Vehicle driving control method, and device

Abstract

Provided in the present application are a vehicle driving control method, and a device. The method comprises: determining the distance between a vehicle and an intersection; when the distance is less than a first distance threshold, acquiring camera image data and radar point cloud data; on the basis of the camera image data and the radar point cloud data, determining perception information; on the basis of the perception information, determining whether the vehicle needs to make a lane change; and when it is determined that the vehicle needs to make a lane change, generating lane change path planning information on the basis of navigation map information, navigation positioning information and the perception information, wherein the lane change path planning information is used for controlling the vehicle to complete a lane change before the intersection. The method is used for achieving the effect of using a standard map to efficiently perform lane-level lane change navigation in an energy-saving manner.

Description

Vehicle driving control methods and equipment

[0001] This application claims priority to Chinese patent application filed on October 21, 2024, with application number 202411469891.5 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, but is not limited to, the field of intelligent driving technology, and more specifically, 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 related intelligent driving technologies, lane changing, as a complex driving behavior, relies on the close collaboration of high-precision maps and multi-sensor fusion technology. Specifically, the intelligent driving system collects surrounding environmental information in real time, and then uses high-precision map information to accurately match the perceived environmental data with the road model in the map, achieving high-precision fusion positioning of the vehicle on the high-precision map. Based on this positioning result, the intelligent driving system, according to the lane-level navigation information provided by the high-precision map, combined with traffic rules, vehicle status, and predicted behavior of other traffic participants, comprehensively judges and makes a lane-changing decision. Finally, according to the decision command, it precisely controls the vehicle to execute the lane-changing operation, ensuring a safe and smooth completion of the lane-changing process.

[0005] However, without high-precision maps, lane-level navigation cannot be achieved using the above methods. Summary of the Invention

[0006] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0007] This application provides a vehicle driving control method and device to solve the problem of lane-level navigation being impossible when high-precision maps are unavailable.

[0008] In a first aspect, this application provides a vehicle driving control method, including:

[0009] Determine the distance between the vehicle and the intersection;

[0010] When the distance is less than the first distance threshold, acquire camera image data and radar point cloud data;

[0011] Based on camera image data and radar point cloud data, the perception information is determined;

[0012] Based on the perceived information, determine whether the vehicle needs to change lanes;

[0013] When it is determined that a vehicle needs to change lanes, lane change path planning information is generated based on navigation map information, navigation positioning information, and perception information. The lane change path planning information is used to control the vehicle to complete the lane change before the intersection.

[0014] In some embodiments, lane change route planning information is generated based on navigation map information, navigation positioning information, and perception information, including:

[0015] Global positioning information is generated based on navigation and positioning information and perception information;

[0016] Based on navigation map information and sensor information, local positioning information is generated;

[0017] Based on the global navigation path information and navigation guidance prompts in the navigation map information, as well as the global positioning information, determine the lane change information, which includes the lane change speed and lane change angle.

[0018] Based on perception information, local positioning information, and lane change information, lane-level lane change path planning information is determined.

[0019] In some embodiments, determining the distance of a vehicle from an intersection includes:

[0020] Based on navigation map information or navigation positioning information, predict the first distance of the vehicle from the intersection;

[0021] Based on camera image data and radar point cloud data, perception information is determined, including road topology;

[0022] Based on the road topology, predict the second distance of the vehicle from the intersection;

[0023] Based on whether an intersection exists in the road topology, the distance of the vehicle from the intersection is determined in the first distance and the second distance.

[0024] 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:

[0025] When there is no intersection in the road topology, the first distance is determined as the distance between the vehicle and the intersection;

[0026] 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.

[0027] In some embodiments, determining whether a vehicle needs to change lanes based on perceived information includes:

[0028] Determine the target lane based on navigation guidance and perception information in the navigation map;

[0029] Determine the current lane based on the perceived information;

[0030] Based on the target lane and the current lane, determine whether the vehicle needs to change lanes.

[0031] In some embodiments, determining the target lane based on navigation guidance prompts and perception information in the navigation map information includes:

[0032] When navigation guidance information includes a recommended lane, select the recommended lane as the target lane;

[0033] 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 the perception information.

[0034] Based on the navigation guidance information at the next intersection, determine the target lane from at least one passable lane.

[0035] In some embodiments, before determining the distance of the vehicle from the intersection, the method further includes:

[0036] Obtain navigation map information or navigation location information;

[0037] Determine the distance of the vehicle from the intersection, including:

[0038] Determine the distance between the vehicle and the intersection based on navigation guidance or location information in the navigation map.

[0039] In some embodiments, after determining the distance of the vehicle from the intersection, the method further includes:

[0040] When the distance between the vehicle and the intersection is greater than or equal to the first distance threshold, the vehicle is controlled to travel in the current lane.

[0041] Secondly, this application provides a vehicle driving control device, comprising:

[0042] The determination module is used to determine the distance of the vehicle from the intersection;

[0043] The acquisition module is used to acquire camera image data and radar point cloud data when the distance is less than a first distance threshold.

[0044] The determination module is also used to determine perception information based on camera image data and radar point cloud data;

[0045] The judgment module is used to determine whether a vehicle needs to change lanes based on the perceived information.

[0046] The generation module is used to generate lane change path planning information based on navigation map information, navigation positioning information, and perception information when a vehicle needs to change lanes. The lane change path planning information is used to control the vehicle to complete the lane change before the intersection.

[0047] Thirdly, this application provides an intelligent driving system, including a map module, a perception module, a positioning module, a planning module, and a control module;

[0048] The map module is used to obtain navigation map information and navigation positioning information from standard map software development kits;

[0049] The perception module is used to determine perception information based on camera image data and radar point cloud data;

[0050] The positioning module is used to generate global positioning information and local positioning information based on navigation map information, navigation positioning information, and perception information;

[0051] The planning module is used to generate lane change path planning information based on navigation map information, navigation positioning information, perception information, global positioning information, and local positioning information.

[0052] The control module is used to control the vehicle to complete the lane change based on the lane change path planning information.

[0053] Fourthly, this application provides an electronic device, including: a memory and a processor;

[0054] 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.

[0055] 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.

[0056] 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.

[0057] The vehicle driving control method and device provided in this application determine the distance between the vehicle and the intersection. When the distance is less than a first distance threshold, camera image data and radar point cloud data are acquired. Based on the camera image data and radar point cloud data, perception information is determined. Based on the perception information, it is determined whether the vehicle needs to change lanes. When it is determined that the vehicle needs to change lanes, lane change path planning information is generated based on navigation map information, navigation positioning information, and perception information, thereby controlling the vehicle to complete the lane change before the intersection. Because it combines standard navigation map information and navigation positioning information with perception information, lane-level lane change navigation can be performed without relying on high-precision maps, reducing the cost of intelligent driving systems. Furthermore, the method of this application acquires camera image data and radar point cloud data to determine perception information when the distance is less than the first distance threshold, eliminating the need for real-time determination of perception information, reducing computational load and energy consumption, and achieving energy-saving effects. Attached Figure Description

[0058] 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.

[0059] Figure 1 is a schematic diagram of the structure of an intelligent driving system in the prior art;

[0060] Figure 2 is a schematic diagram of a vehicle driving scenario provided in an embodiment of this application;

[0061] Figure 3 is a flowchart of a vehicle driving control method provided in an embodiment of this application;

[0062] Figure 4 is a schematic diagram of lane-level lane change path planning provided in an embodiment of this application;

[0063] Figure 5 is a schematic diagram of the distance between a vehicle and an intersection according to an embodiment of this application;

[0064] Figure 6 is a schematic diagram of a vehicle driving control device provided in an embodiment of this application;

[0065] Figure 7 is a schematic diagram of the structure of an intelligent driving system provided in an embodiment of this application;

[0066] Figure 8 is a schematic diagram of another intelligent driving system provided in an embodiment of this application;

[0067] Figure 9 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0068] 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.

[0069] 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.

[0070] 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.

[0071] Terminology Explanation:

[0072] 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).

[0073] Transformer: A neural network model based on the attention mechanism.

[0074] SOME / IP: A service-oriented communication protocol, short for Scalable service-oriented middleware over IP.

[0075] 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.

[0076] 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, outputting 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 lane change positions and lanes 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, thereby controlling the vehicle to change lanes.

[0077] However, the aforementioned lane-changing methods rely on high-definition maps for positioning and lane-changing 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.

[0078] To address the aforementioned issues, this application proposes a vehicle driving control method and device. This method employs standard navigation for navigation planning and positioning, and combines this with perception information to achieve lane-level navigation, thus eliminating the reliance of intelligent driving on high-precision maps. Furthermore, the method in this application acquires perception information only when the vehicle has traveled within a preset distance of an intersection, eliminating the need for real-time acquisition of perception information and lane-change planning. This reduces computational load and energy consumption, achieving highly efficient and energy-saving lane-level navigation using standard navigation.

[0079] The technical solutions of this application will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0080] 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. During the driving process, the distance between the vehicle and the intersection is determined in real time. If vehicle B is far from the intersection, it continues to drive in the current lane. If vehicle A is close to the intersection, it is determined whether vehicle A needs to change lanes. The distance can be determined by the relationship between the distance and a preset threshold. Different preset thresholds can be set for different levels of roads as needed. For example, a preset threshold of 2 kilometers can be set for expressways, and a preset threshold of 1 kilometer can be set for non-expressways. Alternatively, a preset threshold of 600 meters can be set for expressways, and a preset threshold of 300 meters can be set for non-expressways.

[0081] 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.

[0082] Figure 3 shows a flowchart of a vehicle driving control method provided in an embodiment of this application.

[0083] As shown in Figure 3, with an electronic device as the executing entity, the method in this embodiment may include the following steps:

[0084] S101. Determine the distance between the vehicle and the intersection.

[0085] In this embodiment, the electronic device can determine the distance of the vehicle from the intersection using navigation map information from a standard map. For example, if the navigation map information shows a traffic light 500 meters ahead indicating straight ahead, then the distance of the vehicle from the intersection can be determined to be 500 meters. Alternatively, the electronic device can determine the distance of the vehicle from the intersection based on the vehicle's latitude and longitude and the intersection's latitude and longitude information from the navigation positioning information.

[0086] Optionally, the electronic device can first obtain navigation map information or navigation positioning information, and then determine the distance of the vehicle from the intersection based on the navigation guidance prompts or navigation positioning information in the navigation map information.

[0087] 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.

[0088] 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.

[0089] S102. When the distance is less than the first distance threshold, acquire camera image data and radar point cloud data.

[0090] In this embodiment, both the camera and radar are deployed on the vehicle to acquire information about the vehicle's surrounding environment. The first distance threshold can be set according to the road grade or as needed; no limitation is made here.

[0091] S103. Determine the perception information based on camera image data and radar point cloud data.

[0092] In this embodiment, the electronic device can extract feature data from camera image data and convert the feature data into an image under a bird's-eye view. It can also extract geometric features from 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 then fused to form a bird's-eye view of the environment surrounding the vehicle.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] S104. Based on the perceived information, determine whether the vehicle needs to change lanes.

[0097] In this embodiment, changing lanes is to prepare for passing through the intersection smoothly. Therefore, the current lane of the vehicle is determined by the perception information, and then the lane in which the vehicle will pass through the intersection, i.e. the target lane, is determined by combining the global navigation path information or navigation guidance prompt information, and it is determined whether the vehicle needs to change lanes.

[0098] For example, if the system detects that the vehicle is in the straight lane, and the navigation prompts "turn left at the intersection ahead," then the target lane is the left-turn lane, and thus it can be determined that the vehicle needs to change lanes.

[0099] S105. When it is determined that a vehicle needs to change lanes, generate lane change path planning information based on navigation map information, navigation positioning information, and perception information.

[0100] Among them, lane change path planning information is used to control vehicles to complete lane changes before intersections.

[0101] In this embodiment, the navigation map information includes global navigation path information, navigation guidance prompts, and navigation status information.

[0102] Global navigation path information is generated when a navigation path is enabled or updated on a standard navigation map. The navigation path consists of several road segments arranged sequentially according to the navigation direction. Each road segment includes road attribute information such as road segment identifier, shape points that make up the road segment, corresponding navigation action when exiting the road segment, road segment length, road level corresponding to the road segment, and road type corresponding to the road segment.

[0103] Navigation guidance prompts are generated when navigation triggers a prompt. During navigation, the standard map transmits navigation guidance prompts within a preset distance to the electronic device at a frequency of 1Hz. Navigation guidance prompts include guidance action information, intersection and lane information, and information on confusing and complex intersections. Guidance action information can include information on two guide points ahead, specifically the distance between the vehicle and the guide point, and the type of guidance action. Guidance action types include left turn, right turn, and straight ahead. Intersection and lane information includes the distance between the vehicle and the intersection, the number of lanes at the intersection, the direction of travel in the intersection lanes, left and right extended lanes, and recommended lanes.

[0104] Navigation status information is generated when the navigation status changes, such as when road conditions change.

[0105] In navigation mode, the standard map sends navigation and positioning information to electronic devices at a frequency of 1Hz. The navigation and positioning information includes the vehicle's current latitude and longitude, vehicle heading, road binding status, road segment markers, and distance of the vehicle's position relative to the starting point of the road segment.

[0106] 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 caused by navigation map version upgrades or changes in navigation map providers. Secondly, it ensures that the internal data format of the electronic device is easily used by various modules within the device, facilitating the rapid retrieval of key data information.

[0107] In this embodiment, when the electronic device determines that the vehicle needs to change lanes, it plans the lane-changing path by combining navigation map information, navigation positioning information, and perception information.

[0108] The vehicle driving control method provided in this embodiment determines the distance between the vehicle and the intersection. When the distance is less than a first distance threshold, it acquires vehicle sensor information and determines perception information. Then, based on the perception information, it determines whether the vehicle needs to change lanes. When it is determined that the vehicle needs to change lanes, it combines navigation map information, navigation positioning information, and perception information to perform lane change planning. This achieves the effect of using a standard map for lane-level lane change navigation, avoiding inaccurate planning caused by the low accuracy of the standard map or the loss of vehicle information. At the same time, it only acquires sensor information when it is determined that the distance between the vehicle and the intersection is less than the threshold, without the need to acquire and process sensor information in real time, thus reducing the computational load and energy consumption of electronic equipment.

[0109] In some embodiments, after step S101, the electronic device may further control the vehicle to travel in the current lane when the distance between the vehicle and the intersection is greater than or equal to a first distance threshold.

[0110] Specifically, when the distance between a vehicle and an intersection is greater than or equal to a first distance threshold, the vehicle travels in the current lane to reduce energy consumption.

[0111] In some embodiments, generating lane change path planning information based on navigation map information, navigation positioning information, and perception information in step S105 may include the following steps:

[0112] S1051. Generate global positioning information based on navigation and positioning information and perception information.

[0113] S1052. Generate local positioning information based on navigation map information and perception information.

[0114] S1053. Determine lane change information based on the global navigation path information and navigation guidance prompts in the navigation map information, as well as the global positioning information.

[0115] The lane change information includes lane change speed and lane change angle.

[0116] S1054. Based on the perception information, local positioning information, and lane change information, determine the lane-level lane change path planning information.

[0117] In this embodiment, after the electronic device acquires navigation map information, navigation positioning information, and perception information, it determines the vehicle's position in the global coordinate system map based on the navigation positioning information and perception information, that is, it generates global positioning information. Based on the navigation map information and perception information, it constructs a local coordinate system with the vehicle's current position as the origin, and fuses vehicle driving state information such as inertial measurement unit information and wheel speed information based on Kalman filtering to calculate the vehicle's pose, thereby obtaining continuous and stable local positioning information.

[0118] The electronic device calculates lane change information based on global navigation path information, navigation guidance prompts, and global positioning information from the navigation map. This lane change information includes the timing, speed, and angle of the lane change. Then, based on the road topology, local positioning information, and lane change information from the perception information, it calculates refined lane-level lane change path information (as shown by the dashed arrow in Figure 4), thereby achieving navigation lane change planning and replacing the lane-level navigation of high-precision maps.

[0119] In some embodiments, step S101 may include the following steps:

[0120] S1011. Based on navigation map information or navigation positioning information, predict the first distance of the vehicle from the intersection.

[0121] S1012. Determine the perception information based on camera image data and radar point cloud data.

[0122] The sensing information includes road topology.

[0123] Step S1012 is implemented in a similar way to step S103 in the embodiment of Figure 3, and will not be described again here.

[0124] S1013. Based on the road topology, predict the second distance of the vehicle from the intersection.

[0125] S1014. 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.

[0126] Figure 5 is a schematic diagram illustrating the distance between a vehicle and an intersection according to this embodiment. 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. This embodiment determines the distance between the vehicle and the intersection 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.

[0127] Optionally, step S1014 includes:

[0128] If there is no intersection in the road topology, the first distance is determined as the distance between the vehicle and the intersection.

[0129] 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.

[0130] 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.

[0131] 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 it with 3D road topology information from the perception information to perform necessary correction processing on the distance between the vehicle and the intersection. Based on the information provided by the standard map, the global positioning can be matched with the navigation map information, which facilitates the use of effective navigation map information when the electronic device performs navigation planning. At the same time, it can also avoid the situation where the vehicle appears in different positions on different systems, reducing the risk of missing the intersection due to the low positioning accuracy of the standard map.

[0132] In some embodiments, step S104 includes the following steps:

[0133] S1041. Determine the target lane based on the navigation guidance information and perception information in the navigation map.

[0134] For example, if the navigation prompt is "Please take the leftmost lane," then the target lane is the leftmost lane. As another example, if the navigation prompt is "Go straight ahead," and the sensory information indicates that there are multiple straight lanes ahead, then one of those straight lanes needs to be selected as the target lane.

[0135] S1042. Determine the current lane based on the perceived information.

[0136] Specifically, the perceived information includes lane information such as lane, lane lines, and lane center line, which can be used to determine the lane the vehicle is currently in.

[0137] S1043. Determine whether the vehicle needs to change lanes based on the target lane and the current lane.

[0138] Specifically, if the target lane is different from the current lane, it can be determined that the vehicle needs to change lanes; otherwise, it can be determined that the vehicle does not need to change lanes.

[0139] Optionally, step S1041 includes: when the navigation guidance information includes a recommended lane, determining the recommended lane as the target lane; when the navigation guidance information does not include a recommended lane, determining at least one passable lane based on the navigation guidance information and perception information; and determining the target lane from the at least one passable lane based on the navigation guidance information at the next intersection.

[0140] 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 second intersection while passing through the first intersection.

[0141] For example, the navigation guidance message might be "Go straight at the current intersection, turn right after 800 meters," with two guidance points: "current intersection" and "after 800 meters." In this case, the electronic device determines at least one straight lane based on "go straight at the current intersection" and the number of straight lanes at the current intersection.

[0142] When there is only one straight lane, that lane is designated as the target lane. When there are multiple straight lanes, the electronic equipment selects the rightmost lane among the multiple straight lanes as the target lane based on the rule of "turn right after 800 meters".

[0143] It is understandable that when there are multiple passable lanes, if the second guide is for a right turn, the rightmost passable lane is determined as the target lane; if the second guide is for a left turn or U-turn, the leftmost passable lane is determined as the target lane; if the second guide is for going straight, if the number of passable lanes is odd, the middle passable lane is determined as the target lane; if the number of passable lanes is even, the middle two lanes with less traffic are determined as the target lane.

[0144] 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.

[0145] 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:

[0146] Module 11 is used to determine the distance of the vehicle from the intersection;

[0147] The acquisition module 12 is used to acquire camera image data and radar point cloud data when the distance is less than a first distance threshold.

[0148] The determination module 11 is also used to determine the perception information based on the camera image data and radar point cloud data;

[0149] The judgment module 13 is used to determine whether the vehicle needs to change lanes based on the perception information.

[0150] The generation module 14 is used to generate lane change path planning information based on navigation map information, navigation positioning information and perception information when it is determined that the vehicle needs to change lanes. The lane change path planning information is used to control the vehicle to complete the lane change before the intersection.

[0151] The vehicle driving control device 10 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.

[0152] 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 20 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.

[0153] Map module 21 is used to obtain navigation map information and navigation positioning information from the standard map software development kit (SDK);

[0154] The perception module 22 is used to determine perception information based on camera image data and radar point cloud data;

[0155] The positioning module 23 is used to generate global positioning information and local positioning information based on navigation map information, navigation positioning information and perception information;

[0156] Planning module 24 is used to generate lane change path planning information based on navigation map information, navigation positioning information, perception information, global positioning information and local positioning information;

[0157] The control module 25 is used to control the vehicle to complete the lane change based on the lane change path planning information.

[0158] 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 and reducing per-vehicle costs.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The intelligent driving system 20 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.

[0163] 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.

[0164] 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, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0165] 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.

[0166] Alternatively, the memory 31 can be either standalone or integrated with the processor 32.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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 driving control method, characterized in that, The method includes: Determine the distance between the vehicle and the intersection; When the distance is less than a first distance threshold, acquire camera image data and radar point cloud data; Based on the camera image data and the radar point cloud data, the perception information is determined; Based on the perceived information, determine whether the vehicle needs to change lanes; When it is determined that a vehicle needs to change lanes, lane change path planning information is generated based on navigation map information, navigation positioning information, and the perception information. The lane change path planning information is used to control the vehicle to complete the lane change before the intersection.

2. The method according to claim 1, characterized in that, Based on navigation map information, navigation positioning information, and the perceived information, lane change path planning information is generated, including: Global positioning information is generated based on the navigation and positioning information and the perception information; Based on the navigation map information and the perception information, local positioning information is generated; Based on the global navigation path information and navigation guidance prompts in the navigation map information, as well as the global positioning information, lane change information is determined, including lane change speed and lane change angle. Based on the perceived information, the local positioning information, and the lane change information, lane-level lane change path planning information is determined.

3. The method according to claim 1 or 2, characterized in that, Determine the distance of the vehicle from the intersection, including: Based on navigation map information or navigation positioning information, predict the first distance of the vehicle from the intersection; Based on camera image data and radar point cloud data, perception information is determined, including road topology; Based on the road topology, predict the second distance of the vehicle from the intersection; 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.

4. The method according to claim 3, characterized in that, Based on whether an intersection exists in the road topology, the distance from the vehicle to the intersection is determined between the first distance and the second distance, including: When there is no intersection in the road topology, the first distance is determined to be the distance between the vehicle and the intersection; When an intersection exists 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.

5. The method according to claim 1 or 2, characterized in that, Based on the perceived information, determining whether a vehicle needs to change lanes includes: The target lane is determined based on the navigation guidance information in the navigation map and the perceived information. Based on the perceived information, determine the current lane; Based on the target lane and the current lane, determine whether the vehicle needs to change lanes.

6. The method according to claim 5, characterized in that, Based on navigation guidance information in the navigation map and the perceived information, the target lane is determined, including: When the navigation guidance prompt information includes a recommended lane, the recommended lane is determined to be the target lane; When the navigation guidance information does not include a recommended lane, at least one passable lane is determined based on the navigation guidance information and the perception information. Based on the navigation guidance information at the next intersection, the target lane is determined from the at least one passable lane.

7. The method according to claim 1 or 2, characterized in that, Before determining the distance of a vehicle from the intersection, the following steps are also taken: Obtain navigation map information or navigation location information; Determine the distance of the vehicle from the intersection, including: The distance between the vehicle and the intersection is determined based on the navigation guidance or positioning information in the navigation map information.

8. The method according to claim 1 or 2, characterized in that, After determining the distance of the vehicle from the intersection, the following also applies: When the distance between the vehicle and the intersection is greater than or equal to the first distance threshold, the vehicle is controlled to travel in the current lane.

9. A vehicle driving control device, characterized in that, include: The determination module is used to determine the distance of the vehicle from the intersection; The acquisition module is used to acquire camera image data and radar point cloud data when the distance is less than a first distance threshold; The determining module is further configured to determine perception information based on the camera image data and the radar point cloud data; The judgment module is used to determine whether the vehicle needs to change lanes based on the perceived information; The generation module is used to generate lane change path planning information based on navigation map information, navigation positioning information and the perception information when it is determined that the vehicle needs to change lanes. The lane change path planning information is used to control the vehicle to complete the lane change before the intersection.

10. An intelligent driving system, characterized in that, It includes a map module, a perception module, a positioning module, a planning module, and a control module; The map module is used to obtain navigation map information and navigation positioning information from a standard map software development kit; The perception module is used to determine perception information based on camera image data and radar point cloud data; The positioning module is used to generate global positioning information and local positioning information based on the navigation map information, the navigation positioning information and the perception information; The planning module is used to generate lane change path planning information based on the navigation map information, the navigation positioning information, the perception information, the global positioning information, and the local positioning information. The control module is used to control the vehicle to complete the lane change based on the lane change path planning information.

11. An electronic device, characterized in that, include: Memory and 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 1-8.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, is used to implement the vehicle driving control method as described in any one of claims 1-8.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the vehicle driving control method according to any one of claims 1-8.

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