Vehicle driving control method and device

Abstract

Provided in the present application are a vehicle driving control method and device. The method comprises: acquiring vehicle perception information; on the basis of the vehicle perception information, determining a lane in which a vehicle is currently located; on the basis of navigation map information and the vehicle perception information, determining a target lane for the vehicle after intersection traversal; and on the basis of the lane in which the vehicle is currently located and the target lane, performing driving control on the vehicle. The method is used to achieve the effect of using a standard navigation map instead of a high-definition map to assist intelligent driving vehicles in safely passing through intersections.

Description

Vehicle driving control methods and equipment

[0001] This application claims priority to Chinese patent application filed on October 21, 2024, with application number 202411469885.X and entitled "Vehicle Driving Control Method and Apparatus", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of intelligent driving technology, and in particular to a vehicle driving control method and device. Background Technology

[0003] Intelligent driving vehicles integrate advanced sensor technology, data processing capabilities, artificial intelligence algorithms, and high-precision maps to achieve autonomous navigation and driving in various complex road environments.

[0004] In relevant intelligent driving technologies, vehicle passage through intersections relies 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, then uses high-precision map information to accurately match the perceived environmental information with the road model in the high-precision map, achieving high-precision fusion positioning of the vehicle on the high-precision map. Based on this positioning result, the intelligent driving system, using lane-level navigation information provided by the high-precision map, combined with traffic rules, vehicle status, and predicted behavior of other traffic participants, comprehensively plans the vehicle's path through the intersection. Finally, based on the planned path, it precisely controls the vehicle to pass through the intersection.

[0005] However, without a high-precision map, it is impossible to safely cross an intersection using the above methods. Summary of the Invention

[0006] This application provides a vehicle driving control method and device to solve the problem of reliance on high-precision maps when intelligent driving passes through intersections in the prior art.

[0007] In a first aspect, this application provides a vehicle driving control method, including:

[0008] Acquire vehicle perception information;

[0009] Determine the vehicle's current lane based on vehicle perception information;

[0010] Based on navigation map information and vehicle perception information, determine the target lane after the vehicle passes through the intersection;

[0011] The vehicle's driving control is based on its current lane and target lane.

[0012] In some embodiments, determining the target lane after the vehicle passes through the intersection based on navigation map information and vehicle perception information includes:

[0013] Based on the global navigation path information in the navigation map, determine the first angle between the road segment the vehicle is about to exit and the road segment it is about to enter;

[0014] Based on the first included angle, the target road corresponding to the road segment to be entered is determined from the vehicle's perception information;

[0015] Based on the navigation guidance information in the navigation map, determine the target lane after the vehicle enters the target road.

[0016] In some embodiments, determining the target road corresponding to the upcoming road segment from the vehicle perception information based on the first included angle includes:

[0017] Based on vehicle perception information, determine the second angle between multiple roads at the intersection and the vehicle's current lane;

[0018] Based on the first and second included angles, the target road corresponding to the road segment to be entered is determined from the vehicle's perception information.

[0019] In some embodiments, determining the target lane after the vehicle enters the target road based on navigation guidance information in the navigation map information includes:

[0020] When navigation guidance information includes a recommended lane, select the recommended lane as the target lane;

[0021] When the navigation guidance information does not include a recommended lane, at least one passable lane shall be determined based on the navigation guidance information and vehicle perception information.

[0022] Based on the navigation guidance information at the next intersection, determine the target lane from at least one passable lane.

[0023] In some embodiments, driving control of the vehicle is performed based on the vehicle's current lane and the target lane, including:

[0024] Based on navigation map information, predict the vehicle's initial distance from the intersection;

[0025] Based on the road topology in the vehicle perception information, predict the second distance of the vehicle from the intersection;

[0026] Based on whether there is an intersection in the road topology, the distance of the vehicle from the intersection is determined from the first distance and the second distance.

[0027] The vehicle's driving is controlled based on its current lane, target lane, and distance from the intersection.

[0028] In some embodiments, determining the distance of a vehicle from an intersection in a first distance and a second distance based on whether an intersection exists in the road topology includes:

[0029] When there is no intersection in the road topology, the first distance is determined as the distance between the vehicle and the intersection;

[0030] 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 of the vehicle from 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 of the vehicle from the intersection.

[0031] In some embodiments, acquiring vehicle perception information includes:

[0032] Acquire camera image data and radar point cloud data;

[0033] Vehicle perception information is determined based on camera image data and radar point cloud data.

[0034] Secondly, this application provides a vehicle driving control device, comprising:

[0035] The acquisition module is used to acquire vehicle perception information;

[0036] The determination module is used to determine the current lane of the vehicle based on vehicle perception information;

[0037] The determination module is also used to determine the target lane of the vehicle after it passes through the intersection based on navigation map information and vehicle perception information;

[0038] The control module is used to control the vehicle's movement based on its current lane and target lane.

[0039] Thirdly, this application provides an intelligent driving system, including a map module, a perception module, a planning module, and a control module.

[0040] The map module is used to obtain navigation map information from standard map software development kits;

[0041] The perception module is used to determine vehicle perception information based on camera image data and radar point cloud data;

[0042] The planning module is used to generate intersection traffic route planning information based on navigation map information and vehicle perception information;

[0043] The control module is used to control vehicles to pass through the intersection based on the intersection traffic route planning information.

[0044] Fourthly, this application provides an electronic device, including: a memory and a processor;

[0045] 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 of the first aspect and any embodiment of the first aspect.

[0046] Fifthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle driving control method of the first aspect and any embodiment of the first aspect.

[0047] In a sixth aspect, this application provides a computer program product, which includes a computer program that, when executed by a processor, implements the vehicle driving control method of the first aspect and any embodiment of the first aspect.

[0048] The vehicle driving control method and device provided in this application determine the current lane of the vehicle based on vehicle perception information, and determine the target lane after the vehicle passes through the intersection by combining the vehicle perception information and road-level navigation map information. Then, the vehicle driving control is performed based on the current lane and the target lane, which realizes the effect of using standard navigation map instead of high-precision map to assist intelligent driving vehicles to safely pass through intersections, and avoids dependence on high-precision map. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 is a schematic diagram of the structure of an intelligent driving system in the prior art;

[0051] Figure 2 is a schematic diagram of a vehicle driving scenario provided in an embodiment of this application;

[0052] Figure 3 is a flowchart of a vehicle driving control method provided in an embodiment of this application;

[0053] Figure 4 is a schematic diagram of a vehicle passing through an intersection according to an embodiment of this application;

[0054] Figure 5 is a schematic diagram of the distance between a vehicle and an intersection according to an embodiment of this application;

[0055] Figure 6 is a schematic diagram of a vehicle driving control device provided in an embodiment of this application;

[0056] Figure 7 is a schematic diagram of the structure of an intelligent driving system provided in an embodiment of this application;

[0057] Figure 8 is a schematic diagram of another intelligent driving system provided in an embodiment of this application;

[0058] Figure 9 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application 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 used interchangeably where appropriate. For example, without departing from the scope of this document, first information may also be referred to as second information, and similarly, second information may also be referred to as first information.

[0061] 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, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0062] Terminology Explanation:

[0063] In-vehicle infotainment system: This is a shorthand for in-vehicle infotainment products installed inside vehicles. Functionally, in-vehicle infotainment systems enable information communication between people and vehicles, and between vehicles and the outside world (vehicle-to-vehicle communication).

[0064] Transformer: A neural network model based on the attention mechanism.

[0065] SOME / IP: A service-oriented communication protocol, short for Scalable service-oriented middleware over IP.

[0066] Intelligent driving technology is one of the core cutting-edge technologies in the automotive field. Intelligent driving vehicles integrate advanced sensor technology, data processing capabilities, artificial intelligence algorithms, and high-precision maps to achieve autonomous navigation and driving in various complex road environments. Among these, high-precision maps, as a crucial component of the intelligent driving system, provide vehicles with detailed road geometry information, traffic rules, traffic signs, and obstacle distribution data, serving as a key foundation for accurate vehicle positioning, path planning, and intelligent decision-making.

[0067] As shown in Figure 1, in related technologies, intelligent driving systems include a map module, a perception module, a positioning module, a planning module, and a control module. The map module integrates a high-precision map software development kit (SDK) to provide high-precision map data for a preset area in front of and behind the vehicle based on the vehicle's latitude and longitude and navigation path in the positioning information. The perception module receives data from sensors such as cameras, radar, and lidar to perform tasks such as target detection (e.g., mobile devices, pedestrians, non-motorized vehicles), semantic segmentation (obstacles, ground markings, light poles, signs, passable spaces, etc.), lane line fitting, perceiving the relationship between obstacles and lane lines, and target fusion, and outputs real-time perception results. The positioning module achieves high-precision fusion positioning by matching lane lines and sign information from the perception results with lane lines and sign information in the high-precision map, completing the projection of the vehicle's position onto the high-precision map. The planning module selects the lane and target lane for passing through intersections based on high-precision fusion positioning information, high-precision map lane-level navigation data, and chassis information, while also planning obstacle avoidance based on real-time perception results. The control module outputs control commands based on the planning results and vehicle status information, including chassis information obtained through the vehicle bus, to control the vehicle through the intersection. In other words, the prerequisite for using a high-precision map to control a vehicle through an intersection is that the high-precision map contains a complete road topology, lane topology, and lane selection guidance information, such as the connections between roads, road boundaries, lane relationships, and the positional relationship between the vehicle and lane lines. The intelligent driving system uses fusion positioning to locate the vehicle on the high-precision map and then follows the map's guidance to complete the intersection passage.

[0068] However, the aforementioned intersection traffic management methods rely on high-definition maps for positioning and intersection traffic decisions. High-definition map coverage is limited, especially in remote areas, newly constructed roads, or rapidly changing urban areas. The lack or lag of high-definition maps severely impacts the availability and reliability of intelligent driving systems. Furthermore, the update frequency of high-definition maps cannot keep up with the demands of rapidly changing road environments, limiting the functionality of intelligent driving systems. The high cost of high-definition maps also increases the overall cost of intelligent driving systems.

[0069] To address the aforementioned issues, this application proposes a vehicle driving control method and device. In this method, the vehicle's current lane is determined based on vehicle perception information. The target lane after the vehicle passes through an intersection is determined by combining the vehicle perception information with road-level navigation map information. Based on the vehicle's current lane and the target lane, a path planning and control strategy for the vehicle's passage through the intersection is generated, thereby controlling the vehicle to pass safely through the intersection. This achieves lane-level navigation by replacing high-precision maps with standard maps, eliminating the dependence of the intelligent driving system on high-precision maps and reducing the overall cost of the intelligent driving system.

[0070] The technical solutions of this application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0071] Figure 2 illustrates a vehicle driving scenario according to an embodiment of this application. As shown in Figure 2, the black triangle represents an intelligent driving vehicle. When the vehicle reaches point A, it senses its current lane and, in conjunction with navigation map information provided by a standard map, determines the target lane after the vehicle passes through the intersection. This allows for the planning of the vehicle's driving path from point A to point B, along with a control strategy, to ensure the vehicle safely passes through the intersection.

[0072] In this application, an electronic device is used as the execution subject to perform the vehicle driving control method of the following embodiments. Specifically, the execution subject can be a hardware device of the electronic device, a software application implementing the following embodiments in the electronic device, a computer-readable storage medium on which the software application implementing the following embodiments is installed, or code implementing the software application of the following embodiments. The electronic device can be a vehicle terminal or an intelligent driving system installed in the vehicle.

[0073] Figure 3 shows a flowchart of a vehicle driving control method according to an embodiment of this application. As shown in Figure 3, with an electronic device as the executing entity, the method of this embodiment may include the following steps:

[0074] S101. Obtain vehicle perception information.

[0075] In this embodiment, the electronic device can capture environmental data around the vehicle in real time through its integrated sensor system, including information such as the position, speed, shape of obstacles around the vehicle, and road markings.

[0076] S102. Determine the current lane of the vehicle based on vehicle perception information.

[0077] In this embodiment, the electronic device can utilize image processing and machine learning algorithms to process and analyze sensor data to accurately identify and understand the traffic environment in which the vehicle is currently located. Through image recognition technology, the electronic device can identify key road elements such as lane lines, traffic signs, and traffic lights. Based on the processing and analysis results of the vehicle's perception information, the electronic device can accurately determine the lane in which the vehicle is currently located.

[0078] S103. Based on navigation map information and vehicle perception information, determine the target lane after the vehicle passes through the intersection.

[0079] In this embodiment, the navigation map information is provided by a standard navigation map, including road-level global navigation path information. Based on the global navigation path information in the navigation map, the electronic device can determine the target road after the vehicle passes through the intersection. Combined with vehicle perception information, it can determine the target lane from multiple lanes on the target road.

[0080] S104. Control the vehicle's movement based on its current lane and target lane.

[0081] In this embodiment, after the electronic device determines the vehicle's current lane and target lane, it can use path planning and decision-making algorithms based on navigation map information, navigation positioning information, and perception information to plan the optimal driving path and control strategy, thereby controlling the vehicle to pass through the intersection.

[0082] The navigation and positioning information is provided by a standard navigation map. The electronic device determines the vehicle's position in the global coordinate system map based on the navigation and positioning information and the perception information, that is, it generates global positioning information. Based on the navigation map information and the perception information, a local coordinate system is constructed with the vehicle's current position as the origin. Then, based on Kalman filtering, the vehicle's driving state information, such as inertial measurement unit information and wheel speed information, is fused to calculate the vehicle's pose, thereby obtaining continuous and stable local positioning information.

[0083] Electronic devices plan the vehicle's path through intersections based on global and local positioning information. Based on the planned path and vehicle status, they calculate corresponding lateral and longitudinal control signals, as well as body control signals, and send them via the vehicle bus to controllers in the chassis, body, powertrain, and steering systems to control the vehicle. Lateral control signals include steering signals; longitudinal control signals include drive signals, braking signals, and gear position signals; body control signals include turn signals and hazard warning lights.

[0084] Furthermore, when electronic devices acquire navigation map information and navigation positioning information, they can convert this information into a standard map data format before use. This data format conversion serves two purposes: firstly, it isolates the impact of changes in external map data formats on the internal navigation planning and positioning of the electronic device. For example, changes in external map data formats can occur due to navigation map version upgrades or changes in navigation map providers. Secondly, the internal data format of the electronic device facilitates use by various modules within the device, enabling rapid retrieval of key data information.

[0085] The vehicle driving control method provided in this embodiment determines the vehicle's current lane based on vehicle perception information when the vehicle needs to pass through an intersection. It then determines the target lane after passing through the intersection using navigation information provided by a standard map and the vehicle perception information. Finally, it plans the vehicle's trajectory and control strategy based on the current lane and the target lane. This embodiment uses a standard map combined with vehicle perception information for lane-level navigation, achieving the effect of lane-level navigation using a standard map and avoiding the dependence of intelligent driving vehicles on high-precision maps.

[0086] Optionally, step S101 can be implemented in the following ways:

[0087] S1011. Acquire camera image data and radar point cloud data.

[0088] S1012. Determine vehicle perception information based on camera image data and radar point cloud data.

[0089] In this embodiment, both the camera and radar are deployed on the vehicle to acquire information about the vehicle's surrounding environment. The electronic device can extract feature data from the camera image data and convert it into a bird's-eye view image. It can also extract geometric features from the radar point cloud data and project these features into a height map in the bird's-eye view. The bird's-eye view image and the height map are then fused to form a bird's-eye view of the vehicle's surrounding environment.

[0090] We utilize the self-attention mechanism of the Transformer model to extract and fuse features from multimodal data in a bird's-eye view space. The Transformer can capture long-range dependencies in the input sequence, thereby modeling in both space and time and improving the accuracy and robustness of perception.

[0091] By using a multi-layer Transformer encoder-decoder structure, feature extraction and contextual information integration are performed on images in the bird's-eye view space, and the fused feature map is output.

[0092] The feature map output by the Transformer is further transformed into an occupancy probability map in 3D space through an occupancy network. This occupancy probability map is then projected into 3D to generate perceptual information about the road environment surrounding the vehicle. This perceptual information includes the precise location 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, pedestrian crossings, road arrows, traffic lights, and traffic signs. Moving targets include vehicles, non-motorized vehicles, and pedestrians.

[0093] In some embodiments, step S103 is specifically implemented by the following steps:

[0094] S201. Based on the global navigation path information in the navigation map, determine the first angle between the road segment the vehicle is about to exit and the road segment it is about to enter.

[0095] S202. Based on the first included angle, determine the target road corresponding to the road segment to be entered from the vehicle perception information.

[0096] S203. Based on the navigation guidance information in the navigation map, determine the target lane after the vehicle enters the target road.

[0097] Specifically, based on the shape points of the road segment the vehicle is about to exit and the road segment it is about to enter from the global navigation path information, the electronic device calculates the first included angle between the two road segments. For example, in Figure 4, this is the angle between the line formed by shape points 1 and 2 and the line formed by shape points 2 and 3. It should be understood that the first included angle is the angle between the vehicle's current road and the target road. Based on the first included angle, the electronic device can determine the target road that matches the first included angle from the vehicle's perception information.

[0098] As shown in Figure 4, when the vehicle reaches point C, the global navigation route information indicates an imminent right turn. Near point C, there are two intersections. To ensure the vehicle turns right onto the designated right-turn route indicated by the global navigation route information, the electronic device determines a first angle based on the global navigation route information and then identifies a target road matching this first angle from the vehicle's perception information. For example, if the first angle is 90 degrees, and the angle between the first intersection in the perception information and the vehicle's current road is also 90 degrees, then the first intersection can be identified as the right-turn route indicated by the global navigation route information.

[0099] Optionally, step S202 can be implemented in the following ways:

[0100] S2021. Based on vehicle perception information, determine the second angle between multiple roads at the intersection and the lane where the vehicle is currently located.

[0101] S2022. Based on the first included angle and the second included angle, determine the target road corresponding to the road segment to be entered from the vehicle perception information.

[0102] Specifically, the electronic device can determine the second angle between multiple roads at the intersection and the vehicle's current lane in the perceived information. For example, in Figure 4, the second angle between the first fork road and the vehicle's current lane is 90 degrees, and the second angle between the second fork road and the vehicle's current lane is 40 degrees (or it can be described as 140 degrees).

[0103] Multiple second included angles are compared with the first included angle. The road corresponding to the second included angle whose absolute value of the difference with the first included angle is less than a preset threshold is the target road.

[0104] In this embodiment, for intersections with multiple roads, the target road after the vehicle passes through the intersection is determined based on the first angle between the exit segment and the entry segment in the global navigation path information and the second angle between the current road and the other roads in the vehicle perception information. This can be compatible with the accuracy deviation problem of standard maps, improve the accuracy of the road prompts for the vehicle entering the global navigation path information, and improve the accuracy of lane-level guidance for vehicles to pass through intersections using standard maps.

[0105] Optionally, step S203 can be implemented in the following ways:

[0106] S2031. When the navigation guidance information includes a recommended lane, the recommended lane shall be determined as the target lane.

[0107] S2032. When the navigation guidance information does not include a recommended lane, at least one passable lane shall be determined based on the navigation guidance information and vehicle perception information.

[0108] S2033. Based on the navigation guidance information at the next intersection, determine the target lane from at least one passable lane.

[0109] Specifically, when the navigation guidance information does not include the recommended lane, the electronic device can determine the target lane based on the guidance information from the two guide points ahead and the lane information at the intersection, thereby enabling the vehicle to efficiently pass through the next intersection while passing through the current intersection.

[0110] For example, the navigation guidance message might be "Turn left at the current intersection, turn right at the intersection 800 meters later," with two guidance points: "current intersection" and "intersection 800 meters later." 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.

[0111] When there is only one left-turn lane, that left-turn lane is designated as the target lane. When there are multiple left-turn lanes, the electronic equipment selects the rightmost lane among the multiple left-turn lanes as the target lane based on the "right turn at the intersection 800 meters later" rule.

[0112] It is understandable that when there are multiple passable lanes, if the second guidance is to turn right, the rightmost passable lane is determined as the target lane; if the second guidance is to turn left or make a U-turn, the leftmost passable lane is determined as the target lane; if the second guidance is to go straight, if the number of passable lanes is odd, the middle passable lane is determined as the target lane, and if the number of passable lanes is even, the middle two lanes with less traffic are determined as the target lane.

[0113] In this embodiment, when a vehicle passes through an intersection with multiple passable lanes, the target lane is determined based on the guidance action information of two guidance points, enabling the vehicle to pass through the current intersection smoothly and more efficiently to pass through the next intersection.

[0114] In some embodiments, step S104 is specifically implemented by the following steps:

[0115] S301. Based on navigation map information, predict the first distance of the vehicle from the intersection.

[0116] Specifically, electronic devices can determine the initial distance of a vehicle from an intersection using navigation map information from a standard map.

[0117] The navigation map information can include global navigation route information and navigation guidance prompts. The global navigation route information includes the distance between the vehicle's current location and the upcoming intersection. For example, on the global navigation route information display page, a bubble pops up at the intersection showing "1 kilometer," indicating that the vehicle's current location is 1 kilometer away from the intersection.

[0118] Navigation guidance information can be in text form or in voice broadcast form. For example, displaying or broadcasting "Turn left at the intersection 800 meters ahead" on the in-vehicle display screen confirms that the vehicle is 800 meters away from the intersection.

[0119] In addition, electronic devices can determine the initial distance between a vehicle and an intersection based on the vehicle's latitude and longitude and the intersection's latitude and longitude information in the navigation and positioning information.

[0120] S302. Based on the road topology in the vehicle perception information, predict the second distance between the vehicle and the intersection.

[0121] The perception information includes road topology. Based on the road topology, electronic devices can predict the distance of vehicles to obstacles or other traffic participants. If there is an intersection within the vehicle's preset range, electronic devices can also predict the second distance of the vehicle from the intersection.

[0122] S303. Determine the distance of the vehicle from the intersection based on whether there is an intersection in the road topology, using the first distance and the second distance.

[0123] Because the accuracy of navigation map information in standard maps is relatively lower than that in high-precision maps, it is necessary to perform necessary calibration on the first distance determined by navigation map information in order to avoid the low accuracy causing potential safety hazards to vehicles.

[0124] S304. Based on the vehicle's current lane, target lane, and distance from the intersection, control the vehicle's movement.

[0125] Specifically, electronic devices can plan the vehicle's driving path from its current location to the target lane and the corresponding control strategy based on the vehicle's current lane, the target lane, and the distance between the vehicle and the intersection. Then, based on the control strategy, the electronic devices can control the vehicle to drive along the planned path to the target road and complete the intersection passage process.

[0126] Specifically, when the distance between a vehicle and the intersection is greater than a distance threshold, the electronic equipment controls the vehicle to continue in the current lane or plans a lane change. When the distance between the vehicle and the intersection is less than or equal to the distance threshold, the electronic equipment plans a route through the intersection.

[0127] As shown in Figure 5, the first distance is the distance between the vehicle and the intersection predicted based on navigation map information or navigation positioning information, and the second distance is the distance between the vehicle and the intersection predicted from the perception information. The first distance may be greater than or less than the second distance. In this embodiment, the distance between the vehicle and the intersection is determined based on whether an intersection exists in the perception information and the absolute value of the difference between the first distance and the second distance.

[0128] Optionally, step S303 can be implemented in the following ways:

[0129] S3031. When there is no intersection in the road topology, the first distance is determined as the distance between the vehicle and the intersection.

[0130] 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 between the vehicle and 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 between the vehicle and the intersection.

[0131] Specifically, when the absolute value of the difference is greater than or equal to the second distance threshold, in order to prevent false corrections, the electronic equipment does not perform correction processing and determines the first distance as the distance between the vehicle and the intersection.

[0132] In this embodiment, the electronic device uses navigation guidance prompts or navigation positioning information from the navigation map information provided by the standard map as the data source, and combines the road topology information in the perception information to perform necessary correction processing on the distance between the vehicle and the intersection. This makes it easier for the electronic device to use more accurate navigation map information when planning navigation, and reduces the risk of missing the intersection due to the low positioning accuracy of the standard map.

[0133] Figure 6 shows a schematic diagram of a vehicle driving control device according to an embodiment of this application. As shown in Figure 6, the vehicle driving control device 10 of this embodiment is used to implement the operation of the electronic device corresponding to any of the above method embodiments. The vehicle driving control device 10 of this embodiment includes:

[0134] Acquisition module 11 is used to acquire vehicle perception information;

[0135] The determination module 12 is used to determine the current lane of the vehicle based on the vehicle perception information;

[0136] The determination module 12 is also used to determine the target lane of the vehicle after it passes through the intersection based on navigation map information and vehicle perception information.

[0137] The control module 13 is used to control the vehicle's movement based on the vehicle's current lane and the target lane.

[0138] The vehicle driving control device 10 provided in this application embodiment can execute the above method embodiment. Its specific implementation principle and technical effect can be found in the above method embodiment, and will not be repeated here.

[0139] Figure 7 is a schematic diagram of the structure of an intelligent driving system provided in an embodiment of this application. As shown in Figure 7, 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.

[0140] Map module 21 is used to obtain navigation map information and navigation positioning information from the standard map software development kit (SDK);

[0141] The perception module 22 is used to determine vehicle perception information based on camera image data and radar point cloud data;

[0142] The positioning module 23 is used to generate global positioning information and local positioning information from navigation map information, navigation positioning information and perception information;

[0143] Planning module 24 is used to generate intersection traffic route planning information based on navigation map information, global positioning information, local positioning information and vehicle perception information;

[0144] The control module 25 is used to control vehicles to pass through the intersection based on the intersection traffic route planning information.

[0145] It should be noted that in this embodiment, the standard map SDK can be integrated into the vehicle's built-in navigation map, which provides navigation map information and navigation positioning information, thereby reusing existing vehicle resources to reduce per-vehicle costs and broadening the operational scope of the intelligent driving system.

[0146] Specifically, the vehicle's infotainment system can send navigation map information and navigation positioning information to the intelligent driving system via Ethernet, and the various modules of the intelligent driving system can communicate with each other using SOME / IP.

[0147] As shown in Figure 8, 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 the interface, without having to communicate directly with the vehicle's infotainment system, thus solving the problem of the intelligent driving system's direct dependence on the vehicle's infotainment system.

[0148] Furthermore, the map provider's standard map SDK communicates with the vehicle's navigation map SDK via Ethernet to synchronize necessary information, such as the navigation route selected by the user on the vehicle's system and the vehicle's navigation positioning information. The standard map SDK combines local map data and cloud-based map data to provide the map module with all the map information required for intelligent driving.

[0149] The intelligent driving system provided in this embodiment can execute the above method embodiment. Its specific implementation principle and technical effect can be found in the above method embodiment, and will not be repeated here.

[0150] Figure 9 shows a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. As shown in Figure 9, the electronic device 30 is used to implement the operation corresponding to the electronic device in any of the above method embodiments. The electronic device 30 in this embodiment may include: a memory 31, a processor 32, and a communication interface 34.

[0151] The memory 31 is used to store computer programs. The memory 31 may include high-speed random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device, or a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0152] Processor 32 is used to execute computer programs stored in memory to implement the methods in the above embodiments. For details, please refer to the relevant descriptions in the foregoing method embodiments. The processor 32 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0153] Alternatively, the memory 31 can be either standalone or integrated with the processor 32.

[0154] When the memory 31 is a device independent of the processor 32, the electronic device 30 may further include a bus 33. This 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, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0155] The communication interface 34 can be connected to the processor 32 via the bus 33. The processor 32 can control the communication interface 34 to realize the functions of receiving and sending signals.

[0156] The electronic device 30 provided in this embodiment can be used to execute the above-described vehicle driving control method. Its implementation method and technical effects are similar, and will not be described again here.

[0157] This application also provides a computer-readable storage medium storing a computer program / instructions, which, when executed by a processor, are used to implement the methods provided in the various embodiments described above.

[0158] The computer-readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of a computer program from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a computer-readable storage medium is coupled to a processor, enabling the processor to read information from 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 reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the ASIC can reside in a user equipment. Of course, the processor and the computer-readable storage medium can also exist as discrete components in a communication device.

[0159] Specifically, the computer-readable storage medium can 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 storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0160] This application also provides a computer program product comprising a computer program / instructions stored in a computer-readable storage medium. At least one processor of the device can read the computer program / instructions from the computer-readable storage medium, and the at least one processor executes the computer program / instructions to cause the device to perform the methods provided in the various embodiments described above.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0162] The modules can be physically separate, for example, installed in different locations within a single device, installed on different devices, distributed across multiple network units, or distributed across multiple processors. Alternatively, the modules can be integrated, for example, installed in the same device, or integrated into a single codebase. The modules can exist in hardware form, software form, or a combination of both. This application can select some or all of the modules to achieve the objectives of this embodiment based on actual needs.

[0163] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A vehicle travel control method characterized by comprising: The method comprises: acquiring vehicle perception information; determining a current lane of the vehicle according to the vehicle perception information; determining a target lane after the vehicle passes through the intersection according to navigation map information and the vehicle perception information; controlling the vehicle to travel according to the current lane of the vehicle and the target lane.

2. The method of claim 1, wherein, The method comprises: determining a first included angle of a road section that the vehicle is about to exit and a road section that the vehicle is about to enter according to global navigation path information in the navigation map information; determining a target road corresponding to the road section that the vehicle is about to enter in the vehicle perception information according to the first included angle; determining a target lane after the vehicle enters the target road according to navigation guide prompt information in the navigation map information.

3. The method of claim 2, wherein, The method comprises: determining a second included angle of a plurality of roads at the intersection and the current lane of the vehicle according to the vehicle perception information; determining a target road corresponding to the road section that the vehicle is about to enter in the vehicle perception information according to the first included angle and the second included angle.

4. The method of claim 2, wherein, The method comprises: when the navigation guide prompt information comprises a recommended lane, determining the recommended lane as the target lane; when the navigation guide prompt information does not comprise a recommended lane, determining at least one passable lane according to the navigation guide prompt information and the vehicle perception information; determining the target lane from the at least one passable lane according to navigation guide prompt information of a next intersection.

5. The method according to any one of claims 1 to 4, characterized in that, The method comprises: predicting a first distance of the vehicle from the intersection according to the navigation map information; predicting a second distance of the vehicle from the intersection according to road topology in the vehicle perception information; determining the distance of the vehicle from the intersection in the first distance and the second distance according to whether there is an intersection in the road topology; controlling the vehicle to travel according to the current lane of the vehicle, the target lane and the distance of the vehicle from the intersection.

6. The method of claim 5, wherein, The method comprises: when there is no intersection in the road topology, determining the first distance as the distance of the vehicle from the intersection; when there is an intersection in the road topology, determining an absolute value of a difference between the first distance and the second distance; when the absolute value of the difference is less than a second distance threshold, determining the second distance as the distance of the vehicle from the intersection; when the absolute value of the difference is greater than or equal to the second distance threshold, determining the first distance as the distance of the vehicle from the intersection.

7. The method according to any one of claims 1 to 6, characterized in that, The method comprises: acquiring camera image data and radar point cloud data; determining vehicle perception information according to the camera image data and the radar point cloud data.

8. A vehicle travel control device characterized by comprising: The method comprises: an acquisition module, configured to acquire vehicle perception information; a determination module, configured to determine a current lane of the vehicle according to the vehicle perception information; The determining module is further configured to determine a target lane after the vehicle passes through the intersection according to the navigation map information and the vehicle perception information; The control module is configured to perform driving control on the vehicle according to a current lane of the vehicle and the target lane.

9. An intelligent driving system, characterized by, The vehicle driving control system comprises a map module, a perception module, a planning module and a control module; The map module is configured to acquire navigation map information from a standard map software development kit; The perception module is configured to determine vehicle perception information according to camera image data and radar point cloud data; The planning module is configured to generate intersection passing path planning information according to the navigation map information and the vehicle perception information; The control module is configured to control the vehicle to pass through the intersection according to the intersection passing path planning information.

10. An electronic device, comprising: The device comprises a memory and a processor; The memory is configured to store a computer program; The processor is configured to execute the computer program stored in the memory to implement the vehicle driving control method according to 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 the computer program is configured to implement the vehicle driving control method according to any one of claims 1-7 when executed by a processor.

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

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