Vehicle driving control method and device

Abstract

The invention provides a vehicle driving control method and device. The method comprises the following steps: acquiring vehicle sensing information; determining the current lane of the vehicle according to the vehicle sensing information; determining a target lane after the vehicle passes through the intersection according to the navigation map information and the vehicle sensing information; and performing driving control on the vehicle according to the current lane of the vehicle and the target lane. The method is used for achieving the effect that the standard navigation map replaces a high-precision map to assist the intelligent driving vehicle in safely passing through the intersection.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a vehicle driving control method and device. Background Art

[0002] Intelligent driving vehicles integrate key technologies such as advanced sensor technology, data processing capabilities, artificial intelligence algorithms, and high-precision maps to achieve autonomous navigation and driving in different complex road environments.

[0003] In related intelligent driving technologies, vehicles passing through intersections rely on the close collaboration of high-precision maps and multi-sensor fusion technology. Specifically, the intelligent driving system collects intersection environmental information in real time, and then uses high-precision map information to accurately match the perceived environmental information with the road model in the high-precision map, thereby achieving high-precision fusion positioning of the vehicle on the high-precision map. Based on this positioning result, the intelligent driving system comprehensively plans the path information for the vehicle to pass through the intersection based on the lane-level navigation information provided by the high-precision map, combined with traffic rules, vehicle status and predicted behaviors of other traffic participants. Finally, the vehicle is accurately controlled to pass through the intersection according to the planned path.

[0004] However, without a high-precision map, it is impossible to safely pass the intersection using the above method. Summary of the invention

[0005] The present application provides a vehicle driving control method and device to solve the problem of reliance on high-precision maps for intelligent driving through intersections in the prior art.

[0006] In a first aspect, the present application provides a vehicle driving control method, comprising:

[0007] Obtain vehicle perception information;

[0008] Determine the current lane of the vehicle based on the vehicle perception information;

[0009] Determine the target lane after the vehicle passes the intersection based on the navigation map information and vehicle perception information;

[0010] The vehicle is controlled according to its current lane and target lane.

[0011] In some embodiments, determining a target lane after the vehicle passes through an intersection based on navigation map information and vehicle perception information includes:

[0012] Determine, based on the global navigation path information in the navigation map information, a first angle between a road section that the vehicle is about to exit and a road section that the vehicle is about to enter;

[0013] Determining a target road corresponding to the road section to be entered in the vehicle perception information according to the first angle;

[0014] According to the navigation guidance prompt information in the navigation map information, the target lane after the vehicle enters the target road is determined.

[0015] In some embodiments, determining, based on the first angle, a target road corresponding to the road section to be entered in the vehicle perception information includes:

[0016] Determine the second angle between the multiple roads at the intersection and the lane where the vehicle is currently located based on the vehicle perception information;

[0017] According to the first angle and the second angle, a target road corresponding to the road section to be entered is determined in the vehicle perception information.

[0018] In some embodiments, determining a target lane after a vehicle enters a target road according to navigation guidance prompt information in the navigation map information includes:

[0019] When the navigation guidance prompt information includes a recommended lane, determining the recommended lane as the target lane;

[0020] When the navigation guidance prompt information does not include a recommended lane, determining at least one passable lane based on the navigation guidance prompt information and the vehicle perception information;

[0021] According to the navigation guidance prompt information of the next intersection, a target lane is determined from at least one passable lane.

[0022] In some embodiments, the vehicle is controlled according to the current lane and the target lane of the vehicle, including:

[0023] According to the navigation map information, predict the first distance between the vehicle and the intersection;

[0024] Predicting a second distance between the vehicle and the intersection based on the road topology in the vehicle perception information;

[0025] Determining the distance between the vehicle and the intersection in the first distance and the second distance according to whether there is an intersection in the road topology;

[0026] The vehicle is controlled based on the vehicle's current lane, target lane, and the distance between the vehicle and the intersection.

[0027] In some embodiments, according to whether there is an intersection in the road topology, determining the distance between the vehicle and the intersection in the first distance and the second distance includes:

[0028] When there is no intersection in the road topology, the first distance is determined to be the distance between the vehicle and the intersection;

[0029] When there is an intersection in the road topology, the absolute value of the difference between the first distance and the second distance is determined; when the absolute value of the difference is less than the second distance threshold, the second distance is determined to be the distance between the vehicle and the intersection; when the absolute value of the difference is greater than or equal to the second distance threshold, the first distance is determined to be the distance between the vehicle and the intersection.

[0030] In some embodiments, obtaining vehicle perception information includes:

[0031] Obtain camera image data and radar point cloud data;

[0032] Determine vehicle perception information based on camera image data and radar point cloud data.

[0033] In a second aspect, the present application provides a vehicle driving control device, comprising:

[0034] An acquisition module, used to acquire vehicle perception information;

[0035] A determination module, used to determine the current lane of the vehicle based on the vehicle perception information;

[0036] The determination module is also used to determine the target lane after the vehicle passes through the intersection based on the navigation map information and the vehicle perception information;

[0037] The control module is used to control the vehicle's driving according to the vehicle's current lane and target lane.

[0038] In a third aspect, the present application provides an intelligent driving system, including a map module, a perception module, a planning module and a control module.

[0039] Map module, used to obtain navigation map information from the standard map software development kit;

[0040] A perception module is used to determine vehicle perception information based on camera image data and radar point cloud data;

[0041] A planning module is used to generate intersection traffic path planning information based on navigation map information and vehicle perception information;

[0042] The control module is used to control the vehicle to pass through the intersection according to the intersection traffic path planning information.

[0043] In a fourth aspect, the present application provides an electronic device, including: a memory and a processor;

[0044] The memory is used to store computer programs; the processor is used to execute the computer programs stored in the memory to implement the vehicle driving control method in the first aspect and any one of the embodiments of the first aspect.

[0045] In a fifth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the vehicle driving control method in the first aspect and any one of the embodiments of the first aspect is implemented.

[0046] In a sixth aspect, the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the vehicle driving control method in the first aspect and any one of the embodiments of the first aspect.

[0047] The vehicle driving control method and device provided in the present application determine the current lane of the vehicle based on the vehicle perception information, and determine the target lane of the vehicle after passing the intersection by combining the vehicle perception information and the road-level navigation map information, and then control the vehicle driving based on the current lane of the vehicle and the target lane, thereby achieving the effect of using a standard navigation map instead of a high-precision map to assist intelligent driving vehicles to safely pass through intersections, avoiding dependence on high-precision maps. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 It is a structural schematic diagram of an intelligent driving system in the prior art;

[0050] Figure 2 A schematic diagram of a vehicle driving scenario provided in an embodiment of the present application;

[0051] Figure 3 A flow chart of a vehicle driving control method provided in one embodiment of the present application;

[0052] Figure 4 A schematic diagram of a scene of a vehicle passing through an intersection provided in an embodiment of the present application;

[0053] Figure 5 A schematic diagram of the distance between a vehicle and an intersection provided in an embodiment of the present application;

[0054] Figure 6 A schematic diagram of the structure of a vehicle driving control device provided in one embodiment of the present application;

[0055] Figure 7 A schematic diagram of the structure of an intelligent driving system provided in one embodiment of the present application;

[0056] Figure 8 A schematic diagram of the structure of another intelligent driving system provided in one embodiment of the present application;

[0057] Fig. 9 A schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances. For example, without departing from the scope of this document, first information can also be referred to as second information, and similarly, second information can also be referred to as first information.

[0060] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0061] Terminology explanation:

[0062] Car computer: The abbreviation of the in-vehicle infotainment product installed in the vehicle. The car computer can functionally realize information communication between people and vehicles, and between vehicles and the outside world (cars and cars).

[0063] Transformer: A neural network model based on attention mechanism.

[0064] SOME / IP: A service-oriented communication protocol, the full name of which is Scalable service-Oriented Middleware over IP.

[0065] Intelligent driving technology is one of the core cutting-edge technologies in the current automotive field. Intelligent driving vehicles integrate advanced sensor technology, data processing capabilities, artificial intelligence algorithms, high-precision maps and other key technologies to achieve autonomous navigation and driving in different complex road environments. Among them, high-precision maps, as an important part of the intelligent driving system, provide vehicles with detailed road geometry information, traffic rules, traffic signs, obstacle distribution and other key data, which is the key basis for achieving accurate vehicle positioning, path planning and intelligent decision-making.

[0066] like Figure 1 As shown, in the related technology, the intelligent driving system includes a map module, a perception module, a positioning module, a planning module and a control module. Among them, the map module integrates a high-precision map software development kit (SDK), which is used to provide high-precision map data of a preset range before and after the vehicle according to the latitude and longitude of the vehicle and the navigation path in the positioning information. The perception module is used to receive sensor data such as cameras, radars, and lidars to achieve target detection (such as mobile devices, pedestrians, non-motor vehicles, etc.), semantic segmentation (obstacles, ground markings, light poles, signboards, passable spaces, etc.), lane line fitting, perception of obstacles and lane line relationships, target fusion and other tasks and output real-time perception results. The positioning module achieves high-precision fusion positioning by matching the lane line and pole sign information in the perception results with the lane line and pole sign information in the high-precision map, and completes the projection of the vehicle position on the high-precision map. The planning module is used to complete the selection of lanes and target lanes through the intersection based on high-precision fusion positioning information, high-precision map lane-level navigation data and chassis information, and at the same time perform obstacle avoidance planning in combination with real-time perception results. The control module outputs control instructions based on the planning results combined with vehicle status information including chassis information obtained through the vehicle bus, thereby controlling the vehicle to pass through the intersection. In other words, the premise of using high-precision maps to control vehicles through intersections is that the high-precision maps contain complete road topology, lane topology, and lane selection guidance information, such as the connection relationship between roads, road boundaries, lanes, and the position relationship between vehicles and lane lines. The intelligent driving system locates the vehicle on the high-precision map through fusion positioning, and then completes the intersection according to the guidance of the high-precision map.

[0067] However, the above-mentioned intersection passing method relies on high-precision maps for positioning and intersection passing decisions, and the coverage of high-precision maps is limited. Especially in remote areas, newly built roads or rapidly changing urban areas, the lack or lag of high-precision maps seriously affects the availability and reliability of intelligent driving systems. In addition, the update frequency of high-precision maps is difficult to meet the needs of rapidly changing road environments, resulting in limited functions of intelligent driving systems, and the high cost of high-precision maps also increases the cost of intelligent driving systems.

[0068] In response to the above problems, the present application proposes a vehicle driving control method and device. In this method, the current lane of the vehicle is determined based on the vehicle perception information, and the target lane of the vehicle after passing the intersection is determined by combining the vehicle perception information and the road-level navigation map information. According to the current lane of the vehicle and the target lane, the path planning and control strategy of the vehicle passing through the intersection are generated, and then the vehicle is controlled to pass through the safe intersection, which achieves the effect of lane-level navigation by replacing the high-precision map with the standard map, eliminates the dependence of the intelligent driving system on the high-precision map, and reduces the overall cost of the intelligent driving system.

[0069] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0070] Figure 2 FIG. 1 shows a schematic diagram of a vehicle driving scene provided by an embodiment of the present application. Figure 2 As shown in the figure, the black triangle represents an intelligent driving vehicle. When the vehicle drives to point A, it senses its current lane and determines the target lane after the vehicle passes through the intersection based on the navigation map information provided by the standard map. Then, the vehicle's driving path and control strategy from point A to point B are planned to control the vehicle to pass through the intersection safely.

[0071] In this application, the electronic device is used as the execution subject to execute the vehicle driving control method of the following embodiment. Specifically, the execution subject can be a hardware device of the electronic device, or a software application that implements the following embodiment in the electronic device, or a computer-readable storage medium installed with the software application that implements the following embodiment, or a code that implements the software application of the following embodiment. The electronic device can be a vehicle terminal or an intelligent driving system installed in a vehicle.

[0072] Figure 3 FIG. 1 is a flow chart of a vehicle driving control method provided by an embodiment of the present application. Figure 3 As shown, with the electronic device as the execution subject, the method of this embodiment may include the following steps:

[0073] S101. Acquire vehicle perception information.

[0074] In this embodiment, the electronic device can capture the environmental data around the vehicle in real time through its integrated sensor system, including information such as the location, speed, shape and road signs of obstacles around the vehicle.

[0075] S102. Determine the current lane of the vehicle based on the vehicle perception information.

[0076] In this embodiment, the electronic device can use image processing and machine learning algorithms to process and analyze sensor data to accurately identify and understand the traffic environment the vehicle is currently in. Through image recognition technology, the electronic device can identify key road elements such as lane lines, traffic signs, and signal lights. Based on the processing and analysis results of the vehicle's perception information, the electronic device can accurately determine the lane the vehicle is currently in.

[0077] S103: Determine the target lane for the vehicle after it passes the intersection based on the navigation map information and the vehicle perception information.

[0078] In this embodiment, the navigation map information is provided by a standard navigation map, including global navigation path information at the road level. The electronic device can determine the target road after the vehicle passes the intersection based on the global navigation path information in the navigation map information, and can determine the target lane from multiple lanes of the target road in combination with the vehicle perception information.

[0079] S104: Control the vehicle according to the current lane and the target lane.

[0080] In this embodiment, after the electronic device determines the current lane and the target lane of the vehicle, it can use path planning and decision-making algorithms to plan the optimal driving path and control strategy based on navigation map information, navigation positioning information and perception information, and then control the vehicle to pass through the intersection.

[0081] The navigation positioning information is provided by the standard navigation map. The electronic device determines the position of the vehicle in the global coordinate system map based on the navigation positioning information and the perception information, that is, generates global positioning information, and constructs a local coordinate system based on the navigation map information and the perception information, with the current position of the vehicle as the origin. The inertial measurement unit information, wheel speed information and other vehicle driving status information are integrated based on the Kalman filter, and the vehicle posture is solved to obtain continuous and stable local positioning information.

[0082] The electronic device plans the path for the vehicle to pass through the intersection based on the global positioning information and local positioning information. According to the planned path and vehicle status, the corresponding lateral and longitudinal control signals and body control signals are calculated and sent to the chassis, body, power and steering controllers through the vehicle bus to control the vehicle. Among them, the lateral control signal includes the steering signal; the longitudinal control signal includes the drive signal, the brake signal and the gear signal; the body control signal includes the turn signal and the hazard warning light, etc.

[0083] In addition, when the electronic device obtains navigation map information and navigation positioning information, it can convert the navigation map information and navigation positioning information into a standard map data format for use. On the one hand, the conversion of the data format can isolate the impact of changes in the external map data format on the navigation planning and positioning inside the electronic device. For example, changes in the external map data format caused by navigation map version upgrades, changes in navigation map providers, etc. On the other hand, the internal data format of the electronic device is convenient for use by various modules inside the electronic device, and is convenient for quick retrieval of key data information.

[0084] The vehicle driving control method provided in this embodiment determines the current lane of the vehicle through vehicle perception information when the vehicle needs to pass through an intersection, determines the target lane after the vehicle passes through the intersection through navigation information provided by the standard map and vehicle perception information, and then plans the trajectory and control strategy of the vehicle through the intersection based on the current lane and the target lane. In this embodiment, the standard map is used in combination with the vehicle perception information for lane-level navigation, achieving the effect of lane-level navigation using the standard map, avoiding the dependence of intelligent driving vehicles on high-precision maps.

[0085] Optionally, a specific implementation of step S101 includes the following steps:

[0086] S1011. Obtain camera image data and radar point cloud data.

[0087] S1012. Determine vehicle perception information based on camera image data and radar point cloud data.

[0088] In this embodiment, the camera and the radar are both deployed on the vehicle to obtain information about the vehicle's surrounding environment. The electronic device can extract feature data from the camera image data and convert the feature data into an image under a bird's-eye view, extract geometric features from the radar point cloud data and project the geometric features into a height map under a bird's-eye view. The image under the bird's-eye view and the height map under the bird's-eye view are fused to form a bird's-eye view of the vehicle's surrounding environment.

[0089] The self-attention mechanism of the Transformer model is used to extract and fuse features of multimodal data in the bird's-eye view space. The Transformer can capture long-range dependencies in the input sequence, thereby modeling in space and time to improve the accuracy and robustness of perception.

[0090] Through the multi-layer Transformer encoder-decoder structure, the image in the bird's-eye view space is feature extracted and context information is integrated to output the fused feature map.

[0091] The feature map output by the Transformer is further converted into an occupancy probability map in three-dimensional space through the occupancy network. The occupancy probability map is projected in 3D to generate perception information of the road environment around the vehicle. The perception information includes the precise position and shape information of elements such as 3D road topology, moving targets, and obstacles. The 3D road topology can include road edge lines, lanes, lane lines, lane center lines, intersections, road entrance lines, stop lines, crosswalks, road arrows, traffic lights, traffic signs, etc. Moving targets include vehicles, non-motor vehicles, and pedestrians.

[0092] In some embodiments, the specific implementation of step S103 includes the following steps:

[0093] S201. Determine, based on global navigation path information in navigation map information, a first angle between a road section that a vehicle is about to exit and a road section that a vehicle is about to enter.

[0094] S202. Determine, based on the first angle, a target road corresponding to the road section to be entered in the vehicle perception information.

[0095] S203: Determine a target lane after the vehicle enters the target road according to the navigation guidance prompt information in the navigation map information.

[0096] Specifically, the electronic device calculates the first angle between the road section to be exited and the road section to be entered according to the shape point of the road section to be exited and the shape point of the road section to be entered in the global navigation path information, for example Figure 4 The angle between the line formed by shape point 1 and shape point 2 and the line formed by shape point 2 and shape point 3. It should be understood that the first angle is the angle between the road the vehicle is currently on and the target road. Based on the first angle, the electronic device can determine the target road that matches the first angle in the vehicle perception information.

[0097] like Figure 4 As shown, the vehicle drives to point C, and the global navigation path information indicates that it is about to turn right. There are two intersections near point C. In order to ensure that the vehicle turns right into the right-turn road indicated by the global navigation path information, the electronic device determines the first angle based on the global navigation path information, and then determines the target road that matches the first angle in the vehicle perception information. For example, the first angle is 90 degrees, and the angle between the first fork in the perception information and the road the vehicle is currently on is also 90 degrees, so it can be determined that the first fork is the right-turn road indicated by the global navigation path information.

[0098] Optionally, the specific implementation of step S202 includes the following steps:

[0099] S2021. Determine a second angle between multiple roads at the intersection and the lane in which the vehicle is currently located based on the vehicle perception information.

[0100] S2022. Determine, based on the first angle and the second angle, a target road corresponding to the road section to be entered in the vehicle perception information.

[0101] Specifically, the electronic device may determine the second angle between the plurality of roads at the intersection and the lane where the vehicle is currently located in the perception information, for example, Figure 4 The second angle between the first fork road and the lane the vehicle is currently in is 90 degrees, and the second angle between the second fork road and the lane the vehicle is currently in is 40 degrees (which can also be described as 140 degrees).

[0102] The plurality of second angles are compared with the first angle respectively, and the road corresponding to the second angle whose absolute value of the difference with the first angle is less than a preset threshold is the target road.

[0103] In this embodiment, for intersections with multiple roads, the target road for the vehicle after passing through the intersection is determined based on the first angle between the exit section and the entry section in the global navigation path information and the second angle between the current road and the remaining roads in the vehicle perception information. This can be compatible with the accuracy deviation problem of the standard map, improve the accuracy of the vehicle entering the road prompted by the global navigation path information, and improve the accuracy of using the standard map to guide the vehicle through the intersection at the lane level.

[0104] Optionally, the specific implementation of step S203 includes the following steps:

[0105] S2031: When the navigation guidance prompt information includes a recommended lane, determine the recommended lane as the target lane.

[0106] S2032: When the navigation guidance prompt information does not include a recommended lane, determine at least one drivable lane based on the navigation guidance prompt information and the vehicle perception information.

[0107] S2033: Determine a target lane from at least one drivable lane according to the navigation guidance prompt information of the next intersection.

[0108] Specifically, when the navigation guidance prompt information does not include a recommended lane, the electronic device can determine the target lane based on the guidance prompt information of the two guidance points ahead and the intersection lane information, thereby achieving the effect of the vehicle passing through the current intersection while efficiently passing through the next intersection.

[0109] For example, the navigation guidance prompt information is "turn left at the current intersection, turn right at the intersection after 800 meters", where there are two guidance points, namely "current intersection" and "intersection after 800 meters". In this case, the electronic device determines at least one left turn lane based on "turn left at the current intersection" and the number of left turn lanes at the current intersection.

[0110] When there is only one left-turn lane, the left-turn lane is determined as the target lane. When there are multiple left-turn lanes, the electronic device selects the right lane among the multiple left-turn lanes as the target lane based on "turn right at the intersection after 800 meters".

[0111] It can be understood that when there are multiple passable lanes, when the second guidance information is a right turn, the rightmost passable lane is determined as the target lane; when the second guidance is a left turn or a U-turn, the leftmost passable lane is determined as the target lane; when the second guidance is straight ahead, if the passable lanes are odd-numbered, the middlemost passable lane is determined as the target lane; if the passable lanes are even-numbered, the passable lane with less traffic in the two middle lanes is determined as the target lane.

[0112] In this embodiment, when there are multiple passable lanes for the vehicle to pass through the current intersection, the target lane is determined according to the guidance action information of the two guidance points, so that the vehicle can pass through the current intersection smoothly and pass through the next intersection more efficiently.

[0113] In some embodiments, the specific implementation of step S104 includes the following steps:

[0114] S301. Predict a first distance between the vehicle and the intersection based on navigation map information.

[0115] Specifically, the electronic device may determine the first distance between the vehicle and the intersection through navigation map information of the standard map.

[0116] The navigation map information may include global navigation path information and navigation guidance prompt information. The global navigation path information includes the distance between the current position of the vehicle and the intersection ahead. For example, on the display page of the global navigation path information, a bubble pops up at the intersection and displays "1 km", indicating that the distance between the current position of the vehicle and the intersection is 1 km.

[0117] The navigation guidance prompt information can be in text form or in voice broadcast form. For example, displaying on the vehicle display screen or voice broadcasting "turn left at the intersection 800 meters ahead" can determine that the distance between the vehicle and the intersection is 800 meters.

[0118] In addition, the electronic device can also determine the first distance between the vehicle and the intersection based on the longitude and latitude of the vehicle in the navigation positioning information and the longitude and latitude information of the intersection.

[0119] S302: predict a second distance between the vehicle and the intersection based on the road topology in the vehicle perception information.

[0120] Among them, the perception information includes road topology. According to the road topology, the electronic device can predict the distance to the vehicle obstacles or other traffic participants. If there is an intersection within the preset range of the vehicle, the electronic device can also predict the second distance between the vehicle and the intersection.

[0121] S303: Determine the distance between the vehicle and the intersection from the first distance and the second distance according to whether there is an intersection in the road topology.

[0122] Since the accuracy of navigation map information of standard maps is relatively lower than that of high-precision maps, necessary calibration is required for the first distance determined by the navigation map information to avoid potential safety hazards to vehicle driving due to low accuracy.

[0123] S304: Control the vehicle according to the current lane of the vehicle, the target lane, and the distance between the vehicle and the intersection.

[0124] Specifically, the electronic device can plan the vehicle's driving path from the current position to the target lane and the control strategy corresponding to the planned path based on the vehicle's current lane, the target lane and the distance between the vehicle and the intersection, and then control the vehicle to drive to the target road along the planned path according to the control strategy to complete the intersection passage process.

[0125] When the distance between the vehicle and the intersection is greater than the distance threshold, the electronic device controls the vehicle to drive in the current lane or to plan a lane change. When the distance between the vehicle and the intersection is less than or equal to the distance threshold, the electronic device plans the intersection passage path.

[0126] like Figure 5 As shown, the first distance is the distance between the vehicle and the intersection predicted based on the navigation map information or the navigation positioning information, and the second distance is the distance between the vehicle and the intersection predicted in the perception information. The first distance may be greater than the second distance, or may be less than the second distance. This embodiment determines the distance between the vehicle and the intersection based on whether there is an intersection in the perception information and the absolute value of the difference between the first distance and the second distance.

[0127] Optionally, a specific implementation of step S303 includes the following steps:

[0128] S3031: When there is no intersection in the road topology, determine the first distance as the distance between the vehicle and the intersection.

[0129] S3032. When there is an intersection in the road topology, determine the absolute value of the difference between the first distance and the second distance; when the absolute value of the difference is less than the second distance threshold, determine the second distance as the distance from the vehicle to the intersection; when the absolute value of the difference is greater than or equal to the second distance threshold, determine the first distance as the distance from the vehicle to the intersection.

[0130] Specifically, when the absolute value of the difference is greater than or equal to the second distance threshold, in order to prevent erroneous correction, the electronic device does not perform correction processing and determines the first distance as the distance between the vehicle and the intersection.

[0131] In this embodiment, the electronic device uses the navigation guidance prompt information or navigation positioning information in the navigation map information provided by the standard map as the data source, and performs necessary correction processing on the distance of the vehicle from the intersection in combination with the road topology information in the perception information, so as to facilitate the electronic device to use more accurate navigation map information when performing navigation planning, and reduce the risk of missing the intersection due to the low positioning accuracy of the standard map.

[0132] Figure 6 FIG. 1 shows a schematic diagram of the structure of a vehicle driving control device provided by an embodiment of the present application. Figure 6 As shown, the vehicle driving control device 10 of this embodiment is used to implement the operation corresponding to the electronic device in any of the above method embodiments. The vehicle driving control device 10 of this embodiment includes:

[0133] An acquisition module 11 is used to acquire vehicle perception information;

[0134] A determination module 12, used to determine the current lane of the vehicle based on the vehicle perception information;

[0135] The determination module 12 is further used to determine the target lane after the vehicle passes through the intersection based on the navigation map information and the vehicle perception information;

[0136] The control module 13 is used to control the vehicle's driving according to the vehicle's current lane and the target lane.

[0137] The vehicle driving control device 10 provided in the embodiment of the present application can execute the above method embodiment. Its specific implementation principle and technical effects can be found in the above method embodiment, and this embodiment will not be repeated here.

[0138] Figure 7 This is a schematic diagram of the structure of an intelligent driving system provided in one embodiment of the present application. Figure 7 As shown, the intelligent driving system provided in this embodiment includes a map module 21, a perception module 22, a positioning module 23, a planning module 24 and a control module 25.

[0139] A map module 21, used to obtain navigation map information and navigation positioning information from a standard map software development kit (SDK);

[0140] A perception module 22, for determining vehicle perception information based on camera image data and radar point cloud data;

[0141] Positioning module 23, used for navigation map information, navigation positioning information and perception information, generating global positioning information and local positioning information;

[0142] The planning module 24 is used to generate intersection traffic path planning information based on the navigation map information, the global positioning information, the local positioning information and the vehicle perception information;

[0143] The control module 25 is used to control the vehicle to pass through the intersection according to the intersection passage path planning information.

[0144] It should be noted that in this embodiment, the standard map SDK can be integrated into the built-in navigation map of the vehicle, which provides navigation map information and navigation positioning information, thereby reusing the existing resources of the vehicle to reduce the cost of each vehicle and broaden the operating range of the intelligent driving system.

[0145] Specifically, the vehicle computer can send navigation map information and navigation positioning information to the intelligent driving system via Ethernet, and the modules of the intelligent driving system can communicate with each other using SOME / IP.

[0146] like Figure 8 As shown, the standard map SDK can also be integrated into the map module of the intelligent driving system. The map module obtains navigation map information and navigation positioning information from the standard map SDK through an interface without directly communicating with the vehicle computer, thereby solving the problem of direct dependence of the intelligent driving system on the vehicle computer.

[0147] In addition, the map provider's standard map SDK communicates with the navigation map SDK in the car computer via Ethernet to synchronize necessary information, such as the navigation route selected by the user on the car computer, the car computer navigation location information and other basic navigation information. The standard map SDK combines local map data and cloud map data to provide the map module with all the map information required for intelligent driving.

[0148] The intelligent driving system provided in this embodiment can execute the above method embodiments. Its specific implementation principles and technical effects can be found in the above method embodiments, and this embodiment will not be repeated here.

[0149] Fig. 9 FIG. 1 shows a hardware structure diagram of an electronic device provided in an embodiment of the present application. Fig. 9 As shown, the electronic device 30 is used to implement the operations corresponding to the electronic device in any of the above method embodiments. The electronic device 30 of this embodiment may include: a memory 31, a processor 32 and a communication interface 34.

[0150] The memory 31 is used to store computer programs. The memory 31 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a disk, or an optical disk.

[0151] The processor 32 is used to execute the computer program stored in the memory to implement the method in the above embodiment. For details, please refer to the relevant description in the above method embodiment. The processor 32 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the invention can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor.

[0152] Optionally, the memory 31 may be independent or integrated with the processor 32 .

[0153] When the memory 31 is a device independent of the processor 32, the electronic device 30 may further include a bus 33. The bus 33 is used to connect the memory 31 and the processor 32. The bus 33 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0154] The communication interface 34 may be connected to the processor 32 via the bus 33. The processor 32 may control the communication interface 34 to implement the functions of receiving and sending signals.

[0155] The electronic device 30 provided in this embodiment can be used to execute the above-mentioned vehicle driving control method. Its implementation method and technical effect are similar, and this embodiment will not be repeated here.

[0156] The present application also provides a computer-readable storage medium, in which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, it is used to implement the methods provided in the various embodiments described above.

[0157] Among them, the computer-readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that is convenient for transmitting a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a computer-readable storage medium is coupled to a processor so that the processor can read information from the computer-readable storage medium and write information to the computer-readable storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can be located in an application-specific integrated circuit (ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the computer-readable storage medium can also exist in a communication device as discrete components.

[0158] Specifically, the computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The storage medium may be any available medium that can be accessed by a general or special-purpose computer.

[0159] The present application also provides a computer program product, which includes a computer program / instruction, and the computer program / instruction is stored in a computer-readable storage medium. At least one processor of the device can read the computer program / instruction from the computer-readable storage medium, and at least one processor executes the computer program / instruction so that the device implements the methods provided in the various embodiments described above.

[0160] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0161] Among them, each module can be physically separated, for example, installed in different locations of a device, or installed on different devices, or distributed on multiple network units, or distributed on multiple processors. Each module can also be integrated together, for example, installed in the same device, or integrated in a set of codes. Each module can exist in the form of hardware, or can also exist in the form of software, or can also be implemented in the form of software plus hardware. The present application can select some or all of the modules according to actual needs to achieve the purpose of the present embodiment.

[0162] It should be understood that, although the various steps in the flowcharts in the above-described embodiments are sequentially displayed according to the indications of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and they can be executed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily to be carried out sequentially, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the above embodiments can still be modified, or some or all of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A vehicle driving control method, characterized in that: The method comprises: Obtain vehicle perception information; Determining a current lane of the vehicle based on the vehicle perception information; Determining a target lane after the vehicle passes through the intersection based on the navigation map information and the vehicle perception information; The vehicle is controlled according to the current lane of the vehicle and the target lane.

2. The method according to claim 1, characterized in that: Determining a target lane after the vehicle passes through an intersection according to the navigation map information and the vehicle perception information, including: Determine, based on the global navigation path information in the navigation map information, a first angle between a road section that the vehicle is about to exit and a road section that the vehicle is about to enter; Determining, according to the first angle, a target road corresponding to the road section to be entered in the vehicle perception information; According to the navigation guidance prompt information in the navigation map information, a target lane is determined after the vehicle enters the target road.

3. The method according to claim 2, characterized in that Determining, according to the first angle, a target road corresponding to the road section to be entered in the vehicle perception information, includes: Determining, based on the vehicle perception information, a second angle between the plurality of roads at the intersection and the lane in which the vehicle is currently located; According to the first angle and the second angle, a target road corresponding to the road section to be entered is determined in the vehicle perception information.

4. The method according to claim 2, characterized in that: Determining a target lane after the vehicle enters the target road according to the navigation guidance prompt information in the navigation map information includes: When the navigation guidance prompt information includes a recommended lane, determining the recommended lane as a target lane; When the navigation guidance prompt information does not include a recommended lane, determining at least one drivable lane according to the navigation guidance prompt information and the vehicle perception information; According to the navigation guidance prompt information of the next intersection, a target lane is determined from the at least one drivable lane.

5. The method according to any one of claims 1 to 4, characterized in that: According to the current lane of the vehicle and the target lane, the vehicle is controlled to travel, including: Predicting a first distance between the vehicle and the intersection based on the navigation map information; Predicting a second distance between the vehicle and the intersection based on the road topology in the vehicle perception information; Determining the distance between the vehicle and the intersection from the first distance and the second distance according to whether there is an intersection in the road topology; The vehicle is controlled according to the current lane of the vehicle, the target lane and the distance between the vehicle and the intersection.

6. The method according to claim 5, characterized in that Determining the distance between the vehicle and the intersection from the first distance and the second distance according to whether there is an intersection in the road topology includes: When there is no intersection in the road topology, determining the first distance as the distance between the vehicle and the intersection; When there is an intersection in the road topology, the absolute value of the difference between the first distance and the second distance is determined; when the absolute value of the difference is less than the second distance threshold, the second distance is determined to be the distance between the vehicle and the intersection; when the absolute value of the difference is greater than or equal to the second distance threshold, the first distance is determined to be the distance between the vehicle and the intersection.

7. The method according to any one of claims 1 to 4, characterized in that: Obtain vehicle perception information, including: Obtain camera image data and radar point cloud data; Vehicle perception information is determined based on the camera image data and the radar point cloud data.

8. A vehicle driving control device, characterized in that: include: An acquisition module, used to acquire vehicle perception information; A determination module, used to determine the lane the vehicle is currently in based on the vehicle perception information; The determination module is further used to determine the target lane after the vehicle passes through the intersection based on the navigation map information and the vehicle perception information; The control module is used to control the vehicle's driving according to the vehicle's current lane and the target lane.

9. An intelligent driving system, characterized in that: Includes map module, perception module, planning module and control module; The map module is used to obtain navigation map information from a standard map software development kit; The perception module is used to determine vehicle perception information based on camera image data and radar point cloud data; The planning module is used to generate intersection traffic path planning information according to the navigation map information and the vehicle perception information; The control module is used to control the vehicle to pass through the intersection according to the intersection traffic path planning information.

10. An electronic device, characterized in that: The device comprises: a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory to implement the vehicle driving control method as described in any one of claims 1-7.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it is used to implement the vehicle driving control method as described in any one of claims 1-7.

12. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the vehicle driving control method according to any one of claims 1 to 7 is implemented.

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