Automatic driving method, device and intelligent driving equipment

Abstract

An autonomous driving method, apparatus, and intelligent driving device are disclosed. The method includes: acquiring traction information, wherein the traction information indicates the direction and position of the intelligent driving device exiting a first intersection, the traction information being determined based on SD map navigation information, and the SD map navigation information indicating a navigation route from the current position of the intelligent driving device to a target position; and controlling the speed and / or posture of the intelligent driving device toward or through the first intersection based on the traction information. The technical solution of this application can be applied to the field of intelligent vehicles such as electric vehicles and new energy vehicles. When the vehicle's perception system fails to identify the road boundary and / or clear lane lines at the exit position, the vehicle can be controlled to travel at a lower speed based on the traction information. This improves the vehicle's ability and efficiency in navigating complex topological scenarios such as intersections and ramps without relying on high-precision maps, thereby enhancing the vehicle's autonomous driving capabilities.

Description

Technical Field

[0001] This application relates to the field of intelligent driving, and more specifically, to an autonomous driving method, apparatus, and intelligent driving device. Background Technology

[0002] 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.

[0003] Therefore, an autonomous driving solution that is independent of high-precision maps is urgently needed to be developed. Summary of the Invention

[0004] This application provides an autonomous driving method, apparatus, and intelligent driving device that can improve the ability and efficiency of intelligent driving devices to pass through complex topological scenarios such as intersections and ramps without relying on high-precision maps.

[0005] In a first aspect, an autonomous driving method is provided, which can be executed by an intelligent driving device, for example, by the computing platform of the intelligent driving device, or by a chip or circuit for the intelligent driving device; or, the method can also be executed by a cloud server associated with the intelligent driving device, which is not specifically limited in this application.

[0006] The method includes: acquiring traction information, which indicates the direction and position of the intelligent driving device exiting the first intersection, wherein the traction information is determined according to standard definition (SD) map navigation information, and the SD map navigation information indicates the navigation route from the current position of the intelligent driving device to the target position; and controlling the speed and / or posture of the intelligent driving device toward or through the first intersection based on the traction information.

[0007] In some implementations, the first intersection may include at least two boundaries, such as a boundary on the side where the autonomous driving device enters and a boundary on the side where the autonomous driving device exits; or, the first intersection may include other boundaries in addition to the two boundaries mentioned above, for example, the first intersection may be an n-way intersection, where n is an integer greater than or equal to 3. It should be understood that each of the at least two boundaries of the first intersection connects to a road.

[0008] In some implementations, the traction information may include the traction road, which is the road boundary connected to the boundary of the first intersection where the intelligent driving device exits. The traction road may include at least one lane and may be a one-way road.

[0009] In the aforementioned technical solution, when the intelligent driving device is in autonomous driving mode and is too far from the intersection to obtain relevant perception information (such as road boundaries or lane lines for autonomous driving navigation) through sensing devices, or when the obtained perception information is not accurate enough, the device can determine the intersection's location in advance based on traction information. This allows for the adjustment of the vehicle's heading, preventing the intelligent driving device from getting stuck near the intersection and improving its passability and success rate at intersections in the absence of high-precision maps. It also reduces the likelihood of the intelligent driving device entering the oncoming lane or scraping against the road boundary when exiting the intersection. Furthermore, adjusting the speed of the intelligent driving device based on traction information helps improve its driving safety.

[0010] In conjunction with the first aspect, in some implementations of the first aspect, controlling the speed and / or posture of the intelligent driving device toward or through the first intersection based on traction information includes: controlling the intelligent driving device to move toward or through the first intersection at a first speed, wherein the first speed is less than or equal to a speed threshold, based on traction information.

[0011] For example, the speed threshold can be 70% or 80% of the maximum speed limit at the first intersection, or it can be any other speed determined based on the maximum speed limit at the first intersection. The maximum speed limit at the first intersection can be the maximum speed limit of the road connected to the first intersection, such as the maximum speed limit of the road the intelligent driving device is currently traveling on (e.g., the road entering the first intersection), or it can be the maximum speed limit of the target road the intelligent driving device is traveling on (e.g., the road exiting the first intersection).

[0012] In the above technical solution, the intelligent driving device is controlled to drive at a slower speed according to the traction information, so that when the intelligent driving device drives to a specific boundary (i.e. the boundary where the intelligent driving device leaves the first intersection), it has enough time to obtain road information at the specific boundary, thereby increasing the probability of obtaining clear road information (such as clear lane lines), and thus improving the control capability of the intelligent driving device in the process of autonomous driving.

[0013] In conjunction with the first aspect, in some implementations of the first aspect, the first boundary of the first intersection is the target exit boundary of the intelligent driving device, and the first boundary is connected to the first road. The method further includes: acquiring information about the first road perceived by the intelligent driving device; and controlling the speed and / or posture of the intelligent driving device toward or through the first intersection based on traction information, including: controlling the speed and / or posture of the intelligent driving device based on the information of the first road and the traction information.

[0014] The information about the first road perceived by the intelligent driving device can be understood as the information about the first road obtained by the perception system of the intelligent driving device (such as camera devices, radar, etc.).

[0015] Understandably, when the first boundary is far from the autonomous driving device and beyond its perception range, the device may be unable to acquire the first road information, or the reliability and / or accuracy of the acquired information may be insufficient. As the autonomous driving device approaches the first boundary, the reliability and accuracy of the acquired road information gradually improve. Since the traction information is only the position and direction information generated based on the SD map navigation information when exiting the first intersection, there may be errors between it and the actual position and boundary of the first road. Therefore, when the autonomous driving device is sufficiently close to the first boundary, controlling its posture based on the first road information is more reliable than controlling it based on the traction information. This "more reliable" can be understood as preventing the autonomous driving device from crossing lane lines or road boundary lines. Therefore, when navigation is based solely on the SD map, controlling the speed and / or posture of the autonomous driving device based on the first road information and the traction road during its approach to and passage through the first intersection helps improve the throughput of complex intersections and the robustness of the autonomous driving system.

[0016] In conjunction with the first aspect, in certain implementations of the first aspect, the information of the first road indicates the boundary of the first road and / or the position of the lane lines in the first road; controlling the speed and / or posture of the intelligent driving device based on the information of the first road and traction information includes: when the reliability of the information of the first road is less than or equal to a preset threshold, controlling the intelligent driving device to travel at a first speed based on the traction information; or, when the reliability of the information of the first road is greater than the preset threshold, controlling the intelligent driving device to switch from the first speed to a second speed based on the information of the first road; wherein the first speed is less than or equal to the second speed.

[0017] In the above technical solution, when the reliability of the first road information is greater than a preset threshold, controlling the intelligent driving device to drive at a higher speed can improve the speed and efficiency of the intelligent driving device passing through the first intersection.

[0018] In conjunction with the first aspect, in some implementations of the first aspect, the traction information includes the traction road. Obtaining the traction information includes: obtaining the boundary information of the first intersection; determining the direction of exiting the first intersection based on the SD map navigation information; determining the position of exiting the first intersection based on the boundary information and the direction of exiting the first intersection; and generating the traction road based on the position and direction of exiting the first intersection.

[0019] It should be noted that this application does not strictly distinguish between towing lanes and towing roads. When the number of lanes of the road connected to the location where the intelligent driving device exits the first intersection (such as the first road mentioned above) is not obtained, the towing road generated based on the location of the first intersection and the direction of exiting the first intersection may include only one towing lane; when the number of lanes is n, the towing road generated based on the location of the first intersection and the direction of exiting the first intersection may include n towing lanes.

[0020] In the above technical solution, for roads that are beyond the perception range of intelligent driving equipment, a traction road is generated based on the SD map navigation information. This allows the intelligent driving equipment to adjust and move forward in advance toward the target driving direction, reducing the probability of the intelligent driving equipment entering the oncoming lane or deviating from the road. This helps to improve the autonomous driving capability and driving safety of intelligent driving equipment in scenarios without high-precision maps.

[0021] In conjunction with the first aspect, in some implementations of the first aspect, a traction road is generated based on the position and direction of exiting the first intersection, including: generating an initial traction road based on the position and direction of exiting the first intersection; correcting the initial traction road based on the position of the first road connected at the boundary of the intelligent driving device exiting the first intersection, thereby obtaining the traction road.

[0022] For example, the location of the first road can be sensed by the perception system of the intelligent driving device.

[0023] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: controlling the display device to display a navigation interface, the navigation interface including an image of the traction road and a first icon, the first icon indicating the position of the intelligent driving device relative to the traction road.

[0024] For example, when controlling the speed and / or posture of the intelligent driving device through traction information, the control navigation interface displays an image of the traction road. The first icon can be an icon representing the intelligent driving device, and the first icon and the image of the traction road indicate the relative positional relationship between the intelligent driving device and the traction road.

[0025] In the above technical solution, by displaying an image of the towing road, users of the intelligent driving device can clearly identify the data source currently used for navigation. It can also alert the driver of the intelligent driving device to take over the device when necessary.

[0026] In conjunction with the first aspect, in some implementations of the first aspect, the navigation interface also includes an image of the boundary of the first road.

[0027] For example, the image of the boundary of the first road can be obtained by processing the information of the first road perceived by the perception system of the intelligent driving device.

[0028] In some implementations, when the image of the traction road indicates the boundary of the first road, the navigation interface can be controlled to switch from displaying the image of the traction road to displaying the image of the boundary of the first road.

[0029] In some other implementations, when the image of the traction road indicates the position of the lane lines in the first road, the navigation interface can be controlled to overlay the image of the traction road and the image of the boundary of the first road.

[0030] In the above technical solution, by displaying an image of the boundary of the first road, the user can clearly see the difference between the traction road and the actual first road, as well as the perception results of the intelligent driving device on the current driving road, so that the driver can take over the intelligent driving device when necessary.

[0031] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: when the lane line information of the first road is obtained, controlling the navigation interface to switch from the image of the traction road to the image of the lane line of the first road.

[0032] For example, when controlling the speed and / or position of the intelligent driving device via the lane lines of the first road, the control navigation interface switches from displaying an image of the traction road to displaying an image of the lane lines of the first road.

[0033] The "switching" of the content on the navigation interface in this application can be understood as the result of refreshing the navigation interface in real time.

[0034] In the above technical solution, by switching images, users can determine the improvement in the intelligent driving device's perception of the first road, thereby increasing their confidence in the autonomous driving function of the intelligent driving device.

[0035] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: acquiring second road information within a first range of the intelligent driving device, the second road information indicating multiple perceived routes passing through the first intersection; determining the main lane from the multiple perceived routes based on SD map navigation information; controlling the speed and / or pose of the intelligent driving device as it approaches or passes through the first intersection, including: controlling the speed and / or pose of the intelligent driving device when it travels along the main lane.

[0036] In the above technical solution, when controlling the intelligent driving device to drive towards and through the first intersection, it is necessary to control the intelligent driving device to drive along the main road to avoid the intelligent driving device deviating from its course and improve the robustness of the intelligent driving device's autonomous driving function.

[0037] In a second aspect, an autonomous driving device is provided, comprising an acquisition unit and a processing unit, wherein the acquisition unit is configured to: acquire traction information, the traction information indicating the direction and position of the intelligent driving device exiting a first intersection, the traction information being determined based on SD map navigation information, the SD map navigation information indicating a navigation route from the current position of the intelligent driving device to a target position; the processing unit is configured to: control the speed and / or posture of the intelligent driving device toward or through the first intersection based on the traction information.

[0038] In conjunction with the second aspect, in some implementations of the second aspect, the processing unit is used to: control the intelligent driving device to travel at a first speed based on traction information, wherein the first speed is less than or equal to a speed threshold.

[0039] In conjunction with the second aspect, in some implementations of the second aspect, the first boundary of the first intersection is the target exit boundary of the intelligent driving device, the first boundary is connected to the first road, and the acquisition unit is further used to: acquire information of the first road perceived by the intelligent driving device; the processing unit is used to: control the speed and / or posture of the intelligent driving device according to the information of the first road and the traction information.

[0040] In conjunction with the second aspect, in some implementations of the second aspect, the information of the first road indicates the boundary of the first road and / or the position of the lane lines in the first road; the processing unit is configured to: when the reliability of the information of the first road is less than or equal to a preset threshold, control the intelligent driving device to drive at a first speed according to the traction information; or, when the reliability of the information of the first road is greater than the preset threshold, control the intelligent driving device to switch from the first speed to a second speed according to the information of the first road; wherein the first speed is less than or equal to the second speed.

[0041] In conjunction with the second aspect, in some implementations of the second aspect, the traction information includes the traction road, and the acquisition unit is further configured to: acquire the boundary information of the first intersection; the processing unit is further configured to: determine the direction of exiting the first intersection based on the SD map navigation information; determine the position of exiting the first intersection based on the boundary information and the direction of exiting the first intersection; and generate the traction road based on the position of exiting the first intersection and the direction of exiting the first intersection.

[0042] In conjunction with the second aspect, in some implementations of the second aspect, the processing unit is used to: generate an initial traction road based on the position and direction of exiting the first intersection; and correct the initial traction road based on the position of the first road connected at the boundary where the intelligent driving device exits the first intersection, thereby obtaining the traction road.

[0043] In conjunction with the second aspect, in some implementations of the second aspect, the processing unit is further configured to: control the display device to display a navigation interface, the navigation interface including an image of the towing road and a first icon, the first icon indicating the position of the intelligent driving device relative to the towing road.

[0044] In conjunction with the second aspect, in some implementations of the second aspect, the navigation interface also includes an image of the boundary of the first road.

[0045] In conjunction with the second aspect, in some implementations of the second aspect, the processing unit is further configured to: when the lane line information of the first road is obtained, control the navigation interface to switch from the image of the traction road to the image of the lane line of the first road.

[0046] In conjunction with the second aspect, in some implementations of the second aspect, the acquisition unit is further configured to: acquire second road information within a first range of the intelligent driving device, the second road information indicating multiple perceived routes passing through the first intersection; the processing unit is further configured to: determine the main lane from the multiple perceived routes based on SD map navigation information; and control the speed and / or pose of the intelligent driving device when it travels along the main lane.

[0047] Thirdly, an autonomous driving 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.

[0048] In conjunction with the third aspect, in some implementations of the third aspect, the autonomous driving device also includes a memory.

[0049] Fourthly, an intelligent driving device is provided, which includes means as described in any of the possible implementations of the second to third aspects.

[0050] In conjunction with the fourth aspect, in some implementations of the fourth aspect, the intelligent driving device is a vehicle.

[0051] Fifthly, 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.

[0052] 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.

[0053] In a sixth aspect, a computer-readable 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.

[0054] In a seventh 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. Attached Figure Description

[0055] Figure 1 This is a functional schematic block diagram of the intelligent driving device provided in the embodiments of this application;

[0056] Figure 2 This is a schematic diagram of the autonomous driving system architecture provided in an embodiment of this application;

[0057] Figure 3 This is a schematic flowchart of the autonomous driving method provided in the embodiments of this application;

[0058] Figure 4 This is a schematic diagram illustrating an application scenario of the autonomous driving method provided in this application embodiment;

[0059] Figure 5 This is a schematic diagram illustrating the matching results between the perceived route and the SD map navigation route in an embodiment of this application;

[0060] Figure 6 This is another illustrative flowchart of the autonomous driving method provided in the embodiments of this application;

[0061] Figure 7 This is a schematic diagram illustrating the determination result of the exit location provided in the embodiments of this application;

[0062] Figure 8 This is another illustrative flowchart of the autonomous driving method provided in the embodiments of this application;

[0063] Figure 9 This is a schematic diagram of the traction road provided in the embodiments of this application;

[0064] Figure 10 This is another illustrative flowchart of the autonomous driving method provided in the embodiments of this application;

[0065] Figure 11 This is another illustrative flowchart of the autonomous driving method provided in the embodiments of this application;

[0066] Figure 12 This is a schematic block diagram of the autonomous driving device provided in the embodiments of this application;

[0067] Figure 13 This is another schematic block diagram of the autonomous driving device provided in the embodiments of this application. Detailed Implementation

[0068] Before introducing the solution of this application, let's first introduce the relevant concepts involved in this application:

[0069] 1. SD Map: The accuracy is generally at the meter level, and the richness is relatively low. It mainly includes road information and point of interest (POI) information. Among them, POI is point data in electronic map, which includes at least four attributes: name, address, coordinates, and category.

[0070] 2. High-definition (HD) maps: HD maps offer higher accuracy and richness than SD maps, with both absolute and relative accuracy at the centimeter level. In terms of richness, HD maps provide an environmental model of the autonomous vehicle's environment, including static high-definition maps and other dynamic information. The static high-definition map includes lane models, road components, and road attributes. Lane models include road details such as lane lines, lane center lines, and lane attribute changes. Other dynamic information includes all dynamic information within the intelligent network system, such as map dynamics, sensor information, driving behavior, and traffic dynamic information management.

[0071] As mentioned above, current autonomous driving technologies largely rely on high-precision maps for navigation. However, high-precision maps have drawbacks such as high acquisition 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. If navigation relies solely on environmental information perceived by vehicle sensors without high-precision maps, the limited perception range of these sensors prevents the acquisition of information beyond their range, thus restricting the vehicle's autonomous driving capabilities. For example, when a vehicle needs to pass through large intersections, irregular intersections, or make left or right turns at intersections, it may be unable to obtain information such as lane positions and road boundaries at the exit point, affecting its autonomous driving planning capabilities. For instance, the vehicle may be unable to determine the direction of deviation and travel, resulting in lower traffic efficiency in these scenarios.

[0072] In view of this, embodiments of this application provide an autonomous driving method, apparatus, and intelligent driving device. When the road ahead of the intelligent driving device includes an intersection, traction information for the intelligent driving device to exit the intersection is generated based on the road information (including intersection boundary information) acquired by the intelligent driving device and the navigation route generated based on the SD map. This traction information indicates the direction and position of the intelligent driving device exiting the intersection. The intelligent driving device controls its posture and speed as it passes through the intersection based on this traction information. When the perception system of the intelligent driving device fails to identify the road boundary and / or clear lane lines at the exit location, the intelligent driving device can be controlled to travel at a lower speed based on the traction information. As the intelligent driving device approaches the exit location, when its perception system can identify the road boundary and / or clear lane lines at the exit location, the intelligent driving device can be controlled to travel at a higher speed based on the road boundary and / or clear lane lines. This improves the efficiency of the intelligent driving device in passing through intersections without high-precision maps, thereby enhancing the autonomous driving capability of the intelligent driving device.

[0073] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0074] Figure 1 This is a functional block diagram of an intelligent driving device provided in an embodiment of this application. Figure 1As shown, the intelligent driving device 100 may include a perception system 120, a display device 130, and a computing platform 150. The perception system 120 may include several sensors for sensing information about the environment surrounding the intelligent driving device 100. For example, the perception system 120 may include a positioning system, which may be a Global Positioning System (GPS), a BeiDou system, or another positioning system. As another example, the perception system 120 may also include one or more of the following: an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera device.

[0075] Display devices 130 are mainly divided into two categories: the first is in-vehicle displays; the second is projection displays, such as head-up displays (HUDs). In-vehicle displays are physical displays and an important component of in-vehicle infotainment systems. They can include human-machine interfaces (HMIs). Head-up displays, also known as head-up display systems, are primarily used to display driving information such as speed and navigation on a display device in front of the user (e.g., the windshield), reducing the time the user's gaze is diverted, avoiding pupil changes caused by gaze shifts, and improving driving safety and comfort.

[0076] Some or all of the functions of the intelligent driving device 100 can be controlled by the computing platform 150. The 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 a reconfigurable hardware circuit, 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 the functions of some or all of the above units. 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.

[0077] The intelligent driving device 100 may include ADAS. ADAS utilizes various sensors on the intelligent driving device (including but not limited to: lidar, millimeter-wave radar, camera devices, ultrasonic sensors, global positioning system, inertial measurement unit) to acquire information from the surroundings of the intelligent driving device, and analyzes and processes the acquired information to achieve functions such as obstacle perception, target recognition, intelligent driving device positioning, path planning, driver monitoring / alerts, etc., thereby improving the safety, automation and comfort of driving the intelligent driving device.

[0078] Logically, an ADAS system generally includes three main functional modules: a perception module, a decision-making module, and an execution module. The perception module senses the environment around the vehicle through sensors and inputs corresponding real-time data to the decision-making processing center. The perception module mainly includes vehicle cameras, ultrasonic radar, millimeter-wave radar, and lidar. The decision-making module makes corresponding decisions based on the information obtained by the perception module using computing devices and algorithms. After receiving the decision signal from the decision-making module, the execution module takes corresponding actions, such as driving, changing lanes, steering, braking, and issuing warnings.

[0079] At different levels of autonomous driving (L0-L5), ADAS can achieve different levels of automated driving assistance based on artificial intelligence algorithms and information acquired by multiple sensors. The aforementioned autonomous driving levels (L0-L5) are based on the classification standards of the Society of Automotive Engineers (SAE). 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. At levels L4 and L5, the driver can completely transform into a passenger. Currently, the functions that ADAS 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.

[0080] In this embodiment, the computing platform 150 can generate traction information based on the SD map and the surrounding environment information of the intelligent driving device obtained by the perception system 120. Alternatively, the computing platform 150 can also control the display device 130 to display the traction information.

[0081] Figure 2 A schematic diagram of the autonomous driving system architecture provided in an embodiment of this application is shown. For example... Figure 2 As shown, the system includes a perception module 210, a map information acquisition module 220, a road information generation module 230, a road planning and control module 240, and a display module 250. The perception module 210 may include... Figure 1The sensing system 120 shown may include one or more camera devices, or may further include one or more radars; the map information acquisition module 220, the road information generation module 230, and the planning control module 240 may each include Figure 1 The computing platform 150 shown may include one or more processors; the display module 250 may include one or more of the display devices 130, such as an HMI.

[0082] The perception module 210 acquires environmental information surrounding the intelligent driving device and sends it to the road information generation module 230. This environmental information includes road information, such as road boundaries and lane lines of the road the intelligent driving device is currently traveling on. For example, the perception module 210 may also include one or more processors to process the acquired perception information to obtain environmental information. For instance, taking an image as the perception information, one or more processors in the perception module 210 can extract environmental information such as road boundaries, intersection boundaries, and lane lines from the acquired image.

[0083] The map information acquisition module 220 is used to determine the navigation route between the current location and the target location of the intelligent driving device based on the SD map, and then send the navigation route to the road information generation module 230.

[0084] The road information generation module 230 includes a main channel determination module 231, an exit information determination module 232, and a guidance information generation module 233. The main channel determination module determines the main channel from multiple sensing routes identified by the sensing module 210 based on the matching degree between these routes and the navigation route. The exit information determination module 232 determines the exit location and direction based on the intersection boundaries identified by the sensing module 210. The guidance information generation module 233 generates a traction road based on the exit location and direction. This traction road is a generated virtual road. When the sensing module 210 determines the boundary of the real road corresponding to the traction road based on the sensing information, the guidance information generation module 233 can also correct the position of the traction road based on the boundary of the real road. Further, the road information generation module 230 sends the traction road to the planning and control module 240.

[0085] The control module 240 can also acquire the boundary of the real road corresponding to the traction road determined by the perception module 210, as well as the clear lane lines of the real road corresponding to the traction road collected by the perception module 210. Furthermore, the control module 240 can control the speed of the intelligent driving device based on the perception results of the perception module 210. The control module 240 can also control the display module 250 to display different navigation elements, which are one or more of the traction road, the real road boundary, and the clear lane lines.

[0086] It should be understood that the above module is only an example, and in actual applications, it may be added or removed as needed. For example, Figure 2 In the system architecture shown, the map information acquisition module 220 and the road information generation module 230 can be combined into one module; or, the road information generation module 230 and the planning control module 240 can be combined into one module. For example, the processor in the perception module 210 can be a separate processing module, and this processing module can be located in the intelligent driving device, or it can be located in a cloud server communicating with the intelligent driving device.

[0087] The above describes the autonomous driving system architecture provided in the embodiments of this application. The following details the system architecture based on... Figure 2 The flowchart illustrates the process by which the autonomous driving system implements the autonomous driving method provided in the embodiments of this application.

[0088] The intelligent driving devices involved in the embodiments of this application may include road vehicles, water vehicles, air vehicles, industrial equipment, agricultural equipment, or entertainment equipment, etc. For example, the intelligent driving device can be a vehicle, which is a vehicle in a broad sense, and can be a means of transportation (such as commercial vehicles, passenger cars, motorcycles, flying cars, trains, etc.), industrial vehicles (such as forklifts, trailers, tractors, etc.), engineering vehicles (such as excavators, bulldozers, cranes, etc.), agricultural equipment (such as lawnmowers, harvesters, etc.), amusement equipment, toy vehicles, etc. The embodiments of this application do not specifically limit the type of vehicle. For ease of understanding, the following description uses a vehicle as an example of an intelligent driving device.

[0089] Figure 3 A schematic flowchart of an autonomous driving method provided in an embodiment of this application is shown. This method 300 can be applied to... Figure 1 In the intelligent driving device shown, or the method can be provided by Figure 2 The system execution is shown. More specifically, method 300 can be executed by main channel determination module 231, and method 300 may include:

[0090] S301, based on the environmental information obtained by the sensing system, determine multiple sensing routes, each of the multiple sensing routes being a route that connects one road to another via an intersection.

[0091] In some implementations, environmental information may include images of a preset range in front of the vehicle. Elements such as roads and intersections are extracted from the images, and these elements are stitched together to obtain multiple sensing routes. For example, the preset range may be the farthest range that the vehicle's camera or radar can perceive, or it may be a range within 150 meters in front of the vehicle, or a range within 200 meters, or it may be other ranges.

[0092] For example, such as Figure 4 As shown in (a), when the vehicle is traveling from road A to intersection a, it acquires an image containing intersection a and road AD. Then, by processing the image, elements such as road A, road B, road C, road D and intersection a are extracted. Based on the topological relationship of the roads, the following three perception routes are determined: the route from road A to road B via intersection a, the route from road A to road C via intersection a, and the route from road A to road D via intersection a.

[0093] S302 determines the main channel from multiple perceived routes based on the SD map navigation route.

[0094] Among them, the SD map navigation route is a partial or complete navigation route generated based on the SD map from the vehicle's current location to the target location. For example, the SD map navigation route can be a navigation route within a certain range of the vehicle's current location extracted from the complete navigation route. The SD map navigation route is composed of multiple points, each of which indicates a geographic location.

[0095] For example, the current location of the vehicle can be determined based on GPS data, and then an SD navigation route matching the current location of the vehicle can be determined. The starting point of the SD map navigation route can be the current location of the vehicle, or the starting point of the SD map navigation route can be within a preset distance after the current location of the vehicle. For example, the preset distance can be 20 meters, 30 meters, or other distances. The total length of the SD map navigation route can be 100 meters, 120 meters, or other lengths.

[0096] For example, when a vehicle travels to a target location, it needs to be... Figure 4 Taking the example shown in (a) where road A changes to road B via intersection a, the SD map navigation route can be as follows: Figure 4 As shown in (b) in the figure. Further, the SD map navigation route can be geometrically matched with multiple sensing routes respectively, the overlap degree of multiple points of the SD navigation route with each sensing route can be calculated, and the sensing route with the highest overlap degree can be selected as the main route. Figure 5 (a) to (c) show the results of geometric matching between the SD map navigation route and the three perception routes. It can be seen that the SD map navigation route has the highest degree of overlap with the route from road A to road B via intersection a. Therefore, it can be determined that the route from road A to road B via intersection a is the main route.

[0097] The autonomous driving method provided in this application can determine the main route for the vehicle to travel to the target location based on the SD map navigation route and the road information perceived by the vehicle, thereby enabling the vehicle to travel along the main route to the target location even without a high-precision map.

[0098] Figure 6 This illustration shows another schematic flowchart of the autonomous driving method provided in an embodiment of this application. This method 400 can be applied to... Figure 1 In the intelligent driving device shown, or the method can be provided by Figure 2 The system is executed as shown. More specifically, method 400 can be executed by exit information determination module 232, and method 400 may include:

[0099] S401, based on the environmental information obtained by the perception system, determines the intersection boundaries of the intersections through which the main channel passes.

[0100] For example, the intersection through which the main channel passes may include intersection a in method 300.

[0101] In some implementations, environmental information may include images of a predetermined range ahead of the vehicle. In this case, images of the intersection area traversed by the main lane can be extracted from the images, and the perceived intersection boundary can be determined based on these images. This perceived intersection boundary can be the boundary of the intersection area directly extracted from the image. In some scenarios, because the vehicle is far from the intersection, the image acquired by the vehicle's perception system may only include a portion of the intersection area. For example, ... Figure 7 As shown in (a), the perceived intersection boundary is the boundary determined based on the image of the vehicle, which includes part of the intersection area. The initial intersection boundary can be adjusted according to the length of the boundary on the side entering the intersection (such as boundary a) to obtain the predicted intersection boundary, so that the shape and position of the predicted intersection boundary are as close as possible to the shape and position of the real intersection boundary. The predicted intersection boundary is used as the intersection boundary of the main channel.

[0102] As the vehicle moves forward, the area of ​​the intersection region included in the image acquired by the vehicle becomes larger and larger, and the intersection region becomes more and more complete. When the area of ​​the intersection region extracted from the image (i.e. the area enclosed by the initial intersection boundary) is greater than or equal to the area enclosed by the predicted intersection boundary, or when the image acquired by the vehicle includes the farthest end of the intersection region (such as boundary b) and the tracking is stable, it can be determined that the perceived intersection boundary is the boundary of the complete (or real) intersection region. Furthermore, it can be determined that the perceived intersection boundary is the intersection boundary of the main channel.

[0103] It should be noted that the intersection boundary involved in this application refers to the area where the road and the intersection meet. For example, the stop line at a real intersection can be regarded as the boundary of the intersection.

[0104] S402, determine the exit direction based on the SD map navigation route.

[0105] For example, based on the SD map navigation route and intersection locations, the SD map navigation route can be divided into an entry route and an exit route. The entry route indicates the direction of the intersection passed to enter the main channel, while the exit route indicates the direction of the intersection passed to exit the main channel. Furthermore, a certain number of landmarks can be selected on the exit route, and the exit direction can be determined based on the selected landmarks. This certain number can be 3, 5, or other values.

[0106] For example, such as Figure 7 As shown in (b), three shape points can be taken at ①, ②, and ③ respectively, and the results can be obtained by fitting the coordinates indicated by these three shape points. Figure 7 The exit direction is shown in (c) in the diagram.

[0107] S403 determines the exit location based on the intersection boundary and the exit azimuth angle.

[0108] For example, the intersection boundary can be a predicted intersection boundary or a perceived intersection boundary. As the vehicle travels in the main lane toward the intersection, the specific type of intersection boundary can be switched. The specific switching rules can be found in the description in S401, and will not be repeated here.

[0109] In some implementations, the geometric center of the intersection is determined based on the intersection boundary. The initial exit position is the point where the ray from the geometric center along the exit direction intersects with the intersection boundary (e.g., boundary c). Further, the final exit position can be obtained by offsetting a first distance towards the vehicle-traveling side from the initial exit position to avoid oncoming traffic. This first distance can be a preset distance, such as 3 meters or 2 meters, or it can be another distance determined based on the number of lanes on the road at the exit. The number of lanes on the road at the exit can be the number of lanes provided by the electronic horizon provider (EHP).

[0110] It should be noted that when the traffic rule is right-hand traffic, the side on which the vehicle is traveling is the right side, that is, the final exit position is obtained by shifting the vehicle to the right by a first distance from the initial exit position; when the traffic rule is left-hand traffic, the side on which the vehicle is traveling is the left side, that is, the final exit position is obtained by shifting the vehicle to the left by a first distance from the initial exit position.

[0111] The autonomous driving method provided in this application can determine the position and direction of a vehicle exiting an intersection along the main road based on the SD map navigation route and the intersection boundary perceived by the vehicle.

[0112] Figure 8 A further schematic flowchart of the autonomous driving method provided in an embodiment of this application is shown. This method 500 can be applied to... Figure 1 In the intelligent driving device shown, or the method can be provided by Figure 2 The system execution is shown. More specifically, method 500 can be executed by boot information generation module 233, and method 500 may include:

[0113] S501 generates the traction lane / road at the exit based on the exit location and exit direction.

[0114] For example, such as Figure 9 As shown, a traction road can be generated with a straight line passing through the intersection and in the direction of the intersection as the road centerline. The width of this traction road can be a first preset width. For example, the first preset width can be 5 meters, or 6 meters, or other values.

[0115] In some implementations, when obtaining information about the number of lanes at the exit road, the width of the traction road can be the product of the number of lanes and a second preset width. For example, the second preset width can be 3.5 meters, 3.6 meters, or other values. For instance, if the number of lanes is 2 and the second preset width is 3.5 meters, the width of the traction road is 2 * 3.5 = 7 meters; similarly, if the number of lanes is 1 and the second preset width is 3.5 meters, the width of the traction road is 3.5 meters. Furthermore, multiple traction lanes can be generated in the traction road based on the number of lanes.

[0116] In some implementations, the aforementioned traction lane / road is a one-way lane / road. Hereinafter, "traction road" will be used to refer to "traction lane / road." Unless otherwise specified, "traction road" in the following text can refer to either a traction road or a traction lane.

[0117] S502, Correct the position of the traction road.

[0118] In some implementations, the position of the traction road can be dynamically corrected using inertial filtering. For example, the position of the traction road can satisfy the following relationship:

[0119] Q(xi,yi)=AQ f (xi,yi)+BQ f-1 (xi,yi);

[0120] Where Q(xi,yi) represents the coordinates of point i on the boundary of the corrected traction road, Q f (xi,yi) represents the coordinates of point i on the boundary of the traction road determined in the current frame, Q f-1 (xi, yi) represents the coordinates of point i on the boundary of the traction lane determined in the previous frame. A and B represent the weights of the current frame result and the previous frame result, respectively, with A:B = 1:9. Alternatively, the ratio of A to B can be other values. For example, running method 400 and method 500 once can obtain the result of one frame of traction road, that is, the current frame is the coordinates of the traction road obtained by running method 400 and method 500 in the current run, and the previous frame is the coordinates of the traction road obtained by running method 400 and method 500 in the previous run of the nearest neighbor.

[0121] In some implementations, the position of the traction road can be corrected based on the position of the fuzzy road. The fuzzy road indicates the road boundary determined by the environment acquired by the perception system, and it is a one-way road. As the vehicle travels along the main lane towards the exit, it acquires environmental information near the exit and determines the road boundary at the exit, i.e., the fuzzy road, based on this information. The position of this fuzzy road may deviate from the position of the traction road generated in S501; therefore, the position of the traction road can be adjusted based on the position of the fuzzy road. For example, the position of the traction road in the current frame can satisfy the following relationship:

[0122] Q f (xi,yi)=CQ f’ (xi,yi)+DQ s (xi,yi);

[0123] Among them, Q f’ (xi,yi) represents the coordinates of point i on the boundary of the traction road generated in the current frame, Q s (xi,yi) represents the coordinates of point i on the boundary of the fuzzy road, and C and D represent Q respectively. f’ (xi,yi) and Q s The weights of (xi,yi) are C:D = 8:2, or the ratio of C to D can be other values.

[0124] The autonomous driving method provided in this application embodiment can generate a traction lane based on the position and direction of the exit intersection determined in method 400, so as to control the posture and / or speed of the vehicle when passing through the intersection.

[0125] Figure 10 A further schematic flowchart of the autonomous driving method provided in an embodiment of this application is shown. This method 600 can be applied to... Figure 1 In the intelligent driving device shown, or the method can be provided by Figure 2 The system shown is executed. More specifically, the method 600 may include S601 and S602. S601 and S602 may be executed by the planning and control module 240; or, S601 may be executed by the sensing module 210, and S602 may be executed by the planning and control module 240.

[0126] S601 determines the navigation information source based on the perception results of road information at the exit.

[0127] For example, the navigation information source refers to information used to control the vehicle's position and / or speed so that the vehicle moves toward the exit. The navigation information source may include one or more of the following: traction road, fuzzy road, and clear lane. The fuzzy road indicates the boundary of the road at the exit as determined based on environmental information perceived by the vehicle, while the clear lane indicates the position of the lane lines at the exit as determined based on environmental information perceived by the vehicle.

[0128] Understandably, when a vehicle is far from the exit, it may not be able to obtain effective environmental information at the exit. "Effective environmental information at the exit" refers to perceptual information (such as images) that can extract road boundaries and / or lane lines. As the vehicle moves closer to the exit, it can obtain effective environmental information. For example, as the distance between the vehicle and the exit decreases, the fuzzy road and the clear lane can be extracted sequentially from the environmental information at the exit. For instance, when the vehicle is 50 meters away from the exit, it may be possible to extract the fuzzy road from the environmental information at the exit, but due to limitations in perception accuracy, it may not be possible to extract the clear lane; when the vehicle is 20 meters away from the exit, it may be possible to extract the clear lane from the environmental information at the exit.

[0129] When the vehicle does not obtain information on fuzzy roads and clear lanes, it can determine the towing road as the navigation information source; when the vehicle obtains information on fuzzy roads or clear lanes, it can switch to the fuzzy road or clear lane as the navigation information source. For example, the navigation information source can be determined and / or switched according to the contents shown in Table 1.

[0130] Table 1

[0131]

[0132] In some implementations, before switching navigation information sources, the decision to switch can be determined based on the reliability (or confidence level) of the target. The navigation information source is switched only if the reliability of the target is greater than or equal to a reliability threshold. Here, the target refers to the navigation information source after the switch. For example, when switching from a traction road to a vague road, the vague road is the target. If information about the vague road is obtained, but information about the clear lane is not obtained, and the reliability of the vague road is greater than or equal to the reliability threshold, the navigation information source is switched from the traction road to the vague road.

[0133] For example, the reliability of switching targets can be determined using the formula P = aP1 + bP2, where P represents reliability, P1 represents reliability determined based on statistical data, P2 represents reliability determined based on a machine learning algorithm, a and b are the weights of P1 and P2 respectively, and a and b take values ​​from 0 to 1, with a and b summing to 1. More specifically, the statistical data can be the result of statistical analysis of the location information of multiple frames of blurred roads (or clear lanes), for example, determining the variance of the samples including location information from multiple frames, and determining P1 based on the variance. It can be understood that the larger the variance, the greater the deviation between the location information of different frames, therefore the collected results can be considered unstable, and P1 will be smaller. The model used by the machine learning algorithm can be trained based on the location of manually labeled road boundaries (or lane lines). By inputting the location of the road boundaries (or lane lines) collected by the vehicle's perception system into the above model, the deviation between the data collected by the vehicle's perception system and the actual road (or lane) location can be obtained, and P2 can be determined based on this deviation. Understandably, the larger the deviation, the less accurate the results collected by the vehicle, and the smaller P2 will be.

[0134] For example, when the navigation information source is a traction road and the reliability of the ambiguous road is greater than the reliability threshold 1, the system switches from the traction road to the ambiguous road; when the navigation information source is either a traction road or an ambiguous road and the reliability of the clear lane is greater than the reliability threshold 2, the system switches from the traction road or the ambiguous road to the clear lane. The reliability threshold 2 is greater than the reliability threshold 1. For example, the reliability threshold 2 can be a value between 0.6 and 1, and the reliability threshold 1 can be a value between 0.3 and 0.5. Alternatively, when the method for determining the reliability threshold is different, the reliability thresholds 1 and 2 can also be other values.

[0135] In some implementations, the next navigation information source switch is performed after a certain period of time following the initial navigation information source switch. This period of time can be 15 seconds, 20 seconds, or other durations.

[0136] S602 controls the vehicle's position and / or speed in the main lane based on navigation information sources.

[0137] In some implementations, when the navigation information source is a towing road, the vehicle's orientation upon entering the intersection is controlled according to the location and direction of the exit indicated by the towing road. This ensures that the vehicle, while traveling along the main lane, moves in the direction indicated by the towing road, avoiding collisions with the road boundary at the exit location or conflicts with traffic in the opposite lane. Furthermore, when the navigation information source is a towing road, the vehicle is controlled to travel at speed 1. For example, speed 1 can be any speed between 5 kilometers per hour (kph) and 15 kph. This allows the vehicle sufficient time to acquire road information (such as road boundaries, lane lines, etc.) at the intersection through the perception system.

[0138] When the navigation information source is a vague road, the vehicle can be controlled to travel at speed 2; when the navigation information source is a clear lane, the vehicle can be controlled to travel at speed 3. Speed ​​2 can be any speed between 20 kph and 30 kph; speed 3 can be any speed between 30 kph and 60 kph. It should be understood that speeds 1, 2, and 3 are merely illustrative examples. In actual implementation, speeds 1, 2, and 3 can also be speeds determined based on the intersection's maximum speed limit. For example, speed 1 can be less than or equal to 70% or 80% of the intersection's maximum speed limit; speed 2 can be less than or equal to 80% or 90% of the intersection's maximum speed limit; and speed 3 can be less than or equal to the intersection's maximum speed limit. The intersection's maximum speed limit can be the maximum speed limit of the intersection itself, or it can be the maximum speed limit of the road connected to the intersection, such as the maximum speed limit of the vehicle's current route (e.g., the road into the intersection) or the maximum speed limit of the vehicle's target route (e.g., the road out of the intersection). Alternatively, speed 1, speed 2, and speed 3 can be other values ​​that satisfy the following conditions: speed 1 is less than or equal to speed 2, and speed 2 is less than speed 3.

[0139] In some implementations, there is a certain deviation between the location of the traction road and the location of the fuzzy road or clear lane indication. In this case, when the navigation information source is a fuzzy road or a clear lane, the vehicle's posture can be further adjusted according to the fuzzy road and clear lane to improve navigation accuracy.

[0140] The autonomous driving method provided in this application can control the vehicle's pose and / or speed based on the vehicle's perception of the road at the exit intersection. When the vehicle cannot obtain road information at the boundary of the exit intersection, the vehicle's pose is controlled according to traction information, and the vehicle is controlled to travel at a slower speed, allowing the vehicle sufficient time to acquire and process road information at the boundary of the exit intersection, thereby increasing the probability of acquiring clear road information (such as clear road boundaries and / or lane lines). As the vehicle approaches the boundary of the exit intersection, the reliability and accuracy of the road information perceived by the vehicle at the boundary of the exit intersection gradually improve. At this time, controlling the vehicle's pose based on the fuzzy road and / or clear lane helps to improve navigation accuracy, and controlling the vehicle to travel at a faster speed based on the fuzzy road and / or clear lane helps to improve the efficiency of the vehicle passing through the intersection.

[0141] It should be noted that the terms "fuzzy road" and "clear lane" mentioned above are only used to distinguish the types of perceived information. "Fuzzy road" indicates the road boundary perceived by the vehicle, while "clear lane" indicates the lane boundary (such as lane lines) perceived by the vehicle. "Fuzzy" and "clear" should not be interpreted as limitations on the clarity of the perceived results.

[0142] Figure 11 A further schematic flowchart of the autonomous driving method provided in an embodiment of this application is shown. This method 700 can be applied to... Figure 1 In the intelligent driving device shown, or the method can be provided by Figure 2 The system execution is shown. More specifically, method 700 may include S710 and S720.

[0143] S710, acquire traction information. The traction information indicates the direction and position of the intelligent driving device exiting the first intersection. The traction information is determined based on the SD map navigation information, which indicates the navigation route from the current position of the intelligent driving device to the target position.

[0144] For example, the intelligent driving device may include the vehicle in the above embodiments, or it may be other intelligent driving devices; the first intersection may include intersection a in the above embodiments; the traction information may include the location and direction of the exit determined by method 400, or the traction information may also include the traction road determined by method 500; the SD map navigation information may include the SD map navigation route in the above embodiments.

[0145] S720, based on traction information, controls the speed and / or position of the intelligent driving equipment as it approaches or passes through the first intersection.

[0146] In some implementations, the intelligent driving device acquires second road information within a first range, the second road information indicating multiple perceived routes passing through the first intersection; determines the main lane from the multiple perceived routes based on SD map navigation information; and controls the speed and / or posture of the intelligent driving device as it approaches or passes through the first intersection, including controlling the speed and / or posture of the intelligent driving device while it travels along the main lane.

[0147] For example, the first range may include the preset range in method 300. When the first intersection is intersection a, the multiple sensing routes may include: a route from road A to road B via intersection a, a route from road A to road C via intersection a, and a route from road A to road D via intersection a. The specific implementation of determining the multiple sensing routes can be found in the description of method 300, and will not be repeated here.

[0148] In some implementations, controlling the speed and / or orientation of the intelligent driving device toward or through the first intersection based on traction information includes: controlling the intelligent driving device to move toward or through the first intersection at a first speed based on the traction information. For example, the first speed may include speed 1 in method 600.

[0149] In some implementations, the first boundary of the first intersection is the target exit boundary of the intelligent driving device, and the first boundary is connected to the first road. The method further includes: acquiring information about the first road perceived by the intelligent driving device; and controlling the speed and / or posture of the intelligent driving device to or through the first intersection based on traction information, including: controlling the speed and / or posture of the intelligent driving device based on the information of the first road and the traction information.

[0150] For example, when the first intersection is intersection a, the first boundary can be... Figure 7 The boundary c shown in (c) is connected to the first boundary. The first road is road B. The information of the first road includes the information of the aforementioned ambiguous road and / or clear lane.

[0151] In some implementations, the information of the first road indicates the boundary of the first road and / or the position of the lane lines in the first road; controlling the speed and / or posture of the intelligent driving device based on the information of the first road and traction information includes: when the reliability of the information of the first road is less than or equal to a preset threshold, controlling the intelligent driving device to travel at a first speed based on the traction information; or, when the reliability of the information of the first road is greater than the preset threshold, controlling the intelligent driving device to switch from the first speed to a second speed based on the information of the first road; wherein the first speed is less than or equal to the second speed.

[0152] For example, when the information of the first road indicates the boundary of the first road (e.g., the information of the first road is ambiguous), the second speed may include the speed 2 described above; when the information of the first road indicates the position of the lane lines in the first road (e.g., the information of the first road is clear lane information), the second speed may include the speed 3 described above. For a more specific implementation of controlling the speed of the intelligent driving device based on the information of the first road and the traction information, please refer to the description in method 600, which will not be repeated here.

[0153] In some implementations, the traction information includes the traction road. The method also includes: obtaining the boundary information of the first intersection; determining the direction of exiting the first intersection based on the SD map navigation information; determining the position of exiting the first intersection based on the boundary information and the direction of exiting the first intersection; and generating the traction road based on the position and direction of exiting the first intersection.

[0154] For example, the boundary information of the first intersection may include the intersection boundary in method 400, the specific implementation of determining the direction of exiting the first intersection, and the specific implementation of determining the position of exiting the first intersection, which can be referred to in the description of method 400 and will not be repeated here. Furthermore, the method for generating the traction road can be referred to in the description of method 500 and will not be repeated here.

[0155] In some implementations, a traction road is generated based on the position and direction of exiting the first intersection, including: generating an initial traction road based on the position and direction of exiting the first intersection; correcting the initial traction road based on the position of the first road connected to the boundary where the intelligent driving device exits the first intersection, to obtain the traction road.

[0156] For example, the location of the first road may be indicated by information about the first road, and the method for correcting the initial traction road according to the location of the first road can be referred to the description in S502, which will not be repeated here.

[0157] In some implementations, the method further includes: controlling the display device to display a navigation interface, the navigation interface including an image of the towing road and a first icon, the first icon indicating the position of the intelligent driving device relative to the towing road.

[0158] In some implementations, the navigation interface also includes an image of the boundary of the first road. In practice, the image of the boundary of the first road can gradually change as the intelligent driving device approaches the first boundary of the first intersection, for example, gradually becoming clearer.

[0159] In some implementations, when the lane line information of the first road is obtained, the navigation interface is switched from the image of the traction road to the image of the lane lines of the first road.

[0160] In some implementations, when the lane line information of the first road is obtained, the navigation interface can switch from displaying an image of the boundary of the first road to displaying an image of the lane lines of the first road.

[0161] The autonomous driving method provided in this application can improve the autonomous driving capability of intelligent driving devices without relying on high-precision maps. Specifically, when the intelligent driving device is in autonomous driving mode and is too far from the intersection to obtain road information (such as road boundaries or lane lines for autonomous driving navigation) through sensing devices, or when the obtained sensing information is not accurate enough, the device can determine the intersection location in advance based on traction information, thereby adjusting the vehicle's heading and preventing the intelligent driving device from getting stuck near the intersection. This improves the pass rate and success rate of the intelligent driving device at intersections without high-precision maps, and reduces the probability of the intelligent driving device entering the oncoming lane or scraping the road boundary when exiting the intersection. In addition, by controlling the intelligent driving device to travel at a lower speed based on traction information, the intelligent driving device has sufficient time to obtain road information at the first boundary of the first intersection, thereby increasing the probability of obtaining clear road information (such as clear lane lines), which helps to improve the control capability and driving safety of the intelligent driving device during autonomous driving. As the autonomous driving device approaches the first boundary, the reliability and accuracy of the first road information acquired by the device gradually improve. Controlling the autonomous driving device's posture based on the first road information (such as first road information) obtained by the vehicle's perception system is more reliable than controlling it based on traction information, helping to prevent the device from crossing lane lines or road boundary lines while traveling on the first road. Furthermore, controlling the autonomous driving device to travel at a higher speed based on the first road information can also improve the speed and efficiency of the device passing through the first intersection.

[0162] 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.

[0163] The above text combines Figures 1 to 11 The autonomous driving method provided in the embodiments of this application is described in detail below. Figure 12 and Figure 13 The apparatus provided in the embodiments of this application is described in detail. It should be understood that the description of the apparatus embodiments corresponds to the description of the method embodiments. Therefore, for content not described in detail, please refer to the method embodiments above. For the sake of brevity, it will not be repeated here.

[0164] Figure 12A schematic block diagram of an autonomous driving device 2000 provided in an embodiment of this application is shown. The device 2000 may include units for executing methods 300, 400, 500, 600, and 700. Furthermore, each unit in the device 2000 implements a corresponding process of the above-described 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The apparatuses described above are capable of implementing the corresponding steps performed by the computing platform 150 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 processing units, can be replaced by a processor, used to execute the relevant processing operations in each method embodiment.

[0169] For example, the acquisition unit 2010 and the processing unit 2020 can be set in Figure 1 The intelligent driving device 100 shown, or it can also be set in Figure 2 In the system shown, more specifically, the acquisition unit 2010 and processing unit 2020 can be located in the road control module 240, or they can also be located in the road information generation module 230. Exemplarily, the operations performed by the acquisition unit 2010 and processing unit 2020 can be executed by a single processor, or they can be executed by different processors. In specific implementation, the one or more processors can be located in... Figure 1The processor in the intelligent driving device 100 shown; or, the device 2000 described above may be a chip disposed in the intelligent driving device 100.

[0170] 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).

[0171] Figure 13 This is another schematic block diagram of the autonomous driving device provided in the embodiments of this application. Figure 13 The illustrated autonomous driving device 2100 may include a processor 2110, a transceiver 2120, and a memory 2130. The processor 2110, transceiver 2120, and memory 2130 are connected via internal interconnection paths. The memory 2130 stores instructions, and the processor 2110 executes the instructions stored in the memory 2130 to implement the methods described in the above embodiments. Optionally, the memory 2130 may be coupled to the processor 2110 via an interface or integrated with the processor 2110.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] This application also provides an intelligent driving device, which includes the autonomous driving device 2000 or autonomous driving device 2100 in the above embodiments.

[0176] 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.

[0177] 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.

[0178] This application also provides a chip, including circuitry, for performing the methods described in the above embodiments of this application.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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. An autonomous driving method, characterized in that, include: Acquire second road information within a first range perceived by the intelligent driving device, the second road information indicating multiple perceived routes passing through the first intersection; The main route is determined from the multiple perceived routes based on standard SD map navigation information, which indicates the navigation route from the current location of the intelligent driving device to the target location; Acquire traction information, which indicates the direction and position of the intelligent driving device exiting the first intersection; The traction information includes the traction road, and the acquisition of the traction information includes: Obtain the boundary information of the first intersection; Determine the direction to exit the first intersection based on the SD map navigation information; Based on the boundary information and the direction of exiting the first intersection, determine the position of exiting the first intersection; Based on the position and direction of exiting the first intersection, the traction road is generated; based on the traction information, the speed and / or posture of the intelligent driving device as it travels along the main road toward or through the first intersection are controlled.

2. The method according to claim 1, characterized in that, The step of controlling the speed and / or position of the intelligent driving device to move along the main road toward or through the first intersection based on the traction information includes: Based on the traction information, the intelligent driving device is controlled to drive toward or through the first intersection at a first speed, wherein the first speed is less than or equal to a speed threshold.

3. The method according to claim 1 or 2, characterized in that, The first boundary of the first intersection is the target exit boundary of the intelligent driving device, and the first boundary is connected to the first road. The method further includes: Obtain information about the first road as perceived by the intelligent driving device; The step of controlling the speed and / or position of the intelligent driving device to move along the main road toward or through the first intersection based on the traction information includes: Based on the information from the first road and the traction information, the speed and / or posture of the intelligent driving device are controlled.

4. The method according to claim 3, characterized in that, The information of the first road indicates the boundary of the first road and / or the position of the lane lines in the first road; The step of controlling the speed and / or posture of the intelligent driving device based on the information of the first road and the traction information includes: When the reliability of the information on the first road is less than or equal to a preset threshold, the intelligent driving device is controlled to travel at a first speed based on the traction information; or, When the reliability of the information of the first road is greater than the preset threshold, the intelligent driving device is controlled to switch from the first speed to the second speed based on the information of the first road. Wherein, the first speed is less than or equal to the second speed.

5. The method according to claim 1 or 2, characterized in that, The step of generating the traction road based on the position and direction of exiting the first intersection includes: An initial traction road is generated based on the position and direction of exiting the first intersection; The initial traction road is corrected based on the position of the first road connected to the boundary of the first intersection where the intelligent driving device exits, thus obtaining the traction road.

6. The method according to claim 1 or 2, characterized in that, The method further includes: The control display device displays a navigation interface, which includes an image of the towing road and a first icon indicating the position of the intelligent driving device relative to the towing road.

7. The method according to claim 6, characterized in that, The navigation interface also includes an image of the boundary of a first road, which is the road that connects to the boundary of the target exit of the intelligent driving device at the first intersection.

8. The method according to claim 6, characterized in that, The method further includes: When the lane line information of the first road is obtained, the navigation interface is controlled to switch from the image of the traction road to the image of the lane line of the first road, which is the road that connects to the target exit boundary of the intelligent driving device in the first intersection.

9. An automatic driving device, characterized in that, include: The acquisition unit is used to: acquire second road information within a first range perceived by the intelligent driving device, wherein the second road information indicates multiple perceived routes passing through the first intersection; The processing unit is configured to: determine the main route from the multiple perceived routes based on standard SD map navigation information, wherein the SD map navigation information indicates the navigation route from the current position of the intelligent driving device to the target position; The acquisition unit is also used to: acquire traction information, the traction information indicating the direction and position of the intelligent driving device exiting the first intersection; The traction information includes the traction road, and the acquisition unit is further configured to: Obtain the boundary information of the first intersection; The processing unit is used to: determine the direction of exiting the first intersection based on the SD map navigation information; Based on the boundary information and the direction of exiting the first intersection, determine the position of exiting the first intersection; The traction road is generated based on the position and direction of exiting the first intersection; The processing unit is further configured to: control the speed and / or posture of the intelligent driving device as it approaches or passes through the first intersection, based on the traction information.

10. The apparatus according to claim 9, characterized in that, The processing unit is used for: Based on the traction information, the intelligent driving device is controlled to travel at a first speed, which is less than or equal to a speed threshold.

11. The apparatus according to claim 9 or 10, characterized in that, The first boundary of the first intersection is the target exit boundary of the intelligent driving device, and the first boundary is connected to the first road. The acquisition unit is further configured to: Obtain information about the first road as perceived by the intelligent driving device; The processing unit is used for: Based on the information from the first road and the traction information, the speed and / or posture of the intelligent driving device are controlled.

12. The apparatus according to claim 11, characterized in that, The information of the first road indicates the boundary of the first road and / or the position of the lane lines in the first road; The processing unit is used for: When the reliability of the information on the first road is less than or equal to a preset threshold, the intelligent driving device is controlled to travel at a first speed based on the traction information; or, When the reliability of the information of the first road is greater than the preset threshold, the intelligent driving device is controlled to switch from the first speed to the second speed based on the information of the first road. Wherein, the first speed is less than or equal to the second speed.

13. The apparatus according to claim 9 or 10, characterized in that, The processing unit is used for: An initial traction road is generated based on the position and direction of exiting the first intersection; The initial traction road is corrected based on the position of the first road connected to the boundary of the first intersection where the intelligent driving device exits, thus obtaining the traction road.

14. The apparatus according to claim 9 or 10, characterized in that, The processing unit is also used for: The control display device displays a navigation interface, which includes an image of the towing road and a first icon indicating the position of the intelligent driving device relative to the towing road.

15. The apparatus according to claim 14, characterized in that, The navigation interface also includes an image of the boundary of a first road, which is the road that connects to the boundary of the target exit of the intelligent driving device at the first intersection.

16. The apparatus according to claim 14, characterized in that, The processing unit is also used for: When the lane line information of the first road is obtained, the navigation interface is controlled to switch from the image of the traction road to the image of the lane line of the first road, where the first road is the road connecting to the target exit boundary of the intelligent driving device at the first intersection.

17. An automatic driving 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. The apparatus according to claim 17, characterized in that, The device also includes the memory.

19. An intelligent driving device, characterized in that, Includes the apparatus as described in any one of claims 9 to 18.

20. 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.

21. 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.

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

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