Navigation method and apparatus, and vehicle

Abstract

A navigation method and apparatus, and a vehicle. The method comprises: acquiring navigation information and road topology information, wherein the navigation information indicates P roads a vehicle needs to pass from the current position where the vehicle is located to a target position, and a traveling direction, and the road topology information indicates a topological relationship between Q roads in a first region associated with the current position, and the number of lanes included in each road; on the basis of the navigation information and the road topology information, generating navigation field information, wherein the navigation field information indicates the passability corresponding to each of different positions of lanes in a target road, wherein the target road is the current road where the vehicle is located or a road the vehicle needs to pass when traveling to the target position; and on the basis of the navigation field information, controlling the vehicle to travel towards the target position. The solution can be applied to the field of intelligent driving of vehicles, such as electric vehicles and new energy vehicles, and can reduce the number of ineffective lane changes and / or the probability of going off-course during the traveling towards a destination, without relying on a high-precision map, thereby improving the traveling efficiency.

Description

Navigation methods, devices and vehicles

[0001] This application claims priority to Chinese Patent Application No. 202411987194.9, filed with the China National Intellectual Property Administration on December 30, 2024, entitled "Navigation Method, Apparatus and Vehicle", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of intelligent driving, and more specifically, to a navigation method, device, and vehicle. Background Technology

[0003] With the rapid development of the automotive industry, many driver assistance and autonomous driving technologies have emerged, which can reduce driving stress and improve safety and traffic efficiency. Currently, most autonomous driving technologies rely on high-precision maps for navigation. However, high-precision maps have drawbacks such as high collection and production costs, long processing times, insufficient coverage, and difficulty in ensuring data freshness, making it difficult to promote autonomous driving technologies that rely on high-precision maps nationwide or globally.

[0004] Intelligent vehicles use sensors such as cameras and LiDAR to detect static elements (such as lanes, roads, traffic lights, and signs) within a certain range around the vehicle in real time. This allows them to model the surrounding environment, enabling the vehicle to navigate in complex and ever-changing traffic conditions without relying on high-precision maps. However, the current vehicle sensors have limited sensing range, and the complex and ever-changing navigation rules during actual driving can lead to problems such as multiple lane changes, invalid lane changes, and even deviations from the intended path.

[0005] Therefore, a navigation solution that can improve vehicle driving efficiency and reduce unnecessary lane changes is urgently needed. Summary of the Invention

[0006] This application provides a navigation method, device, and vehicle that can reduce the number of invalid lane changes and / or the probability of veergence during the vehicle's journey to a target location without relying on high-precision maps, thereby improving the vehicle's driving efficiency.

[0007] Firstly, a navigation method is provided that can be executed by a vehicle, for example, by the vehicle's computing platform, or by a chip or circuitry used in the vehicle.

[0008] The method includes: acquiring navigation information and road topology information, wherein the navigation information indicates the P roads that the vehicle needs to traverse from its current location to a target location and the direction of travel; the road topology information indicates the topological relationship between Q roads within a first region associated with the current location, and the number of lanes contained in each of the Q roads, wherein the Q roads include P roads, and P and Q are both positive integers; generating navigation field information based on the navigation information and road topology information, wherein the navigation field information indicates the passability of different positions of at least one lane in the target road; wherein the target road is the current road where the vehicle is located, or the target road is the road that the vehicle needs to traverse to reach the target location; and controlling the vehicle to travel towards the target location based on the navigation field information.

[0009] In some implementations, the accessibility of a position within a lane can be characterized by the remaining drivable distance at that position. This remaining drivable distance can be understood as the distance a vehicle can continue traveling along the lane containing that position while traveling towards the target location; a larger remaining drivable distance indicates higher accessibility. Alternatively, the accessibility of a position within a lane can be characterized by the navigation cost at that position. This navigation cost can be understood as the cost required for a vehicle to travel from its current position to that position within the lane while traveling towards the target location. Alternatively, the accessibility of a position within a lane can also be characterized by other parameters.

[0010] In the above technical solution, road topology information can provide the topological relationship between roads beyond line of sight (i.e. beyond the vehicle's perception range), and navigation information can provide information on the driving direction. In this way, even without high-precision map guidance, the vehicle can obtain information including far-field information, enabling the vehicle to determine whether to change lanes and / or when to change lanes in complex road conditions, reducing the probability of invalid lane changes.

[0011] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: determining a set of key points based on road topology information, wherein each key point in the set of key points indicates the position of the lane centerline; generating navigation field information based on navigation information and road topology information, including: determining the value of each key point based on navigation information and road topology information, wherein the value of each key point indicates the passability of the corresponding location of the key point; and generating navigation field information based on the value of each key point.

[0012] In the above technical solution, determining navigation field information based on key point information obtained from road topology helps reduce the processing complexity and computational load required to generate navigation field information, and enables vehicles to drive towards the target location efficiently and human-like based on beyond-line-of-sight road topology information.

[0013] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: acquiring vehicle perception information, wherein the vehicle perception information indicates the boundary of each of the M lanes included in the target road, where M is a positive integer; determining N query points based on the vehicle perception information, wherein each of the N query points indicates a position on one of the N lanes, where M lanes include N lanes, and N is a positive integer; and determining the key point set based on road topology information, which includes: determining N sets of key points based on the N query points.

[0014] In some implementations, each set of key points includes two rows of key points, and the area between the two rows of key points indicates the possible location of the lane centerline in a real-world scenario.

[0015] There may be discrepancies between the road structure perceived by the vehicle's perception system and the road structure indicated by road topology information. When this discrepancy prevents the vehicle from accurately determining the target lane, it may frequently change lanes or select alternative routes. The above technical solution, based on N sets of key points, can determine the passability within the range of the lane centerline, improving the system's robustness. This ensures that the lane boundary perceived by the vehicle has a higher probability of falling within the range of the lane centerline determined by road topology information, reducing reliance on the stability of the perceived road structure. This allows the vehicle to accurately determine the target lane even when its field of vision is limited, enabling efficient travel to the target location.

[0016] In conjunction with the first aspect, in some implementations of the first aspect, the navigation field information includes multiple grids, where each grid point in the multiple grids indicates a road coordinate, and the value of each grid point indicates the degree of accessibility. The method also includes: determining the position of the multiple grids based on a set of key points.

[0017] In the above technical solution, a continuously arranged grid matrix is ​​obtained based on the set of key points, and navigation field information is generated based on the grid matrix, which helps to improve the continuity of lane-level guidance information (i.e., navigation field information).

[0018] In conjunction with the first aspect, in some implementations of the first aspect, the key point set includes a first key point, and navigation field information is generated based on the value of each key point, including: determining the values ​​of the grid points of the grid containing the first key point based on the value of the first key point.

[0019] In conjunction with the first aspect, in some implementations of the first aspect, P roads are associated with at least one intersection, the at least one intersection including a first intersection and a second intersection, and vehicles need to pass through the first intersection and the second intersection sequentially during their journey to the target location. The P roads include the first road, the end of which connects to the first intersection. Based on navigation information and road topology information, navigation field information is generated, including: determining the intersection navigation trend corresponding to each intersection in the at least one intersection based on the navigation information and road topology information, the intersection navigation trend of the first intersection indicating the probability of passage of at least one lane contained in the first road at the first intersection; determining the intersection navigation trend of the first intersection based on the intersection navigation trend of the second intersection; and generating navigation field information based on the intersection navigation trends of the first intersection and the second intersection.

[0020] In the above technical solution, determining the passability of each lane at a near-field intersection based on the passability of each lane at the far-field intersection helps to make the determined near-field navigation field information more convenient for vehicles to travel to the target location, reducing the probability of vehicles making unnecessary lane changes. For example, when a vehicle needs to turn left at the target intersection while traveling to the target location, the navigation information instructs the vehicle to move right first and then left, so that the vehicle can travel along the middle lane without making multiple unnecessary lane changes.

[0021] It should be noted that the far field involved in this application can be understood as a position that is far away from the vehicle and cannot be perceived by the vehicle's own perception system; the near field involved in this application can be understood as a position that is close to the vehicle and can be perceived by the vehicle's perception system.

[0022] In conjunction with the first aspect, in certain implementations of the first aspect, controlling the vehicle to travel toward the target location based on navigation field information includes: determining, based on the navigation field information, a navigation trend query value for each lane in the target road, wherein the navigation trend query value indicates the navigation cost of each lane, and / or the remaining drivable distance of each lane when the distance between the lane and the vehicle is a first distance; and controlling the vehicle to travel in the lane with the longest remaining drivable distance and / or the lowest navigation cost in the target road based on the navigation trend query value.

[0023] In the above technical solution, the vehicle can drive to the target location based on navigation field information. Without relying on high-precision maps, it reduces the number of invalid lane changes and / or the probability of yaw during the vehicle's journey to the target location. This reduces the number of times the human can take over the autonomous driving system and improves the vehicle's driving efficiency.

[0024] In conjunction with the first aspect, in some implementations of the first aspect, controlling the vehicle to travel toward the target location based on navigation field information includes: controlling the vehicle to travel toward the target location based on navigation field information when the offset between the boundary of the target road indicated by the navigation field information and the boundary of the target road perceived by the vehicle is less than or equal to a distance threshold; and / or controlling the vehicle to travel toward the target location based on navigation field information when the number of lanes of the target road indicated by the navigation field information is consistent with the number of lanes of the target road perceived by the vehicle.

[0025] In the above technical solution, when the road information perceived by the vehicle's perception system matches the road topology information, the vehicle's driving is controlled based on the navigation field information to prevent the vehicle from deviating due to real-world changes in the road.

[0026] Secondly, a navigation device is provided, comprising an acquisition unit and a processing unit. The acquisition unit is configured to: acquire navigation information and road topology information, wherein the navigation information indicates P roads that a vehicle needs to traverse from its current location to a target location and the direction of travel; the road topology information indicates the topological relationship between Q roads within a first area associated with the current location, and the number of lanes contained in each of the Q roads, wherein the Q roads include P roads, and P and Q are both positive integers; the processing unit is configured to: generate navigation field information based on the navigation information and the road topology information, wherein the navigation field information indicates the passability of different positions of at least one lane in the target road; wherein the target road is the current road where the vehicle is located, or the target road is the road that the vehicle needs to traverse to the target location; the processing unit is further configured to: control the vehicle to travel towards the target location based on the navigation field information.

[0027] In conjunction with the second aspect, in some implementations of the second aspect, the processing unit is further configured to: determine a set of key points based on road topology information, wherein each key point in the set of key points indicates the position of the lane centerline; determine the value of each key point based on navigation information and road topology information, wherein the value of each key point indicates the passability of the corresponding location of the key point; and generate navigation field information based on the value of each key point.

[0028] In conjunction with the second aspect, in some implementations of the second aspect, the acquisition unit is further configured to: acquire vehicle perception information, which indicates the boundary of each of the M lanes included in the target road, where M is a positive integer; determine N query points based on the vehicle perception information, where each of the N query points indicates a position on one of the N lanes, where M lanes include N lanes and N is a positive integer; and determine N sets of key points, where each set of key points indicates the position range of the centerline of one lane based on the N query points.

[0029] In conjunction with the second aspect, in some implementations of the second aspect, the navigation field information includes multiple grids, where each grid point in the multiple grids indicates a road coordinate, and the value of each grid point indicates the degree of accessibility. The processing unit is also used to: determine the position of the multiple grids based on the set of key points.

[0030] In conjunction with the second aspect, in some implementations of the second aspect, the key point set includes a first key point, and the processing unit is used to: determine the values ​​of the grid points of the grid containing the first key point based on the values ​​of the first key point.

[0031] In conjunction with the second aspect, in some implementations of the second aspect, P roads are associated with at least one intersection, and the at least one intersection includes a first intersection and a second intersection. Vehicles traveling towards a target location must sequentially pass through the first intersection and the second intersection. The P roads include a first road, the end of which connects to the first intersection. The processing unit is used to: determine the intersection navigation trend corresponding to each intersection in the at least one intersection based on navigation information and road topology information; the intersection navigation trend of the first intersection indicates the probability of passage for at least one lane of the first road at the first intersection; determine the intersection navigation trend of the first intersection based on the intersection navigation trend of the second intersection; and generate navigation field information based on the intersection navigation trends of the first and second intersections.

[0032] In conjunction with the second aspect, in some implementations of the second aspect, the processing unit is used to: determine the navigation trend query value of each lane in the target road based on the navigation field information, wherein the navigation trend query value indicates the navigation cost of each lane, and / or the remaining drivable distance of each lane when the distance between it and the vehicle is a first distance; and control the vehicle to drive in the lane with the longest remaining drivable distance and / or the lowest navigation cost in the target road based on the navigation trend query value.

[0033] In conjunction with the second aspect, in some implementations of the second aspect, the processing unit is used to: control the vehicle to travel toward the target location based on the navigation field information when the offset between the boundary of the target road indicated by the navigation field information and the boundary of the target road perceived by the vehicle is less than or equal to a distance threshold; and / or control the vehicle to travel toward the target location based on the navigation field information when the number of lanes of the target road indicated by the navigation field information is consistent with the number of lanes of the target road perceived by the vehicle.

[0034] Thirdly, a navigation device is provided, the device comprising: a processor for executing a computer program stored in the memory, such that the device performs the method in any possible implementation of the first aspect described above.

[0035] In conjunction with the third aspect, in some implementations of the third aspect, the device also includes a memory.

[0036] Fourthly, a computer program product is provided, comprising: computer program code, which, when executed on a computer or processor, causes the computer or processor to perform the method in any possible implementation of the first aspect.

[0037] It should be noted that the above computer program code can be stored in whole or in part on a storage medium, which can be packaged together with the processor or packaged separately from the processor.

[0038] Fifthly, a computer-readable storage medium is provided, the computer-readable medium storing instructions that, when executed by a processor, cause the processor to implement the method in any possible implementation of the first aspect.

[0039] In a sixth aspect, a chip is provided, the chip including circuitry for performing the method in any of the possible implementations of the first aspect described above.

[0040] In a seventh aspect, a vehicle is provided that includes means as in any possible implementation of the second or third aspect, or the vehicle includes a computer-readable storage medium as in any possible implementation of the fifth aspect, or the vehicle includes a chip as in any possible implementation of the sixth aspect, or the vehicle is loaded with a computer program product as in any possible implementation of the fourth aspect.

[0041] In conjunction with the seventh aspect, in some implementations of the seventh aspect, the vehicle is a vehicle in a broad sense, such as a means of transportation (e.g., commercial vehicles, passenger cars, motorcycles, flying cars, trains, etc.), industrial vehicles (e.g., forklifts, trailers, tractors, etc.), engineering vehicles (e.g., excavators, bulldozers, cranes, etc.), agricultural equipment (e.g., lawnmowers, harvesters, etc.), amusement equipment, toy vehicles, etc. In practical implementation, the vehicle can also be a road vehicle, a water vehicle, an air vehicle, industrial equipment, agricultural equipment, or other intelligent driving equipment such as entertainment equipment.

[0042] For the beneficial effects not described in detail in aspects two through seven, please refer to the description in aspect one, which will not be repeated here. Attached Figure Description

[0043] Figure 1 is a functional block diagram of the vehicle provided in an embodiment of this application;

[0044] Figure 2 is a schematic diagram of the autonomous driving system architecture provided in an embodiment of this application;

[0045] Figure 3 is a schematic flowchart of the navigation method provided in an embodiment of this application;

[0046] Figure 4 is a schematic diagram of the application scenario of the navigation method involved in the embodiments of this application;

[0047] Figure 5 is a schematic diagram of the road topology information and navigation information involved in the embodiments of this application;

[0048] Figure 6 is another schematic diagram of the visualization of road topology information and navigation information involved in the embodiments of this application;

[0049] Figure 7 is a schematic diagram of the relationship between grid points and key points in the navigation field involved in the embodiments of this application;

[0050] Figure 8 is a schematic diagram of the relationship between grid points and shape points in the navigation field according to an embodiment of this application;

[0051] Figure 9 is a schematic diagram of the relationship between grid points and query points in the navigation field involved in the embodiments of this application;

[0052] Figure 10 is a schematic diagram of the relationship between grid points, key points and query points in the navigation field involved in the embodiments of this application;

[0053] Figure 11 is another schematic flowchart of the navigation method provided in the embodiments of this application;

[0054] Figure 12 is a schematic block diagram of a navigation device provided in an embodiment of this application;

[0055] Figure 13 is another schematic block diagram of the navigation device provided in the embodiments of this application. Detailed Implementation

[0056] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0057] Figure 1 is a functional block diagram of a vehicle provided in an embodiment of this application. As shown in Figure 1, the vehicle 100 may include a perception system 120 and a computing platform 150. The perception system 120 may include several sensors for sensing information about the surrounding environment of the vehicle 100. For example, the perception system 120 may include a positioning system, which may be a Global Positioning System (GPS), a BeiDou system, or other positioning systems. As another example, the perception system 120 may also include one or more of the following: an inertial measurement unit (IMU), a lidar, a millimeter-wave radar, an ultrasonic radar, and a camera device.

[0058] Some or all of the functions of vehicle 100 can be controlled by computing platform 150. Computing platform 150 may include processors 151 to 15n. A processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a central processing unit (CPU), microprocessor, graphics processing unit (GPU) (which can be understood as a type of microprocessor), or digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. These logical relationships are fixed or reconfigurable. For example, the processor may be a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as a field-programmable gate array (FPGA). In reconfigurable hardware circuits, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement related functions. Furthermore, the processor can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), tensor processing unit (TPU), deep learning processing unit (DPU), etc. In addition, the computing platform 150 may also include a memory for storing instructions. Some or all of the processors 151 to 15n can call the instructions in the memory to implement the corresponding functions.

[0059] The computing platform 150 can control the operation of the intelligent driving system, which may include an advanced driving assistance system (ADAS) and an autonomous driving system (ADS). The intelligent driving system utilizes various sensors on the vehicle (including but not limited to: LiDAR, millimeter-wave radar, cameras, ultrasonic sensors, GPS, and inertial measurement units) to acquire information from the vehicle's surroundings, and analyzes and processes this information to achieve functions such as obstacle perception, target recognition, vehicle localization, path planning, and driver monitoring / alerts, thereby improving the safety, automation, and comfort of driving the vehicle.

[0060] At different levels of autonomous driving (or intelligent driving levels, ranging from L0 to L5, totaling six levels), intelligent driving systems can achieve different levels of automated driving assistance based on artificial intelligence algorithms and information acquired by multiple sensors. These levels of autonomous driving are based on the classification standards of the Society of Automotive Engineers (SAE). Specifically, L0 is no automation; L1 is driver assistance; L2 is partial automation; L3 is conditional automation; L4 is high automation; and L5 is full automation. At levels L1 to L3, the task of monitoring road conditions and reacting is jointly completed by the driver and the system, requiring the driver to take over dynamic driving tasks. Levels L4 and L5 allow the driver to completely transform into a passenger. Currently, the functions that intelligent driving systems can achieve mainly include, but are not limited to: adaptive cruise control, automatic emergency braking, automatic parking, blind spot monitoring, forward cross-traffic alert / braking, rear cross-traffic alert / braking, forward collision warning, lane departure warning, lane keeping assist, rear collision warning, traffic sign recognition, traffic jam assist, and highway assist. It should be understood that the above-mentioned functions can have specific modes at different levels of autonomous driving (L0-L5). The higher the level of autonomous driving, the more intelligent the corresponding mode.

[0061] The roles of the perception system 120 and the computing platform 150 in this application are explained in detail below with reference to Figure 2. Figure 2 shows a schematic block diagram of the autonomous driving system architecture provided in an embodiment of this application. The system includes a map information acquisition module 210, a navigation information acquisition module 220, a topology generation module 240, a perception module 240, and a planning and control module 250. The map information acquisition module 210, the navigation information acquisition module 220, the topology generation module 240, and the planning and control module 250 may each include one or more processors from the computing platform 150 shown in Figure 1. The perception module 240 may include one or more camera devices from the perception system 120 shown in Figure 1, or it may also include one or more radars from the perception system 120. The roles of each module in the system shown in Figure 2 are described in items (I) to (V) below.

[0062] (i) Map information acquisition module 210: used to acquire road topology information, which indicates the topological relationship between multiple roads within a certain area, and the number of lanes included in each road; or, the road topology information can also indicate the changes in the number of lanes in each road, and the location where the number of lanes changes.

[0063] In some implementations, road topology information can be information indicating historical road topology. For example, road topology information can include pre-made map data or information extracted from pre-made map data. The pre-made map data can be roadcode (RC) maps, electric horizon (EHP) data, etc.

[0064] In some implementations, pre-built map data can be generated based on traffic flow data. Traffic flow data can be understood as data consisting of the trajectories formed by one or more vehicles traveling on the road. A set of traffic flow data can include multiple traffic flow points, each indicating a coordinate in a vehicle's trajectory, the time the vehicle arrived at that coordinate, and the vehicle's orientation and pose at that coordinate.

[0065] Pre-built map data can include at least one of road vectors, intersection vectors, and lane vectors. For example, a processor segments and clusters traffic flow data to obtain road vectors. Further, for multiple roads intersecting at the same intersection, based on traffic flow data and road vectors, the vector points connecting each road to the intersection are determined, and the vector points corresponding to multiple roads constitute the intersection vector. The road width is determined based on traffic flow data, and the intersections of multiple sets of traffic flow data with the perpendicular lines from the roads are clustered. The number of lanes is determined based on the clustering results, and then the lane vectors are determined based on the road width and the number of lanes. It can be understood that lane vectors, road vectors, and intersection vectors constitute a vectorized map. Specifically, road vectors indicate the location and direction of a road segment, intersection vectors indicate the location and boundaries of intersections, and lane vectors indicate the roadway for various vehicles to travel within the same width. Alternatively, lane vectors can also indicate the position of each lane within a road segment.

[0066] It should be noted that there is a topological relationship between the two roads involved in this application, which can be understood as: the two roads are connected at a certain intersection, so that vehicles can enter the other road from one of the two roads through the intersection.

[0067] (II) Navigation Information Acquisition Module 220: Used to acquire navigation information, which indicates the road-level driving direction from the vehicle's current location to the target location. The road-level driving direction includes the vehicle's driving direction or deviation within a road, and may also include the vehicle's driving direction at an intersection between two roads. The driving deviation indicates whether the vehicle is driving closer to the left side of the road, closer to the right side, or in the center. For example, the vehicle's driving deviation within a road can be determined based on the driving direction at the intersection where the road ends. For instance, if the driving direction at the intersection is straight, the vehicle's driving deviation within the road can be in the center; or, if the driving direction at the intersection is a left turn, the vehicle's driving deviation within the road can be closer to the left side. When the road includes a destination, the vehicle's driving deviation within the road can be determined based on the location of the destination within the road. For example, if the destination is on the right side of the road, the vehicle can travel towards the right side of the road; or if the destination is on the left side of the road, the vehicle can travel towards the left side of the road.

[0068] For example, the left or right side of the road can be determined relative to the vehicle's coordinate system. The positive direction of the Y-axis of the vehicle coordinate system points to the left side of the road, and the negative direction of the Y-axis of the vehicle coordinate system points to the right side of the road. The origin O of the vehicle coordinate system can be located at the projection point of the rear axle center of the vehicle body onto the ground. The positive directions of the X-axis and Z-axis are the direction of the vehicle's front and the direction perpendicular to the vehicle's plane, respectively.

[0069] For example, the navigation information can be SD map navigation information determined according to the standard definition (SD) map.

[0070] (iii) Perception module 230: used to acquire environmental information around the vehicle and send it to the planning and control module 250. The environmental information may include the road boundary, lane boundary, etc. of the road where the vehicle is currently traveling.

[0071] (iv) Navigation Field Generation Module 240: This module generates a navigation field that indicates the navigation trend associated with each lane along the road the vehicle must traverse to reach the target location. The navigation trend indicates the remaining drivable distance and / or navigation cost at different locations within the lane. Specifically, the navigation field generation module 240 includes a navigation trend calculation module 241 and a navigation field generation module 242. The navigation trend calculation module 241 generates topology guidance information based on navigation information and road topology information, indicating the navigation trend of each lane at intersections. The navigation field generation module 242 generates key points based on road topology information, indicating the positions of lane centerlines and / or lane boundaries, and then generates the navigation field based on the key points and topology guidance information. In some implementations, the navigation field generation module 242 can also generate key points based on environmental information obtained from the perception module 230, and then generate the navigation field based on the key points and topology guidance information.

[0072] (v) Planning and Control Module 250: This module generates query points based on environmental information from the perception module 230. Based on the query points and the generated navigation field, it determines the navigation trends of each lane along the road the vehicle must traverse to reach the target location. Then, it determines the target lane for the vehicle and plans the vehicle's travel path accordingly. Further, the planning and control module 250 can calculate relevant control quantities based on the planned travel path and output these control quantities to the actuator. When the actuator executes the aforementioned control quantities, it can control the vehicle to travel along the planned travel path. For example, the actuator may include the steering and braking control system in the vehicle 100.

[0073] It should be understood that the above modules are only an example, and in actual applications, these modules may be added or removed according to actual needs. For example, in the system architecture shown in Figure 2, the navigation field generation module 240 and the planning and control module 250 can be merged into one module; as another example, the map information acquisition module 210, the navigation information acquisition module 220, the navigation field generation module 240, and the planning and control module 250 can all be merged into one module.

[0074] The above describes the autonomous driving system architecture provided in the embodiments of this application. The following details the navigation method provided in the embodiments of this application based on the autonomous driving system shown in Figure 2.

[0075] Figure 3 shows a schematic flowchart of a navigation method provided in an embodiment of this application. This method 300 can be applied to the vehicle shown in Figure 1, or it can be executed by the system shown in Figure 2. More specifically, this method 300 can be executed by the navigation field generation module 240 and the planning and control module 250, and it may include:

[0076] S310, obtain navigation information and road topology information. The navigation information indicates the road-level driving direction from the vehicle's current location to the target location. The road topology information indicates the topological relationship between multiple roads within area 1 associated with the current location, as well as the number of lanes contained in each of the multiple roads.

[0077] For example, the methods for obtaining navigation information and road topology information can refer to the description in the foregoing embodiments, and will not be repeated here. Region 1 can be a region including the current location, or Region 1 can be a region at a certain distance from the current location (such as a distance between 10 and 30 meters). For example, Region 1 can be a rectangular region with a length of n meters and a width of m meters, or Region 1 can be a circular region with a diameter of m meters, or Region 1 can be a region of other shapes. For example, n can be a value between 500 and 1000, m can be a value between 10 and 20, or n and m can be other values.

[0078] In some implementations, road topology information includes multiple shape points indicating the positions of lanes within the road, with each shape point indicating a location on the lane centerline. When the number of lanes changes, the road topology information can also indicate the location of the lane change.

[0079] For example, Figure 4 shows an example of an application scenario involved in this application. If a vehicle needs to pass through intersection a to intersection e in sequence from its current location to its destination, the navigation information at least indicates the vehicle's driving direction at intersection a, and the road topology information at least indicates the road connected to intersection a and the road direction.

[0080] It should be noted that the intersections involved in this application may include, but are not limited to, cross intersections, off-ramp connection intersections, on-ramp connection intersections, intersections leading to auxiliary roads, and intersections merging into main roads. Among them, a cross intersection can be an n-way intersection, where n is an integer greater than or equal to 3. That is, each intersection includes at least three boundaries, and each of the at least three boundaries connects to a road.

[0081] S320 determines topology guidance information based on navigation information and road topology information, the topology guidance information indicating the navigation trend of lanes contained in at least one road at intersections.

[0082] For example, at least one road is the route that a vehicle needs to take to travel to a target location, and any two adjacent roads in the at least one road are connected by an intersection.

[0083] In some implementations, based on navigation information and road topology information, the driving direction of the vehicle at each of the N1 target intersections and the number of lanes contained in the preceding road of that intersection are determined. These N1 target intersections can be some or all of the intersections the vehicle needs to pass through on its journey to the target location, where N1 is a positive integer. The preceding road of a given intersection refers to the nearest neighbor road the vehicle must pass through on its journey to that intersection. For example, if a vehicle travels from road 1 through intersection 1 to road 2, and road 1 and intersection 1 are connected, then road 1 is the preceding road of intersection 1.

[0084] In some implementations, the vehicle's driving direction on a particular road can be determined based on navigation information and road topology information. For example, in the navigation scenario shown in Figure 4, it can be determined that the vehicle will drive on the right side of the road following intersection e to reach its destination. Here, the following road at an intersection refers to the nearest neighboring road that the vehicle enters after passing through that intersection. For example, if a vehicle travels from road 1 through intersection 1 to road 2, and road 2 connects to intersection 1, then road 2 is the following road of intersection 1.

[0085] For example, the visualization of the road topology information corresponding to the navigation scenario shown in Figure 4 can be represented by the dashed box on the left in Figure 5, indicating the road topology relationships at intersections a, b, and c, as well as the number of lanes on each road. It is understood that the road topology relationship at each intersection can indicate the two or more roads connected to that intersection, and the direction of each road. Furthermore, the road topology information can also include the road topology relationships at other intersections. The visualization of the navigation information can be represented by the dashed box on the right in Figure 5, indicating the driving directions of vehicles at intersections a, b, and c: right turn, left turn, and straight ahead, respectively. It is understood that the navigation information can also indicate the driving directions at other intersections. If road 1 changes from a 2-lane road to a 4-lane road, and road 2 changes from a 3-lane road to a 4-lane road, the visualization result after fusing the navigation information and road topology information can be shown in Figure 6. That is, it can obtain the number and location of lanes on each road, the location where the number of lanes changes, and the driving direction of vehicles at the intersections.

[0086] Furthermore, based on the vehicle's direction of travel at each target intersection or the vehicle's deviation on the road, as well as the number of lanes contained in the preceding road at the target intersection, the initial navigation trend at each target intersection is determined.

[0087] For example, for each target intersection, determining its initial navigation trend may specifically include the following steps (a) and (b):

[0088] (a) Determine the accessibility of different sections of the preceding road at the target intersection.

[0089] In one example, when lane guidance (i.e., the drivable direction of vehicles supported by the lane at the intersection) cannot be determined, the portion of the preceding road connecting to the target intersection can be divided into M1 equal parts along the width direction of the preceding road, for example, into 60 equal parts. This M1 division can be further divided into six parts, each consisting of 10 equal parts; or, this M1 division can be further divided into ten parts, each consisting of 6 equal parts. Further, the drivability of each part is determined based on the vehicle's drivable direction at the intersection. For example, if the navigation information determines that the vehicle's drivable direction at the intersection is turning towards a first side (e.g., left or right), the drivability of the preceding road closest to the first side at the intersection can be determined to be 100%, and the drivability of the remaining half can be determined to be 0%. The portion with a 100% drivability can be considered a drivable portion. For example, when navigation information indicates that the vehicle is going straight at an intersection, it can be determined that the passability of the preceding road in the middle 4 / 5 of the intersection is 100%, and the passability of the remaining 1 / 10 on each of the left and right sides is 0%.

[0090] In another example, if lane guidance can be determined, each lane can be divided into multiple parts of the preceding road to the target intersection based on the guidance of each lane. For example, the preceding road includes lanes 1 to 3, where lane 1 supports going straight and turning left at the nearest intersection, lane 2 only supports going straight at the nearest intersection, and lane 3 supports going straight, turning right, and making a U-turn at the nearest intersection. For example, lane 1 can be divided into two equal parts, each occupying 1 / 6 of the width of the preceding road at the target intersection, for vehicles to turn left and vehicles to go straight, respectively; since lane 2 only supports straight travel, lane 2 is not further divided, that is, lane 2 occupies 1 / 3 of the width of the preceding road at the target intersection; lane 3 can be divided into three equal parts, each occupying 1 / 9 of the width of the preceding road at the target intersection, for vehicles to go straight, turn right and make a U-turn, respectively; since lane 2 only supports straight travel, lane 2 is not further divided, that is, lane 2 occupies 1 / 3 of the width of the preceding road at the target intersection. Furthermore, when a vehicle needs to proceed straight through the target intersection, the passable portion of the preceding road includes 50% of lane 1, 100% of lane 2, and 33.3% of lane 3; when a vehicle needs to turn left at the target intersection, the passable portion of the preceding road includes 50% of lane 1; and when a vehicle needs to turn right or make a U-turn at the target intersection, the passable portion of the preceding road includes 33.3% or 66.7% of lane 3.

[0091] (b) Smooth the accessibility and obtain the navigation cost based on the smoothed result, which can characterize the navigation trend.

[0092] Taking an intersection where the preceding road has three lanes, its width is divided into 60 equal parts, and vehicles turn right at the intersection where the preceding road connects, as an example, the passability of the preceding road can be converted into the following form: [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0 The expression is: .0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0, where each of the above terms represents the probability of passage of an equal division of the preceding road. A probability of 0.0 indicates that the road is impassable, and a probability of 1.0 indicates that the road is passable. From left to right, these terms represent the equal divisions of the preceding road from the left to the right.

[0093] In some implementations, the above passage probability can be smoothed, for example, by using the following formula (1):

[0094] Where, σ 2 The variance σ is determined by the distance between each intersection and the vehicle's current location, based on the SD map navigation information. The greater the distance, the smaller the variance. For example, the variance σ in formula (1) can be determined using the following formula (2). 2 The specific value.

[0095] Where dis represents the distance between the vehicle and the intersection of the preceding road indicated by the navigation information. σ0 is the baseline standard deviation, which can be 1.76 or other calibrated values; dis1 and dis2 are distance thresholds, which can be 0.5 km and 1 km respectively, or other calibrated values. For example, when driving on urban roads, dis1 and dis2 take the above values, and when driving on highways, dis1 and dis2 take 1 km and 2 km respectively; k, μ1, and μ2 are coefficients, which can be -2, 0.88, and -0.5 respectively, or other calibrated values.

[0096] For example, if the distance between the target intersection and the vehicle is 101 meters (i.e., 0.101 kilometers), the variance σ in formula (1) can be determined according to formula (2). 2 It is 2.5. Furthermore, inputting the above passage probability 1 into formula (1) yields the smoothed passage probability: [0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.05 0.11 0.19 0.30 0.43 1.14 1.14 1.14 1.14 1.14 ...

[0097] Furthermore, the smoothed passage probability is scaled to three lanes, resulting in passage probabilities of 0.0, 0.07, and 1.0 for the three lanes from left to right. Then, the passage probabilities of each lane are mapped using a pre-defined function (e.g., formula (3)) to obtain navigation costs of 4000, 2000, and 0 for the three lanes from left to right. The higher the navigation cost of a lane, the lower its recommended driving level.

[0098] Where p is the actual passage probability, p0 and p1 are the passage probability thresholds, which can be 0.05 and 0.9 respectively, or other calibrated values. cost0 and cost1 represent the navigation cost, which can be 4000 and 0 respectively, or other calibrated values. a and b are coefficients, which can be 100 and 2000 respectively, or other calibrated values.

[0099] After determining the initial navigation trend at each of the N1 target intersections, in one example, the initial navigation trends of the N1 target intersections can be superimposed to obtain the final navigation trend for each target intersection. For example, starting from the initial navigation trend corresponding to the target intersection farthest from the current location of the vehicle, the trends of the target intersections closer to the current location are superimposed sequentially, and the navigation trends of at least one target intersection close to the current location are corrected in turn to obtain the final navigation trend.

[0100] In another example, the passability probabilities corresponding to two adjacent navigation events (such as the direction of travel of a vehicle at an intersection) can be superimposed. The passability probability corresponding to a navigation event farther from the vehicle is nested within the passable interval of a navigation event closer to the vehicle, and this superposition operation yields the superposition of the two navigation events. For example, if each intersection is divided into 60 equal parts along the road width, and intersection 2 and intersection 1 are the intersections a vehicle must pass sequentially to reach its target location, then if the left half (i.e., 30 equal parts) of intersection 2 is the passable portion, then the passability probability b of intersection 2 can be obtained as follows: [1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 ,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0 ... [0.0,0.0,0.0,0.0,0.0,0.0,0.0], representing the equal divisions from left to right of intersection 2; if the right 1 / 6 (or 10) of intersection 1 is a passable portion, then the passability probability 'a' of intersection 1 can be obtained as follows: [0.0, ... ,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0], from left to right, represent the equal divisions from the left to the right of intersection 1. For example, taking the distance between intersection 1 and intersection 2 as 1159 meters, the variance σ in formula (1) is determined according to formula (2). 2The value can be 0.58. Inputting the passage probability 'a' into the above formula (1) yields the smoothed passage probability 'a': [0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.04 0.04 0.04 0.04 0.05 0.05 0.05 0.06 0.06 0.06 0.07 0.07 0.07 0.08 0.08 0.09 0.09 0.09 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.13 0.13 0.13 0.14 0.14 0.14 0.15] 0.15 0.15 0.15 0.16 0.16 0.16 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18], since the vehicle passage interval corresponding to intersection 2 is half to the left of intersection 2, after scaling the smoothed passage probability a proportionally to the effective interval of navigation event b (i.e., half to the left of intersection 2), the scaled passage probability a can be obtained as: [0.01 0.02 0.02 0.02 0.03 0.03 0.04 0.04 0.05 0.06 0.06 0.07 0.08 0.08 0.09 0.10 0.11 0.12 0.12 0.13] [0.14 0.15 0.15 0.16 0.16 0.18 0.18 0.18 0.18 0.18], that is, the scaled-up probability of passage a is the probability of passage corresponding to the left 1 / 2 of intersection 2.After superimposing and normalizing the scaled traffic probabilities 'a' and 'b', the traffic probability 'c' corresponding to intersection 2, resulting from intersection 1 acting on intersection 2, is obtained: [0.08 0.08 0.10 0.11 0.14 0.16 0.20 0.22 0.27 0.32 0.36 0.38 0.42 0.45 0.49 0.55 0.58 0.65 0.71 0.75 0.77 0.82 0.85 0.86 0.88 0.96 1.00 1.00 1.00 1.00 0 ... 0.00 0.000.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00], where the scaling probability a and probability b are superimposed, which can be understood as multiplying the 30 elements of scaling probability a by the first 30 elements of probability b. Further, the navigation cost corresponding to intersection 2 can be obtained through the probability c and the aforementioned formula (3).

[0101] In some implementations, topology guidance information can also indicate navigation trends for different positions of at least one lane in a road. In one example, after determining the navigation trends of N1 target intersections, the navigation trends for different positions of at least one lane in the road between any two adjacent intersections can be determined based on the navigation trends of those two adjacent intersections. In another example, during the overlay process of the initial navigation trends of the N1 target intersections, the navigation trends for different positions of at least one lane in the road between any two adjacent intersections can be determined based on the navigation trends of those two adjacent intersections.

[0102] In actual implementation, some of the N1 target intersections may lack navigation information. In this case, the navigation information of the vehicle at the intersection can be deduced based on the vehicle's target location and the subsequent roads of the intersection with missing navigation information.

[0103] S330 generates a navigation field based on topology guidance information. The navigation field includes multiple grids, and the grid points of each grid indicate a position in the lane. The values ​​of the grid points indicate the navigation trend.

[0104] In some implementations, road topology information within a certain range can be obtained based on the vehicle's current location, and key points can then be generated based on the shape points included in the road topology information. This certain range can be within 1 to 1.5 kilometers of the vehicle, or it can be other ranges determined by the vehicle's speed; for example, this range expands as the vehicle's speed increases.

[0105] For example, road topology information may include a sparse set of key points. For each key point, it is extended laterally to the left and right by a certain distance (e.g., a value between 0.5 meters and 0.75 meters) to obtain two key points located to the left or right of that key point. Then, it is interpolated longitudinally to obtain a dense set of key points. Here, the longitudinal direction is parallel to the road boundary (or lane centerline), and the lateral direction is perpendicular to the road boundary (or lane centerline).

[0106] Furthermore, a grid matrix is ​​generated based on the location of the key points, covering the locations where the key points exist. For example, the left dashed box in Figure 7 illustrates the positional relationship between key points, lane center positions, and grid points in the grid matrix. Further, the navigation trend corresponding to each key point is determined, and values ​​are assigned to the grid points of the grid containing that key point based on the navigation trend. For example, when the topology guidance information indicates the navigation trend at an intersection, the corresponding navigation trend can be determined based on the distance between the key point and the intersection; or, when the topology guidance information also indicates the navigation trend at different locations within the lane, the navigation trend of that key point can be determined based on the navigation trends of its neighboring locations. After assigning values ​​to the grid points, the values ​​of grid points at different locations in the grid matrix differ. The visualization of the grid matrix can be shown in the left dashed box in Figure 7. Darker colored grid points represent higher navigation costs at their corresponding locations and / or shorter remaining drivable distances.

[0107] After assigning values ​​to the grid points of the grid containing key points, values ​​are then assigned to the grid points of the grid without key points based on the assigned values, thus obtaining the navigation field. For example, a portion of the navigation field is visualized within the dashed box on the right side of Figure 7. Further, taking the vehicle's current position as shown in Figure 4, and the vehicle needing to travel to the target location via roads 1 and 2 as shown in Figure 6, the relationship between the shape points associated with roads 1 and 2, the navigation field generated based on the shape points and navigation information, and the vehicle's current position can be illustrated in Figure 8. In this context, the darker the color of a grid point in the navigation field, the higher the navigation cost at that location; correspondingly, traveling to the target location is less recommended.

[0108] In some implementations, grid points of a raster that does not contain keypoints can be assigned values ​​based on the following formula (4):

[0109] Where f(x,y) represents the assigned values ​​of the grid points, u represents the values ​​of the grid points at different positions (i.e., the navigation trend), and x and y represent the horizontal and vertical directions, respectively. Let Ω be the boundary of region Ω. Indicates boundary conditions.

[0110] S340, acquire environmental perception information, which indicates the lane boundaries and / or lane center lines of the road where the vehicle is currently located.

[0111] For example, the environmental perception information can be an image including pixels of lane boundaries and / or lane centerlines, or it can be point cloud data including point clouds of lane boundaries and / or lane centerlines. Further, image processing can be performed on the environmental perception information to determine the lane boundary position of the vehicle's current location on the road.

[0112] S350 determines M2 query points based on environmental perception information, and each of the M2 query points corresponds to one of the M2 lanes of the target road.

[0113] For example, the target road may include the road where the vehicle is currently located, or the target road may also include the next road after the nearest intersection that the vehicle needs to pass through during its journey to the target location.

[0114] For example, each of the M2 query points can indicate a position on the center line of the lane, and the distance between each of the M2 query points and the current position of the vehicle can be greater than or equal to distance 1 and less than or equal to distance 2. For example, distance 1 can be a value between 30 meters and 50 meters, and distance 2 can be a value between 50 meters and 100 meters, or distance 1 and distance 2 can be other values.

[0115] S360 determines the navigation trend corresponding to each lane in M2 lanes based on the M2 query points and navigation field, and controls the vehicle to drive towards the target location according to the aforementioned navigation trend.

[0116] For example, for each query point, its value is determined based on the values ​​of the four grid points of the raster in its navigation field. This value represents the navigation trend of the query point's location. For instance, the positional relationship between the query point and the raster in the navigation field can be shown in Figure 9, where each grid point within a dashed box in Figure 9 is used to determine the value of the query point within that box. For example, the query point's value can be determined using bilinear interpolation based on the values ​​of the four grid points of the raster where the query point is located.

[0117] Furthermore, based on the values ​​of the M2 query points, the system controls the vehicle to either stay in the current lane or switch to another lane. Here, M2 is a positive integer.

[0118] For example, a target lane can be determined based on navigation trends, and the vehicle can be controlled to change to the target lane. The target lane can be the lane with the highest drivability at the first horizontal line among multiple lanes on the target road; or, the target lane can be a lane among multiple lanes with a drivability at the first horizontal line greater than or equal to a certain threshold. The first horizontal line can be a straight line perpendicular to the lane boundaries in the target road.

[0119] In some implementations, the method further includes: acquiring obstacle information, which indicates the position and / or speed of obstacles in the current road; determining the recommendation level of each lane based on the obstacle information and the navigation trend of each lane; and controlling the vehicle to travel in the current lane or change to another lane based on the recommendation level. The recommendation level indicates how suitable each lane in the road the vehicle must traverse to travel towards the target location is for the vehicle.

[0120] For example, the vehicle can be controlled to switch to a lane with a higher or highest recommendation level. More specifically, when a lane with a higher recommendation level includes multiple lanes, a target lane can be determined based on the difficulty of switching to a lane with a higher recommendation level, and the vehicle can be controlled to switch to the target lane. The difficulty of switching lanes can be measured by the number of lane changes; the more lane changes, the higher the difficulty. Alternatively, the difficulty of switching lanes can also be measured in other ways.

[0121] In some scenarios, if the target road is the vehicle's current road, at least one lane is selected from multiple lanes on the target road, and the passability of this lane at the first horizontal line is greater than or equal to the passability of the vehicle's current lane. If the target road is a road the vehicle needs to travel to a target location, at least one lane is selected from multiple lanes on the target road, and the passability of this lane at the first horizontal line is greater than or equal to a certain threshold. Then, based on whether there are obstacles in this at least one lane, the recommendation level of each lane is determined. The recommendation level of lanes on the target road other than the selected at least one lane can be set to the lowest. Furthermore, if there are stationary or slowly moving obstacles in one of the selected at least one lanes, the recommendation level of that lane is reduced, decreasing the probability of the vehicle changing lanes to that lane, thereby reducing the probability of the vehicle needing to change lanes again after changing lanes to the target lane due to obstacles.

[0122] In some other scenarios, after selecting the target lane, if there is an obstacle in the target lane, the vehicle can be controlled to pass the obstacle in another lane before switching to the target lane, based on the location of the obstacle, in order to reduce the number of invalid lane changes.

[0123] It should be noted that, in actual implementation, a different order of generating the navigation field than method 300 can also be used. Specifically, a query point can be determined based on the perception information acquired by the vehicle; further, based on the query point and road topology information, multiple key points within a certain area corresponding to the query point can be determined, as shown in Figure 10. Each query point indicates a position on the centerline of a lane, and the multiple key points corresponding to that query point indicate the position range of a section of the lane centerline. Then, a navigation field is generated based on these multiple key points. A more specific implementation of generating the query point, key points, and navigation field can be found in the descriptions in the preceding embodiments, and will not be repeated here.

[0124] Figure 11 shows another schematic flowchart of the navigation method provided in an embodiment of this application. This method 1000 can be applied to the vehicle shown in Figure 1, or it can be executed by the system shown in Figure 2. More specifically, this method 1000 can be executed by the navigation field generation module 240 and the planning and control module 250, and the method 1000 may include:

[0125] S1010, Obtain navigation information and road topology information. The navigation information indicates the P roads that the vehicle needs to take from its current location to the target location and the direction of travel. The road topology information indicates the topological relationship between the Q roads within the first area associated with the current location, as well as the number of lanes contained in each of the Q roads.

[0126] Among them, Q roads include P roads, where P and Q are both positive integers.

[0127] For example, the first region may include region 1 in method 300; the methods for obtaining navigation information and road topology information can be referred to the description in the foregoing embodiments, and will not be repeated here.

[0128] S1020, Based on navigation information and road topology information, generate navigation field information, which indicates the passability of different positions of at least one lane in the target road.

[0129] The target road is either the current road the vehicle is on, or the road the vehicle needs to travel to reach the target location.

[0130] For example, the navigation field information may include the navigation field in method 300, and the accessibility indicated by the navigation field may be the navigation trend indicated by the navigation field information.

[0131] In some implementations, the method further includes: determining a set of key points based on road topology information, where each key point in the set indicates the position of the lane centerline; S1020 can be further refined as: determining the value of each key point based on navigation information and road topology information, where the value of each key point indicates the passability of the corresponding location; and generating navigation field information based on the value of each key point.

[0132] For example, the key point set may include M2 ​​key points in method 300. The specific implementation of determining the value of the key points and the specific implementation of generating navigation field information based on the value of the key points can be referred to the description in method 300, and will not be repeated here.

[0133] In some implementations, the method further includes: acquiring vehicle perception information, where the vehicle perception information indicates the boundary of each of the M lanes in the target road, where M is a positive integer; determining N query points based on the vehicle perception information, where each of the N query points indicates a position on one of the N lanes, where M lanes include N lanes, and N is a positive integer; and determining the key point set based on road topology information, which includes: determining N sets of key points based on the N query points.

[0134] For example, N query points may include the 3 key points shown in Figure 10, and N sets of key points may include the 6 rows of key points shown in Figure 10. Each set of key points includes two rows of key points on both sides of the lane center position indicated by the shape point.

[0135] In some implementations, the navigation field information includes multiple grids, where each grid point indicates a road coordinate, and the value of each grid point indicates the degree of accessibility. The method also includes determining the position of multiple grids based on a set of key points.

[0136] For example, the multiple grids can be grids indicating the navigation field as shown in any of Figures 7, 9, and 10.

[0137] In some implementations, the key point set includes a first key point. Based on the value of each key point, navigation field information is generated, including: determining the values ​​of the grid points of the grid containing the first key point based on the value of the first key point.

[0138] For example, the specific implementation of determining the value of the grid points containing the first key point based on the value of the first key point can be referred to the description in the foregoing embodiments, and will not be repeated here.

[0139] In some implementations, P roads are associated with at least one intersection, including a first intersection and a second intersection. Vehicles traveling towards a target location must sequentially pass through the first intersection and then the second intersection. The P roads include a first road, the end of which connects to the first intersection. Navigation field information is generated based on navigation information and road topology information, including: determining the intersection navigation trend for each intersection based on the navigation information and road topology information; the intersection navigation trend for the first intersection indicating the probability of passage for at least one lane of the first road at the first intersection; determining the intersection navigation trend for the first intersection based on the intersection navigation trend for the second intersection; and generating navigation field information based on the intersection navigation trends for the first and second intersections.

[0140] For example, the first intersection can be intersection a in the aforementioned embodiments, and the second intersection can be intersection b in the aforementioned embodiments; or, the first intersection can be intersection b in the aforementioned embodiments, and the second intersection can be intersection c in the aforementioned embodiments; or, the first intersection can be intersection 2 in the aforementioned embodiments, and the second intersection can be intersection 1 in the aforementioned embodiments. More specifically, the implementation method of determining the intersection navigation trend of the first intersection based on the intersection navigation trend of the second intersection can be found in the description in the aforementioned embodiments, and will not be repeated here.

[0141] S1030 controls the vehicle to travel to the target location based on navigation information.

[0142] In some implementations, S1030 can be further refined as follows: based on the navigation field information, determine the navigation trend query value of each lane in the target road, the navigation trend query value indicating the navigation cost of each lane, and / or the remaining drivable distance of each lane when the distance between it and the vehicle is a first distance; based on the navigation trend query value, control the vehicle to drive in the lane with the longest remaining drivable distance and / or the lowest navigation cost in the target road.

[0143] For example, the first distance can be a value between 50 meters and 200 meters, or it can be any other distance.

[0144] In some implementations, S1030 can be further refined as follows: when the offset between the boundary of the target road indicated by the navigation field information and the boundary of the target road perceived by the vehicle is less than or equal to a distance threshold, the vehicle is controlled to drive towards the target location according to the navigation field information; and / or when the number of lanes of the target road indicated by the navigation field information is consistent with the number of lanes of the target road perceived by the vehicle, the vehicle is controlled to drive towards the target location according to the navigation field information.

[0145] For example, the distance threshold can be a value between 0.5 meters and 0.75 meters, or it can be any other value.

[0146] In the navigation method provided in this application embodiment, road topology information can provide the topological relationship between roads beyond line of sight, and navigation information can provide information on the driving direction. In this way, even without high-precision map guidance, the vehicle can obtain far-field information, enabling the vehicle to determine whether to change lanes and / or the timing of lane changes in complex road conditions, reducing the probability of invalid lane changes.

[0147] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions between the various embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0148] The methods provided by the embodiments of this application have been described in detail above with reference to Figures 1 to 11. The apparatus provided by the embodiments of this application will now be described in detail with reference to Figures 12 and 13. It should be understood that the descriptions of the apparatus embodiments correspond to the descriptions of the method embodiments; therefore, any content not described in detail can be found in the above method embodiments, and for the sake of brevity, will not be repeated here.

[0149] Figure 12 shows a schematic block diagram of a navigation device 2000 provided in an embodiment of this application. The device 2000 may include units for executing the methods described in the foregoing embodiments. Furthermore, each unit in the device 2000 implements a corresponding process of the above method embodiments. The device 2000 includes an acquisition unit 2010, which can be used to implement corresponding data acquisition or transmission / reception functions. The device 2000 also includes a processing unit 2020, which can be used to implement corresponding processing functions.

[0150] Optionally, the device 2000 further includes a storage unit, which can be used to store instructions and / or data. The processing unit 2020 can read the instructions and / or data in the storage unit so that the device can perform the relevant actions in the aforementioned method embodiments.

[0151] It should be understood that the specific process of each unit performing the above-mentioned corresponding steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0152] It should also be understood that the device 2000 described herein is embodied in the form of a functional unit. The terms “module” or “unit” may refer to application-specific ASICs, electronic circuits, processors (e.g., shared processors, proprietary processors, or group processors) and memory for executing one or more software or firmware programs, integrated logic circuits, and / or other suitable components that support the described functions.

[0153] The apparatuses described above have the function of implementing the corresponding steps in the methods described above. These functions can be implemented in hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the functions described above; for example, the acquisition unit 2010 can be replaced by a transceiver, and other units, such as the processing unit, can be replaced by a processor, used to execute the relevant processing operations in each method embodiment.

[0154] Exemplarily, the acquisition unit 2010 and processing unit 2020 can be disposed in the vehicle 100 shown in FIG. 1, or they can also be disposed in the system shown in FIG. 2. More specifically, the acquisition unit 2010 and processing unit 2020 can be disposed in the control module 250. Exemplarily, the operations performed by the acquisition unit 2010 and processing unit 2020 can be performed by a single processor, or they can be performed by different processors. In specific implementation, the one or more processors can be processors disposed in the vehicle 100 shown in FIG. 1; or, the device 2000 can be a chip disposed in the vehicle 100.

[0155] In the specific implementation process, the units in the above device can be fully or partially integrated together, or they can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a system-on-a-chip (SoC).

[0156] Figure 13 is another schematic block diagram of the navigation device provided in an embodiment of this application. The device 2100 shown in Figure 13 may include a processor 2110, a transceiver 2120, and a memory 2130. The processor 2110, transceiver 2120, and memory 2130 are connected via internal connection paths. The memory 2130 is used to store instructions, and the processor 2110 is used to execute the instructions stored in the memory 2130 to implement the methods in the above embodiments. Optionally, the memory 2130 may be coupled to the processor 2110 via an interface or integrated with the processor 2110.

[0157] It should be noted that the transceiver 2120 mentioned above may include, but is not limited to, transceiver devices such as input / output interfaces, to realize communication between device 2100 and other devices or communication networks.

[0158] Memory 2130 can be volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM). For example, RAM can be used as an external cache. By way of example and not limitation, RAM includes various forms such as: static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0159] Transceiver 2120 uses transceiver devices, such as but not limited to transceivers, to enable communication between device 2100 and other devices or communication networks to receive / send data / information for implementing the methods in the above embodiments.

[0160] This application also provides an intelligent driving device, which includes the device 2000 or device 2100 in the above embodiments.

[0161] This application also provides a computer program product, which includes computer program code. When the computer program code is run on a computer, it causes the computer to implement the methods described in the above embodiments of this application.

[0162] This application also provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to implement the methods described in the above embodiments of this application.

[0163] This application also provides a chip, including circuitry, for performing the methods described in the above embodiments of this application.

[0164] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0165] In the description of the embodiments of this application, unless otherwise stated, " / " means "or", for example, A / B can mean A or B; "and / or" in this document describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. In this application, "at least one" means one or more, and "more" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0166] The use of prefixes such as "first" and "second" in this application embodiment is solely for distinguishing different descriptive objects and does not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes to distinguish descriptive objects in this application embodiment does not constitute a limitation on the described objects. The description of the described objects is found in the claims or the context of the embodiments, and the use of such prefixes should not constitute unnecessary restrictions.

[0167] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components 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 units may be electrical, mechanical, or other forms.

[0168] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions between the various embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0169] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0170] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0171] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A navigation method, characterized in that, include: Obtain navigation information and road topology information. The navigation information indicates the P roads that the vehicle needs to take from its current location to the target location and the direction of travel. The road topology information indicates the topological relationship between Q roads within a first area associated with the current location, as well as the number of lanes contained in each of the Q roads. The Q roads include the P roads, where P and Q are both positive integers. Based on navigation information and road topology information, navigation field information is generated, which indicates the passability of different positions of at least one lane in the target road. Wherein, the target road is the current road where the vehicle is located, or the target road is the road that the vehicle needs to take to travel to the target location; Based on the navigation field information, the vehicle is controlled to travel towards the target location.

2. The method according to claim 1, characterized in that, The method further includes: A set of key points is determined based on the road topology information, wherein each key point in the set indicates the position of the lane centerline; The step of generating navigation field information based on navigation information and road topology information includes: Based on the navigation information and the road topology information, the value of each key point is determined, and the value of each key point indicates the accessibility of the corresponding location of the key point; The navigation field information is generated based on the value of each key point.

3. The method according to claim 2, characterized in that, The method further includes: Acquire vehicle perception information, wherein the vehicle perception information indicates the boundary of each lane in the M lanes of the target road, where M is a positive integer; Based on the vehicle perception information, N query points are determined. Each of the N query points indicates a location on one of the N lanes. The M lanes include the N lanes, and N is a positive integer. The key point set includes N groups of key points, each group indicating the location range of a lane centerline. Determining the key point set based on the road topology information includes: Based on the N query points, determine the N sets of key points.

4. The method according to claim 2 or 3, characterized in that, The navigation field information includes multiple grids, where each grid point indicates a road coordinate, and the value of each grid point indicates the degree of accessibility. The method further includes: The positions of the multiple grid cells are determined based on the set of key points.

5. The method according to claim 4, characterized in that, The set of key points includes a first key point, and the step of generating the navigation field information based on the value of each key point includes: Based on the value of the first key point, determine the values ​​of the grid points of the grid containing the first key point.

6. The method according to any one of claims 1 to 5, characterized in that, The P roads are associated with at least one intersection, including a first intersection and a second intersection. The vehicle needs to pass through the first intersection and the second intersection in sequence when traveling to the target location. The P roads include the first road, and the end of the first road is connected to the first intersection. The step of generating navigation field information based on navigation information and road topology information includes: Based on the navigation information and the road topology information, the intersection navigation trend corresponding to each intersection in the at least one intersection is determined. The intersection navigation trend of the first intersection indicates the probability of passage of at least one lane contained in the first road at the first intersection. Based on the intersection navigation trend of the second intersection, determine the intersection navigation trend of the first intersection; The navigation field information is generated based on the intersection navigation trends of the first intersection and the intersection navigation trends of the second intersection.

7. The method according to any one of claims 1 to 6, characterized in that, The step of controlling the vehicle to travel towards the target location based on the navigation field information includes: Based on the navigation field information, determine the navigation trend query value for each lane in the target road. The navigation trend query value indicates the navigation cost of each lane and / or the remaining drivable distance of each lane when the distance between it and the vehicle is a first distance. Based on the navigation trend query value, the vehicle is controlled to travel in the lane with the longest remaining drivable distance and / or the lowest navigation cost on the target road.

8. The method according to claim 7, characterized in that, The step of controlling the vehicle to travel towards the target location based on the navigation field information includes: When the offset between the boundary of the target road indicated by the navigation field information and the boundary of the target road perceived by the vehicle is less than or equal to a distance threshold, the vehicle is controlled to move towards the target location based on the navigation field information; and / or When the number of lanes on the target road indicated by the navigation field information matches the number of lanes on the target road perceived by the vehicle, the vehicle is controlled to drive toward the target location based on the navigation field information.

9. A navigation device, characterized in that, include: The acquisition unit is used to acquire navigation information and road topology information. The navigation information indicates the P roads that the vehicle needs to take from its current location to the target location and the direction of travel. The road topology information indicates the topological relationship between Q roads within a first area associated with the current location, as well as the number of lanes contained in each of the Q roads. The Q roads include the P roads, where P and Q are both positive integers. The processing unit is configured to generate navigation field information based on navigation information and road topology information, wherein the navigation field information indicates the passability of different positions of at least one lane in the target road; Wherein, the target road is the current road where the vehicle is located, or the target road is the road that the vehicle needs to take to travel to the target location; The processing unit is further configured to: control the vehicle to travel toward the target location based on the navigation field information.

10. The apparatus according to claim 9, characterized in that, The processing unit is also used for: A set of key points is determined based on the road topology information, wherein each key point in the set indicates the position of the lane centerline; Based on the navigation information and the road topology information, the value of each key point is determined, and the value of each key point indicates the accessibility of the corresponding location of the key point; The navigation field information is generated based on the value of each key point.

11. The apparatus according to claim 10, characterized in that, The acquisition unit is also used for: Acquire vehicle perception information, wherein the vehicle perception information indicates the boundary of each lane in the M lanes of the target road, where M is a positive integer; Based on the vehicle perception information, N query points are determined. Each of the N query points indicates a location on one of the N lanes. The M lanes include the N lanes, and N is a positive integer. The set of key points includes N groups of key points, each group of key points indicating the location range of a lane centerline. The processing unit is used for: Based on the N query points, determine the N sets of key points.

12. The apparatus according to claim 10 or 11, characterized in that, The navigation field information includes multiple grids, where each grid point indicates a road coordinate, and the value of each grid point indicates the degree of accessibility. The processing unit is further configured to: The positions of the multiple grid cells are determined based on the set of key points.

13. The apparatus according to claim 12, characterized in that, The set of key points includes a first key point, and the processing unit is used for: Based on the value of the first key point, determine the values ​​of the grid points of the grid containing the first key point.

14. The apparatus according to any one of claims 9 to 13, characterized in that, The P roads are associated with at least one intersection, including a first intersection and a second intersection. The vehicle needs to pass through the first intersection and the second intersection in sequence when traveling to the target location. The P roads include the first road, and the end of the first road is connected to the first intersection. The processing unit is used for: Based on the navigation information and the road topology information, the intersection navigation trend corresponding to each intersection in the at least one intersection is determined. The intersection navigation trend of the first intersection indicates the probability of passage of at least one lane contained in the first road at the first intersection. Based on the intersection navigation trend of the second intersection, determine the intersection navigation trend of the first intersection; The navigation field information is generated based on the intersection navigation trends of the first intersection and the intersection navigation trends of the second intersection.

15. The apparatus according to any one of claims 9 to 14, characterized in that, The processing unit is used for: Based on the navigation field information, determine the navigation trend query value for each lane in the target road. The navigation trend query value indicates the navigation cost of each lane and / or the remaining drivable distance of each lane when the distance between it and the vehicle is a first distance. Based on the navigation trend query value, the vehicle is controlled to travel in the lane with the longest remaining drivable distance and / or the lowest navigation cost on the target road.

16. The apparatus according to claim 15, characterized in that, The processing unit is used for: When the offset between the boundary of the target road indicated by the navigation field information and the boundary of the target road perceived by the vehicle is less than or equal to a distance threshold, the vehicle is controlled to drive toward the target location according to the navigation field information. and / or When the number of lanes on the target road indicated by the navigation field information matches the number of lanes on the target road perceived by the vehicle, the vehicle is controlled to drive toward the target location based on the navigation field information.

17. A navigation device, characterized in that, include: A processor for executing a computer program stored in memory to cause the apparatus to perform the method as described in any one of claims 1 to 8.

18. A computer-readable storage medium, characterized in that, It stores instructions that, when executed by a processor, implement the method as described in any one of claims 1 to 8.

19. A chip, characterized in that, The chip includes circuitry for performing the method as described in any one of claims 1 to 8.

20. A computer program product, characterized in that, The computer program product includes: computer program code, which, when executed by a processor, implements the method as described in any one of claims 1 to 8.

21. A vehicle, characterized in that, Includes the apparatus as claimed in any one of claims 9 to 17, or the computer-readable storage medium as claimed in claim 18, or the chip as claimed in claim 19, or the vehicle is equipped with the computer program product as claimed in claim 20.

Images