trajectory curve; based on the vehicle's fitted trajectory curve, the target vehicle's driving intention at the intersection without traffic lights can be determined.

[0045] S104. Based on the target's driving intention, the lane where the target vehicle is located, and the monitoring vehicle information of each monitored vehicle within the target's monitoring range at intersections without traffic lights, select the target following vehicle from the monitored vehicles within the target's monitoring range.

[0046] The monitored vehicle information includes the monitored vehicle's turn signal information, speed, and the relative distance between the monitored vehicle and the target vehicle. The target following vehicle refers to the vehicle selected from the monitored vehicles within the target's monitoring range that is most suitable for the target vehicle to follow.

[0047] Specifically, the target vehicle's driving intention, the lane where the target vehicle is located, and the monitoring information of each monitored vehicle within the target monitoring range at intersections without traffic lights are input into a pre-trained following vehicle screening model. After processing by the pre-trained following vehicle screening model, the target following vehicle is obtained.

[0048] S105. Based on the path relationship and collision time between the target vehicle and the target following vehicle, as well as the fitted trajectory curve of the target following vehicle, determine the driving control strategy of the target vehicle at the intersection without traffic lights.

[0049] The path association can be along the same path or different paths. Specifically, the collision time between the target vehicle and the following vehicle is matched with the collision time range in a preset collision risk level determination table, and the collision risk level corresponding to the successfully matched collision time range is determined as the collision risk level between the target vehicle and the following vehicle. The path association between the target vehicle and the following vehicle, the collision risk level, and the fitted trajectory curve of the following vehicle are input into a pre-trained driving control strategy generation model. After processing by the pre-trained driving control strategy generation model, the driving control strategy for the target vehicle at the intersection without traffic lights is obtained.

[0050] Optionally, after obtaining the collision risk level between the target vehicle and the target following vehicle, the driving control strategy for the target vehicle at an intersection without traffic lights can be determined based on the correspondence between path relationships, collision risk levels, dominant decisions, and key control strategy points in a preset driving control strategy table, using the path relationships and collision risk levels between the target vehicle and the target following vehicle as indexes. The preset driving control strategy table can be as follows:

[0051]

[0052] The technical solution of this invention involves: determining the target monitoring range of the target vehicle; performing trajectory fitting processing on the historical driving trajectory of each monitored vehicle within the target monitoring range over a preset time period to obtain the fitted trajectory curve of each monitored vehicle within the target monitoring range; acquiring the target driving intention of the target vehicle at an intersection without traffic lights; selecting target following vehicles from the monitored vehicles within the target monitoring range based on the target driving intention, the lane where the target vehicle is located, and the monitoring vehicle information of each monitored vehicle within the target monitoring range at the intersection without traffic lights; and determining the driving control strategy of the target vehicle at the intersection without traffic lights based on the path association and collision time between the target vehicle and the target following vehicle, as well as the fitted trajectory curve of the target following vehicle. The above technical solution comprehensively analyzes the historical driving trajectory of each monitored vehicle within a preset time period and the monitoring vehicle information of each monitored vehicle at intersections without traffic lights, as well as the target vehicle's driving intention at intersections without traffic lights, to determine the driving control strategy for the target vehicle at intersections without traffic lights. This improves the accuracy of the driving control strategy for the target vehicle at intersections without traffic lights, thereby reducing the collision risk of the target vehicle at intersections without traffic lights and improving the safe passage efficiency of the target vehicle at intersections without traffic lights.

[0053] Example 2

[0054] Figure 2A This is a flowchart of a driving control strategy determination method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment further optimizes the steps of "determining the target monitoring range of the target vehicle" and "selecting the target following vehicle from the monitoring vehicles within the target monitoring range based on the target driving intention, the lane where the target vehicle is located, and the monitoring vehicle information of each monitoring vehicle within the target monitoring range at an intersection without traffic lights," providing an optional implementation scheme. It should be noted that parts not detailed in this embodiment can be referred to in the relevant descriptions of other embodiments. For example... Figure 2A As shown, the method includes:

[0055] S201. Obtain the relative distance, relative speed, and relative acceleration between the target vehicle and the vehicle in front.

[0056] The vehicle in front refers to a vehicle in the same lane as the target vehicle, traveling in the same direction as the target vehicle, and traveling in front of the target vehicle. It should be noted that there may be more than one vehicle in front.

[0057] Specifically, the relative distance between the target vehicle and the vehicle in front can be obtained through millimeter-wave radar on the target vehicle; the relative speed between the target vehicle and the vehicle in front can be obtained through visual tracking algorithm on the target vehicle; and the relative acceleration between the target vehicle and the vehicle in front can be obtained through inertial measurement unit on the target vehicle.

[0058] S202. Determine the collision time between the target vehicle and the vehicle in front based on the relative distance, relative speed, and relative acceleration.

[0059] Specifically, based on relative distance, relative speed, and relative acceleration, and using a preset vehicle collision algorithm, the collision time between the target vehicle and the vehicle in front is determined.

[0060] S203. Determine the target monitoring range of the target vehicle based on the collision time between the target vehicle and the vehicle in front, as well as the lane width of the adjacent lane lines on the left and right sides of the target vehicle.

[0061] It should be noted that the target monitoring range includes both lateral and longitudinal monitoring ranges. Specifically, based on the collision time between the target vehicle and the vehicle ahead, the range where the collision time is less than or equal to a collision time threshold is defined as the longitudinal monitoring range. Based on the lane widths of the adjacent lane lines on both sides of the target vehicle, the range between the outer edges of the left and right adjacent lane lines is defined as the lateral monitoring range, thus obtaining the target monitoring range for the target vehicle. The collision time threshold can be preset according to actual business needs; for example, it could be 5 seconds. This embodiment of the invention does not impose a specific limitation on it. Optionally, the collision time threshold can also be dynamically adjusted based on the real-time speed of the target vehicle.

[0062] S204. Perform trajectory fitting processing on the historical driving trajectory of each monitored vehicle within the target monitoring range within a preset time period to obtain the fitted trajectory curve of each monitored vehicle within the target monitoring range.

[0063] Optionally, to facilitate the differentiation of monitoring vehicles within the target monitoring range, vehicle numbers can be assigned based on the relative positions of the monitoring vehicles within the target monitoring range and the target vehicle. See [link to relevant documentation]. Figure 2B . Figure 2B The diagram illustrates how to assign vehicle numbers to six vehicles within the target monitoring range based on their relative positions to the target vehicle within the target monitoring range.

[0064] S205. Obtain the target vehicle's driving intention at an intersection without traffic lights.

[0065] S206. Determine the conditions for following vehicles based on the target vehicle's driving intention and the lane in which the target vehicle is located.

[0066] Specifically, based on the target vehicle's driving intention and the lane in which the target vehicle is located, and combined with the expert experience of those skilled in the art, the conditions for following the vehicle are determined.

[0067] S207. Based on the conditions for vehicles that can be followed and the monitoring vehicle information of each monitored vehicle within the target monitoring range at intersections without traffic lights, select the target following vehicle from the monitored vehicles within the target monitoring range.

[0068] Specifically, the system can sequentially check whether there are any monitored vehicles within the target monitoring range that meet the criteria for being a followable vehicle at the intersection without traffic lights, based on the vehicle number of the monitored vehicles within the target monitoring range, from smallest to largest. If such a vehicle exists, it will be designated as the target followable vehicle. If not, there is no target followable vehicle, and the target vehicle will pass through the intersection without traffic lights based on the real-time perception data identified by its onboard sensors or the map drawn by the navigation assistance system.

[0069] S208. Based on the path relationship and collision time between the target vehicle and the target following vehicle, as well as the fitted trajectory curve of the target following vehicle, determine the driving control strategy of the target vehicle at the intersection without traffic lights.

[0070] The technical solution of this invention involves: acquiring the relative distance, relative speed, and relative acceleration between the target vehicle and the vehicle ahead; determining the collision time between the target vehicle and the vehicle ahead based on the relative distance, relative speed, and relative acceleration; determining the target monitoring range of the target vehicle based on the collision time between the target vehicle and the vehicle ahead, and the lane widths of the adjacent lane lines on the left and right sides of the target vehicle; performing trajectory fitting processing on the historical driving trajectory of each monitored vehicle within the target monitoring range over a preset time period to obtain the fitted trajectory curve of each monitored vehicle within the target monitoring range; acquiring the target driving intention of the target vehicle at an intersection without traffic lights; determining the conditions for followable vehicles based on the target driving intention and the lane where the target vehicle is located; selecting target followable vehicles from the monitored vehicles within the target monitoring range based on the followable vehicle conditions and the monitoring vehicle information of each monitored vehicle within the target monitoring range at the intersection without traffic lights; and determining the driving control strategy of the target vehicle at the intersection without traffic lights based on the path association and collision time between the target vehicle and the target followable vehicle, and the fitted trajectory curve of the target followable vehicle. The aforementioned technical solution determines the target monitoring range of the target vehicle based on the collision time between the target vehicle and the vehicle in front, as well as the lane width of the adjacent lane lines on the left and right sides of the target vehicle. This improves the accuracy of the target monitoring range, thereby enhancing the accuracy of the monitored vehicles within the target monitoring range. Secondly, based on the target vehicle's driving intention and the lane it is in, the conditions for following vehicles are first determined. Then, based on the conditions for following vehicles and the monitoring vehicle information of each monitored vehicle within the target monitoring range at the unsignalized intersection, the target following vehicle is selected from the monitored vehicles within the target monitoring range. By using strict conditions for following vehicles, the number of complex scenarios that the target vehicle needs to handle is reduced, the false positive rate is lowered, and the accuracy of the target following vehicle is improved. This, in turn, improves the accuracy of the target vehicle's driving control strategy at the unsignalized intersection, reduces the collision risk of the target vehicle at the unsignalized intersection, and improves the safe passage efficiency of the target vehicle at the unsignalized intersection.

[0071] Example 3

[0072] Figure 3 This is a schematic diagram of a driving control strategy determination device provided in Embodiment 3 of the present invention. This embodiment is applicable to situations where vehicles with low sensor configuration and memory navigation function safely pass through intersections without traffic lights. The device can be implemented in hardware and / or software and can be configured in electronic devices. Figure 3 As shown, the device includes:

[0073] The target monitoring range determination module 301 is used to determine the target monitoring range of the target vehicle.

[0074] The fitting trajectory curve determination module 302 is used to perform trajectory fitting processing on the historical driving trajectory of each monitored vehicle within the target monitoring range within a preset time period to obtain the fitting trajectory curve of each monitored vehicle within the target monitoring range.

[0075] The target driving intention acquisition module 303 is used to acquire the target driving intention of the target vehicle at an intersection without traffic lights.

[0076] The target following vehicle filtering module 304 is used to filter target following vehicles from the monitored vehicles within the target monitoring range based on the target's driving intention, the lane where the target vehicle is located, and the monitoring vehicle information of each monitored vehicle within the target monitoring range at intersections without traffic lights.

[0077] The driving control strategy determination module 305 is used to determine the driving control strategy of the target vehicle at an intersection without traffic lights based on the path association and collision time between the target vehicle and the target following vehicle, as well as the fitted trajectory curve of the target following vehicle.

[0078] The technical solution of this invention involves: determining the target monitoring range of the target vehicle; performing trajectory fitting processing on the historical driving trajectory of each monitored vehicle within the target monitoring range over a preset time period to obtain the fitted trajectory curve of each monitored vehicle within the target monitoring range; acquiring the target driving intention of the target vehicle at an intersection without traffic lights; selecting target following vehicles from the monitored vehicles within the target monitoring range based on the target driving intention, the lane where the target vehicle is located, and the monitoring vehicle information of each monitored vehicle within the target monitoring range at the intersection without traffic lights; and determining the driving control strategy of the target vehicle at the intersection without traffic lights based on the path association and collision time between the target vehicle and the target following vehicle, as well as the fitted trajectory curve of the target following vehicle. The above technical solution comprehensively analyzes the historical driving trajectory of each monitored vehicle within a preset time period and the monitoring vehicle information of each monitored vehicle at intersections without traffic lights, as well as the target vehicle's driving intention at intersections without traffic lights, to determine the driving control strategy for the target vehicle at intersections without traffic lights. This improves the accuracy of the driving control strategy for the target vehicle at intersections without traffic lights, thereby reducing the collision risk of the target vehicle at intersections without traffic lights and improving the safe passage efficiency of the target vehicle at intersections without traffic lights.

[0079] Optionally, the target monitoring range determination module 301 is specifically used for:

[0080] The relative distance, relative speed, and relative acceleration between the target vehicle and the vehicle in front are obtained; where the vehicle in front refers to a vehicle in the same lane as the target vehicle, traveling in the same direction as the target vehicle, and traveling in front of the target vehicle.

[0081] The collision time between the target vehicle and the vehicle in front is determined based on the relative distance, relative speed, and relative acceleration.

[0082] The target monitoring range of the target vehicle is determined based on the collision time between the target vehicle and the vehicle in front, as well as the lane width of the adjacent lane lines on the left and right sides of the target vehicle.

[0083] Optionally, the fitted trajectory curve determination module 302 is specifically used for:

[0084] For each monitored vehicle within the target monitoring range, the historical driving trajectory of the monitored vehicle within a preset time period is filtered to obtain the optimized historical driving trajectory of the monitored vehicle within the preset time period.

[0085] A local coordinate system is established with the target vehicle as the origin. The historical optimized driving trajectory of the monitored vehicle within a preset time period is transformed to obtain the historical trajectory points of the monitored vehicle in the local coordinate system.

[0086] Curve fitting is performed on the historical trajectory points of the monitored vehicle in the local coordinate system to obtain the fitted trajectory curve of the monitored vehicle.

[0087] Optionally, the target vehicle filtering module 304 is specifically used for:

[0088] Determine the conditions for following vehicles based on the target vehicle's driving intention and the lane it is in;

[0089] Based on the conditions for following vehicles and the monitoring vehicle information of each monitored vehicle within the target monitoring range at intersections without traffic lights, target following vehicles are selected from the monitored vehicles within the target monitoring range.

[0090] Optionally, the target driving intention is to turn left, go straight, turn right, or make a U-turn.

[0091] Optionally, the monitored vehicle information includes the monitored vehicle's turn signal information, speed, and relative distance between the monitored vehicle and the target vehicle.

[0092] The vehicle control strategy determination device provided in the embodiments of the present invention can execute the vehicle control strategy determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing each vehicle control strategy determination method.

[0093] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0094] Example 4

[0095] Figure 4A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, 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 processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), 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 invention described and / or claimed herein.

[0096] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0097] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0098] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 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 processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the driving control strategy determination method.

[0099] In some embodiments, the vehicle control strategy determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle control strategy determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the vehicle control strategy determination method by any other suitable means (e.g., by means of firmware).

[0100] 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), payload-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.

[0101] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0102] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. 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 fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0103] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. 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).

[0104] 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), blockchain networks, and the Internet.

[0105] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0106] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0107] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining a driving control strategy, characterized by, The method comprises the following steps: determining a target monitoring range of a target vehicle; performing trajectory fitting processing on historical driving trajectories of each monitoring vehicle in the target monitoring range within a preset time period to obtain a fitted trajectory curve of each monitoring vehicle in the target monitoring range; obtaining a target driving intention of the target vehicle at a signal-free intersection; screening a target following vehicle from the monitoring vehicles in the target monitoring range according to the target driving intention, a lane where the target vehicle is located, and monitoring vehicle information of each monitoring vehicle at the signal-free intersection in the target monitoring range; determining a driving control strategy of the target vehicle at the signal-free intersection according to a path correlation relationship and a collision time between the target vehicle and the target following vehicle, and the fitted trajectory curve of the target following vehicle.

2. The method of claim 1, wherein, The step of determining the target monitoring range of the target vehicle comprises the following steps: obtaining a relative distance, a relative speed and a relative acceleration between the target vehicle and a front vehicle; wherein the front vehicle refers to a vehicle which is in the same lane as the target vehicle, has the same driving direction as the target vehicle, and is located in front of the target vehicle; determining a collision time between the target vehicle and the front vehicle according to the relative distance, the relative speed and the relative acceleration; determining the target monitoring range of the target vehicle according to the collision time between the target vehicle and the front vehicle, and a lane width of adjacent lane lines on left and right sides of the target vehicle.

3. The method of claim 1, wherein, The step of performing trajectory fitting processing on the historical driving trajectories of each monitoring vehicle in the target monitoring range within the preset time period to obtain the fitted trajectory curve of each monitoring vehicle in the target monitoring range comprises the following steps: for each monitoring vehicle in the target monitoring range, performing filtering processing on historical driving trajectories of the monitoring vehicle within the preset time period to obtain historical optimized driving trajectories of the monitoring vehicle within the preset time period; establishing a local coordinate system with the target vehicle as a coordinate origin, and performing coordinate conversion on the historical optimized driving trajectories of the monitoring vehicle within the preset time period to obtain historical trajectory points of the monitoring vehicle in the local coordinate system; performing curve fitting on the historical trajectory points of the monitoring vehicle in the local coordinate system to obtain a fitted trajectory curve of the monitoring vehicle.

4. The method of claim 1, wherein, The step of screening the target following vehicle from the monitoring vehicles in the target monitoring range according to the target driving intention, the lane where the target vehicle is located, and the monitoring vehicle information of each monitoring vehicle at the signal-free intersection in the target monitoring range comprises the following steps: determining a followable vehicle condition according to the target driving intention and the lane where the target vehicle is located; screening the target following vehicle from the monitoring vehicles in the target monitoring range according to the followable vehicle condition and the monitoring vehicle information of each monitoring vehicle at the signal-free intersection in the target monitoring range.

5. The method of claim 1, wherein, The target driving intention is left turn, straight driving, right turn or U-turn.

6. The method of claim 1, wherein, The monitoring vehicle information comprises steering light information, a vehicle speed of the monitoring vehicle, and a relative distance between the monitoring vehicle and the target vehicle.

7. A driving control strategy determination device characterized by comprising: The method comprises the following steps: A target monitoring range determination module is configured to determine a target monitoring range of the target vehicle. A fitted trajectory curve determination module is configured to perform trajectory fitting processing on historical driving trajectories of each monitoring vehicle in the target monitoring range within a preset time period, to obtain a fitted trajectory curve of each monitoring vehicle in the target monitoring range. A target driving intention acquisition module is configured to acquire a target driving intention of the target vehicle at the intersection without traffic lights. A target following vehicle screening module is configured to screen a target following vehicle from the monitoring vehicles in the target monitoring range according to the target driving intention, a lane where the target vehicle is located, and monitoring vehicle information of each monitoring vehicle in the target monitoring range at the intersection without traffic lights. A driving control strategy determination module is configured to determine a driving control strategy of the target vehicle at the intersection without traffic lights according to a path correlation relationship and a collision time between the target vehicle and the target following vehicle, and the fitted trajectory curve of the target following vehicle.

8. An electronic device, comprising: The electronic device includes: at least one processor; and a memory connected in communication with the at least one processor; wherein The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the driving control strategy determination method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the driving control strategy determination method of any one of claims 1-6 when executed.

10. A computer program product comprising a computer program that, when executed by a processor, implements the driving control strategy determination method of any one of claims 1-6.

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