Autonomous driving method and apparatus, and intelligent driving device

Abstract

An autonomous driving method and apparatus, and an intelligent driving device. The method comprises: on the basis of SD map information, determining passability information, wherein the passability information indicates the passability of each section among a plurality of sections in the direction of width of a first sub-road, the SD map information at least indicates a driving bias in the first sub-road and / or a driving direction at an intersection ahead of the first sub-road within a first range of a vehicle, and the first sub-road is a sub-road between two adjacent intersections, or is a sub-road between the current position of the vehicle and the nearest intersection ahead; and on the basis of perception information of the vehicle and the passability information, controlling the vehicle to travel in the first sub-road. The technical solution of the present application can be applied to the fields of intelligent vehicles such as electric vehicles and new energy vehicles, can realize lane-level navigation without relying on high-precision maps, facilitates reducing the manufacturing and maintenance costs of maps required for autonomous driving, and expands the application scope of autonomous driving technology.

Description

Automatic driving method, device and intelligent driving equipment

[0001] The present application claims priority to the Chinese patent application No. 202410644557.2, filed on May 21, 2024, and entitled "Automatic driving method, device and intelligent driving equipment", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the field of intelligent driving, and more particularly, to an automatic driving method, device and intelligent driving equipment. BACKGROUND

[0003] With the rapid development of the automobile industry, a lot of assisted driving and automatic driving technologies have been developed, which can reduce driving stress, improve safety and traffic efficiency. Current automatic driving technologies mostly rely on high-precision maps for navigation. However, high-precision maps have drawbacks such as high cost, long time consumption, insufficient coverage, and difficulty in ensuring data freshness, which makes it difficult to promote automatic driving technologies relying on high-precision maps in the national or global range.

[0004] In view of this, an automatic driving scheme independent of high-precision maps is urgently needed to be developed. SUMMARY

[0005] The present application provides an automatic driving method, device and intelligent driving equipment, which can realize lane-level navigation without relying on high-precision maps, thereby helping to reduce the production and maintenance costs of maps required for automatic driving, and helping to improve the application range of automatic driving technology.

[0006] In a first aspect, an automatic driving method is provided, which can be executed by an intelligent driving equipment, for example, can be executed by a computing platform of the intelligent driving equipment, or by a chip or circuit for the intelligent driving equipment; or the method can also be executed by a cloud server associated with the intelligent driving equipment.

[0007] The method comprises: acquiring standard definition (SD) map navigation information, the SD map navigation information at least indicating a driving bias in at least one sub-road within a first range of the intelligent driving device and / or a driving direction at at least one intersection, wherein a first sub-road in the at least one sub-road is a sub-road between two adjacent intersections or a sub-road between a current position of the intelligent driving device and a nearest adjacent intersection in front; determining first traffic degree information according to the SD map navigation information, the first traffic degree information indicating a traffic degree of each of a plurality of parts in a width direction of the first sub-road; and controlling the intelligent driving device to drive in the first sub-road according to perception information of the intelligent driving device and the first traffic degree information, wherein the perception information indicates positions of a plurality of lanes in the first sub-road and a position of a lane in which the intelligent driving device is located.

[0008] The perception information of the intelligent driving device can be determined according to information collected by a perception system of the intelligent driving device, for example, the information collected by the perception system of the intelligent driving device can be an image of the first sub-road collected by a camera of the intelligent driving device, and the perception information can be obtained by image processing on the image of the first sub-road collected by the camera. The SD map navigation information indicates a road-level driving route of the intelligent driving device from a current position to a target position, that is, which sub-road the intelligent driving device needs to change to. It can be understood that the first sub-road is a sub-road that the intelligent driving device passes through when driving to the target position.

[0009] In some implementations, the SD map navigation information further indicates a number of lanes in the first sub-road and a guide of each lane, and the method further comprises: determining a plurality of parts in the width direction of the first sub-road according to the guide of each lane in the first sub-road. Alternatively, the SD map navigation information does not indicate the number of lanes in the first sub-road and the guide of each lane, and the method further comprises: dividing the first sub-road into a plurality of parts along the width direction. The first traffic degree information is determined according to the SD map navigation information, comprising: determining the first traffic degree information according to the driving bias of the first sub-road and / or according to the driving direction at the intersection in front of the first sub-road.

[0010] In the above technical solution, the intelligent driving device is controlled to drive in a certain lane of a sub-road to a target position according to the SD map navigation information and the perception information of the intelligent driving device, without relying on a high-precision map, which helps to reduce the production and maintenance cost of a map required for automatic driving, helps to improve the geographical range to which the automatic driving technology can be applied, and can improve the robustness of the navigation function of the intelligent driving device.

[0011] In some implementations of the first aspect, the first sub-road is a sub-road currently located by the intelligent driving device, the at least one sub-road further includes a second sub-road, the second sub-road is located between the target position of the intelligent driving device and the first sub-road, the first traffic degree information is determined according to the SD map navigation information, and the determining includes: determining second traffic degree information according to a driving bias in the second sub-road and / or a driving direction at a second intersection, the second traffic degree information indicating a traffic degree of each of a plurality of portions in a width direction of the second sub-road, the second intersection being an intersection between the second sub-road and a nearest neighboring sub-road in front of the second sub-road; determining third traffic degree information according to a driving bias in the first sub-road and / or a driving direction at a first intersection, the third traffic degree information indicating a second traffic degree of each of a plurality of portions in a width direction of the first sub-road; and performing a first processing on the second traffic degree information and the third traffic degree information to obtain the first traffic degree information.

[0012] It should be noted that the higher the navigation cost is, the more difficult it is to drive in the driving direction indicated by the SD map navigation information at the first intersection.

[0013] In some implementations, the intelligent driving device can be controlled to drive in a lane with the minimum navigation cost among the plurality of lanes.

[0014] In the above technical solution, the navigation cost is determined, and the intelligent driving device is controlled to drive in a lane with a lower navigation cost, which helps to improve the traffic efficiency of the intelligent driving device at the intersection and the success rate of driving to the target position according to the navigation.

[0015] In some implementations of the first aspect, the first sub-road is a sub-road currently located by the intelligent driving device, the at least one sub-road further includes a second sub-road, the second sub-road is located between the target position of the intelligent driving device and the first sub-road, the first traffic degree information is determined according to the SD map navigation information, and the determining includes: determining second traffic degree information according to a driving bias in the second sub-road and / or a driving direction at a second intersection, the second traffic degree information indicating a traffic degree of each of a plurality of portions in a width direction of the second sub-road, the second intersection being an intersection between the second sub-road and a nearest neighboring sub-road in front of the second sub-road; determining third traffic degree information according to a driving bias in the first sub-road and / or a driving direction at a first intersection, the third traffic degree information indicating a second traffic degree of each of a plurality of portions in a width direction of the first sub-road; and performing a first processing on the second traffic degree information and the third traffic degree information to obtain the first traffic degree information.

[0016] In some implementations, the determining of the third traffic degree information according to the driving bias in the first sub-road and / or the driving direction at the first intersection includes: determining a drivable interval of the first sub-road according to the driving bias in the first sub-road and / or the driving direction at the first intersection, and mapping the traffic probability of the second sub-road to the drivable interval of the first sub-road to obtain the mapped third traffic degree information.

[0017] In the technical solution, by superimposing the traffic degree information of the multiple sub-roads, for the road beyond the sensing distance of the intelligent driving device, timely and more accurate lane-level navigation guidance can be provided, redundant lane changing operations are reduced, and the probability that the lane changing results in failure to drive according to the navigation is reduced.

[0018] With reference to the first aspect, in some implementations of the first aspect, the perception information further indicates lane types of the lanes in the first sub-road; and determining the navigation cost of each lane of the multiple lanes according to the first traffic degree information comprises: determining the navigation cost of each lane of the multiple lanes according to the first traffic degree information and the lane types.

[0019] With reference to the first aspect, in some implementations of the first aspect, determining the navigation cost of each lane of the multiple lanes according to the first traffic degree information and the lane types comprises: when the lane type of the first lane of the multiple lanes is at least one of the following: a tidal lane, a variable lane, a bus lane, a temporarily-stopping lane, an emergency lane, and an emergency lane, increasing the navigation cost of the first lane.

[0020] In the technical solution, the intelligent driving device can avoid special lanes during driving, which not only improves the traffic efficiency, but also improves the human-like nature of the intelligent driving device, thereby improving the driving experience of the user of the intelligent driving device.

[0021] With reference to the first aspect, in some implementations of the first aspect, determining the navigation cost of each lane of the multiple lanes according to the first traffic degree information comprises: when the first sub-road is connected to a third sub-road, and the intelligent driving device needs to turn from the third sub-road to the first sub-road, reducing the navigation cost of a second lane of the multiple lanes, the second lane being a lane on the side corresponding to the turning direction of the multiple lanes.

[0022] In the technical solution, when the intelligent driving device turns, the intelligent driving device is controlled to turn inwards, reducing the influence on vehicles driving on other lanes, which not only improves the driving safety, but also improves the human-like nature of the intelligent driving device.

[0023] With reference to the first aspect, in some implementations of the first aspect, the navigation cost of a third lane and a fourth lane of the multiple lanes are both less than or equal to a cost threshold, and the intelligent driving device is controlled to drive in a lane of the multiple lanes whose navigation cost is less than or equal to the cost threshold, comprising: when the road right of the third lane is higher than the road right of the fourth lane, the intelligent driving device is controlled to drive in the third lane.

[0024] In the technical solution, the intelligent driving device is controlled to drive in a lane with higher road right, which helps to improve the traffic efficiency of the intelligent driving device and reduce invalid navigation lane changing.

[0025] In a second aspect, an automatic driving apparatus is provided, which includes: an acquisition unit configured to acquire SD map navigation information, the SD map navigation information indicating at least a driving bias in at least one sub-road within a first range of the intelligent driving device and / or a driving direction at at least one intersection, wherein a first sub-road in the at least one sub-road is a sub-road between two adjacent intersections or a sub-road between a current position of the intelligent driving device and a nearest adjacent intersection in front; and a processing unit configured to determine first passability degree information according to the SD map navigation information, the first passability degree information indicating a passability degree of each of a plurality of portions in a width direction of the first sub-road, and control the intelligent driving device to drive in the first sub-road according to perception information of the intelligent driving device and the first passability degree information, wherein the perception information indicates positions of a plurality of lanes in the first sub-road and a position of a lane in which the intelligent driving device is located.

[0026] With reference to the second aspect, in some implementations of the second aspect, the processing unit is configured to determine a navigation cost of each of the plurality of lanes according to the first passability degree information, the navigation cost indicating a difficulty of driving in the lane to the first intersection and then driving in a direction indicated by the driving direction at the first intersection, wherein the first intersection is an intersection between the first sub-road and a nearest adjacent sub-road in front of the first sub-road, and control the intelligent driving device to drive in a lane in which the navigation cost is less than or equal to a cost threshold.

[0027] With reference to the second aspect, in some implementations of the second aspect, the first sub-road is a sub-road in which the intelligent driving device is currently located, and the at least one sub-road further includes a second sub-road between a target position of the intelligent driving device and the first sub-road, and the processing unit is configured to determine second passability degree information according to a driving bias in the second sub-road and / or a driving direction at a second intersection, the second passability degree information indicating a passability degree of each of a plurality of portions in a width direction of the second sub-road, wherein the second intersection is an intersection between the second sub-road and a nearest adjacent sub-road in front of the second sub-road, determine third passability degree information according to the driving bias in the first sub-road and / or the driving direction at a first intersection, the third passability degree information indicating a second passability degree of each of a plurality of portions in a width direction of the first sub-road, and perform a first processing on the second passability degree information and the third passability degree information to obtain the first passability degree information.

[0028] With reference to the second aspect, in some implementations of the second aspect, the perception information further indicates a lane type of each lane in the first sub-road, and the processing unit is configured to determine the navigation cost of each of the plurality of lanes according to the first passability degree information and the lane type.

[0029] In some implementations of the second aspect, in response to the lane type of the first lane in the plurality of lanes being at least one of a following: a tidal lane, a variable lane, a bus lane, a stop-able lane, an escape lane, and an emergency lane, the processing unit is configured to increase the navigation cost of the first lane.

[0030] In some implementations of the second aspect, in response to the first sub-road being connected to the third sub-road and the intelligent driving device needing to turn from the third sub-road to the first sub-road, the processing unit is configured to decrease the navigation cost of a second lane in the plurality of lanes, the second lane being a lane on a side corresponding to a turning direction in the plurality of lanes.

[0031] In some implementations of the second aspect, the navigation cost of a third lane and a fourth lane in the plurality of lanes are both less than or equal to a cost threshold, and in response to the road right of the third lane being higher than the road right of the fourth lane, the processing unit is configured to control the intelligent driving device to travel in the third lane.

[0032] In some implementations of the second aspect, the SD map navigation information further indicates a number of lanes in the first sub-road and a direction of each lane, and the processing unit is further configured to determine a plurality of sections in a width direction of the first sub-road according to the direction of each lane in the first sub-road.

[0033] In a third aspect, an automatic driving device is provided, and the device includes a processor configured to execute a computer program stored in a memory to cause the device to perform the method in any possible implementation of the first aspect.

[0034] In some implementations of the third aspect, the automatic driving device further includes the memory.

[0035] In a fourth aspect, an intelligent driving device is provided, and the intelligent driving device includes the device in any possible implementation of the second aspect or the third aspect.

[0036] In some implementations of the fourth aspect, the intelligent driving device is a vehicle.

[0037] In a fifth aspect, a computer program product is provided, and the computer program product includes computer program code that, when executed on a computer or a processor, causes the computer or the processor to perform the method in any possible implementation of the first aspect.

[0038] It should be noted that the computer program code can be stored in whole or in part on a storage medium, and the storage medium can be packaged together with the processor or packaged separately from the processor.

[0039] In a sixth aspect, a computer readable medium is provided, and the computer readable medium stores instructions which, when executed by a processor, cause the processor to implement the method in any possible implementation of the first aspect.

[0040] In a seventh aspect, a chip is provided, and the chip comprises a circuit configured to execute the method in any possible implementation of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0041] FIG. 1 is a functional schematic block diagram of an intelligent driving device according to an embodiment of the present application;

[0042] FIG. 2 is a schematic diagram of an autonomous driving system architecture according to an embodiment of the present application;

[0043] FIG. 3 is a schematic flowchart of an autonomous driving method according to an embodiment of the present application;

[0044] FIG. 4 is a schematic diagram of SD map navigation information according to an embodiment of the present application;

[0045] FIG. 5 is a schematic diagram of a sub-road division result according to an embodiment of the present application;

[0046] FIG. 6 is another schematic diagram of a sub-road division result according to an embodiment of the present application;

[0047] FIG. 7 is another schematic flowchart of an autonomous driving method according to an embodiment of the present application;

[0048] FIG. 8 is a schematic diagram of an application scenario of an autonomous driving method according to an embodiment of the present application;

[0049] FIG. 9 is another schematic diagram of an application scenario of an autonomous driving method according to an embodiment of the present application;

[0050] FIG. 10 is another schematic diagram of an application scenario of an autonomous driving method according to an embodiment of the present application;

[0051] FIG. 11 is another schematic diagram of an application scenario of an autonomous driving method according to an embodiment of the present application;

[0052] FIG. 12 is another schematic flowchart of an autonomous driving method according to an embodiment of the present application;

[0053] FIG. 13 is a schematic block diagram of an autonomous driving device according to an embodiment of the present application;

[0054] FIG. 14 is another schematic block diagram of an autonomous driving device according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] Before introducing the scheme of the present application, first, the related concepts involved in the present application are introduced:

[0056] 1. SD map: the precision is generally meter level, and the richness is low. Generally, it mainly includes road information, point of information (or point of interest, POI), etc. The POI is point class data in the electronic map, and at least includes four attributes of name, address, coordinate and category.

[0057] 2. High definition (HD) map: the precision and richness of the HD map are higher than those of the SD map. The absolute precision and the relative precision of the HD map are both centimeter level. In terms of richness, the HD map provides an environment model in which an autonomous vehicle is located, including a static high-precision map and other dynamic information. The static high-precision map includes a lane model, road components, road attributes, etc. The lane model includes road details, such as lane lines, lane center lines, lane attribute changes, etc. The other dynamic information includes all dynamic information under an intelligent network system, such as map dynamic information, sensor information, driving behavior, traffic dynamic information control, etc.

[0058] 3. Navigation event: indicating the driving direction of a vehicle at an intersection, or also indicating the driving bias of a vehicle between two intersections.

[0059] An autonomous vehicle needs lane-level accurate navigation when driving to handle complex scenarios such as left and right turns at intersections, on-ramps and off-ramps, etc. The HD map contains detailed topological information of each lane on the road, which facilitates the selection of a reasonable lane when navigating. Therefore, most autonomous driving technologies rely on high-precision maps for navigation. However, high-precision maps have drawbacks such as high cost, long time-consuming, insufficient coverage, and difficulty in guaranteeing data freshness, which makes it difficult for autonomous driving technologies relying on high-precision maps to be popularized in a nationwide or global range.

[0060] In view of this, the embodiments of the present application provide an autonomous driving method, device and intelligent driving equipment, which can determine the passable degree of each part of a sub-road in a first range according to the driving bias of the intelligent driving equipment in the sub-road and / or the driving direction of the intelligent driving equipment at an intersection in the first range indicated by SD map navigation information. Further, the navigation cost of each lane in the sub-road is determined according to the information of the position of each lane in the sub-road obtained by the perception system of the intelligent driving equipment, in combination with the passable degree of each part of the sub-road, and then the intelligent driving equipment is controlled to drive in a lane according to the navigation cost of each lane.

[0061] The technical solutions in the present application will be described below with reference to the drawings.

[0062] FIG. 1 is a functional block diagram of an intelligent driving device according to an embodiment of the present application. As shown in FIG. 1, the intelligent driving device 100 can include a perception system 120, a display device 130, and a computing platform 150. The perception system 120 can include a plurality of sensors for sensing information of an environment around the intelligent driving device 100. For example, the perception system 120 can include a positioning system, which can be a global positioning system (GPS), a Beidou system, or another positioning system. For another example, the perception system 120 can further include one or more of an inertial measurement unit (IMU), a laser radar, a millimeter wave radar, an ultrasonic radar, and a camera.

[0063] The display device 130 mainly includes two types, a first type being a vehicle-mounted display screen, and a second type being a projection display screen, such as a head up display (HUD). The vehicle-mounted display screen is a physical display screen and is an important component of a vehicle information entertainment system. The vehicle-mounted display screen can include a human machine interface (HMI). The head up display, also referred to as a head up display system, is mainly used to display driving information such as a speed, navigation, and the like on a display device (such as a windshield) in front of a user, so as to reduce a user's sight transfer time, avoid pupil changes caused by the user's sight transfer, and improve driving safety and comfort.

[0064] Some or all functions of the intelligent driving device 100 can be controlled by the computing platform 150. The computing platform 150 can include processors 151-15n, which are circuits having a processing capability for signals. In one implementation, the processor can be a circuit having an instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a kind of microprocessor), or a digital signal processor (DSP), etc. In another implementation, the processor can implement certain functions through a logical relationship of hardware circuits, which is fixed or can be reconfigured, such as an application-specific integrated circuit (ASIC) or a programmable logic device (PLD) implemented hardware circuit, such as a field programmable gate array (FPGA). In the reconfigurable hardware circuit, the processor loads a configuration document to implement the hardware circuit configuration, which can be understood as the process of the processor loading instructions to implement the functions of the above part or all units. In addition, the processor can also be a hardware circuit designed for artificial intelligence, which can be understood as a kind of ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc. In addition, the computing platform 150 can also include a memory for storing instructions, and some or all of the processors 151-15n can call the instructions in the memory to implement corresponding functions.

[0065] The intelligent driving device 100 can include an ADAS, which uses various sensors (including but not limited to laser radar, millimeter wave radar, camera, ultrasonic sensor, global positioning system, inertial measurement unit) on the intelligent driving device to obtain information from the surroundings of the intelligent driving device, and analyzes and processes the obtained information to realize functions such as obstacle perception, target recognition, intelligent driving device positioning, path planning, driver monitoring / reminding, etc., thereby improving the safety, automation level and comfort of the intelligent driving device driving.

[0066] From the logical function, the ADAS system generally includes three main functional modules: a perception module, a decision module and an execution module. The perception module perceives the environment around the vehicle body through sensors, inputs corresponding real-time data to the decision layer processing center, and the perception module mainly includes vehicle-mounted cameras, ultrasonic radars, millimeter wave radars, laser radars, etc. The decision module makes corresponding decisions using computing devices and algorithms based on the information obtained by the perception module. The execution module takes corresponding actions such as driving, lane changing, steering, braking, warning, etc. after receiving the decision signal from the decision module.

[0067] At different automatic driving levels (L0-L5), ADAS can realize different levels of automatic driving assistance based on artificial intelligence algorithms and information obtained by multiple sensors. The above-mentioned automatic driving levels (L0-L5) are based on the classification standard of the Society of Automotive Engineers (SAE). Among them, L0 level is non-automation; L1 level is driving assistance; L2 level is partial automation; L3 level is conditional automation; L4 level is high automation; L5 level is complete automation. The tasks of monitoring the road conditions and making responses at L1 to L3 levels are completed by the driver and the system together, and the driver needs to take over the dynamic driving task. L4 and L5 levels can make the driver completely change to the role of a passenger. At present, the functions that ADAS can realize mainly include but are not limited to: adaptive cruise control, automatic emergency braking, automatic parking, blind spot monitoring, front intersection traffic warning / braking, rear intersection traffic warning / braking, front vehicle collision warning, lane departure warning, lane keeping assistance, rear vehicle collision warning, traffic sign recognition, traffic congestion assistance, highway assistance, etc. It should be understood that the above-mentioned various functions can have specific modes at different automatic driving levels (L0-L5), and the higher the automatic driving level, the more intelligent the corresponding mode.

[0068] In the embodiment of the present application, the computing platform 150 can generate traction information according to the SD map and the intelligent driving device surrounding environment information obtained by the perception system 120, or the computing platform 150 can also control the display device 130 to display the traction information.

[0069] FIG. 2 shows a schematic diagram of an autonomous driving system architecture according to an embodiment of the present application. As shown in FIG. 2, the system includes a perception module 210, a map information obtaining module 220, a lane navigation information generating module 230, and a control module 240. The perception module 210 can include one or more cameras of the perception system 120 shown in FIG. 1, or can further include one or more radars of the perception system 120; the map information obtaining module 220, the lane navigation information generating module 230, and the control module 240 can each include one or more processors of the computing platform 150 shown in FIG. 1. In some implementations, the lane navigation information generating module 230 can also include one or more processors of a cloud server in communication with the intelligent driving device. The functions of the modules in the system shown in FIG. 2 are described below as items (I) to (IV).

[0070] (I) The perception module 210 is configured to obtain environmental information around the intelligent driving device and send the environmental information to the lane navigation information generating module 230. The environmental information includes road information, such as road boundaries, lane lines, and lane types of the road on which the intelligent driving device is currently driving. For example, the perception module 210 can also include one or more processors to process the collected perception information to obtain the environmental information. For example, for an image as the perception information, the one or more processors in the perception module 210 can extract the environmental information such as road boundaries, lane lines, and lane types (e.g., a tidal lane, a bus lane, a variable lane, etc.) from the obtained image.

[0071] (II) The map information obtaining module 220 is configured to determine SD map navigation information between a current position and a target position of the intelligent driving device based on an SD map, and send the SD map navigation information to the lane navigation information generating module 230. For example, as shown in FIG. 3, the SD map navigation information can indicate at least one of the following 1) to 4):

[0072] 1) The distance and driving direction of each of at least one intersection within a preset range of the intelligent driving device, such as straight, left turn, right turn, merging into a main road, and entering a ramp. The preset range can be 1 kilometer, 2 kilometers, or other distances in front of the intelligent driving device. More specifically, the preset range can be determined according to the driving scenario. As the maximum speed limit of the road increases, the preset range can increase. For example, if the intelligent driving device is driving on a highway, the preset range can be 2 kilometers. If the intelligent driving device is driving on an urban road, the preset range can be 1 kilometer. For example, the SD map navigation information shown in FIG. 3 indicates that the driving direction at the intersection is right turn.

[0073] 2) driving bias in the road, such as driving close to the left side of the road, driving close to the right side of the road, driving along the middle lane, etc. For example, in the SD map navigation information shown in FIG. 3, according to the high-lighting of the right-turn arrow in the lane-related information, it is determined that the driving bias indicated by the SD map navigation information is right-side driving.

[0074] 3) lane information of at least one sub-road in a preset range, wherein the at least one sub-road includes a road between the intelligent driving device and the nearest intersection, a road between two adjacent intersections, the lane information can include the number of lanes in the sub-road, and can also include guidance information of each lane, such as straight driving only, right-turn only, straight driving and left-turn, etc. at the intersection.

[0075] 4) The SD map navigation information can also indicate intersection topology information, or intersection type, of at least one intersection. The intersection type can include, but is not limited to, a cross intersection, a ramp-in connecting intersection, a ramp-out connecting intersection, a driving-into auxiliary road intersection, and a merging-into main road intersection. The cross intersection can be an n-branch intersection, where n is greater than or equal to 3, for example, the cross intersection can also be refined into a crossroad intersection, a T-shaped intersection, etc. For example, the SD map navigation information shown in FIG. 3 indicates that the intersection type is a cross intersection. That is, the number of lanes of the two connected sub-roads can be different, or the number of lanes of the two connected sub-roads can be the same.

[0076] It should be understood that the information shown in FIG. 3 is only for understanding the content of the SD map navigation information, and in actual implementation, the SD map navigation information is not information in graphical form.

[0077] (Three) Lane navigation information generation module 230: including SD navigation information preprocessing module 231, lane passing probability determination module 232, perception result fusion module 233, and human-like cost calculation module 234. The functions of each module in the lane navigation information generation module 230 are described in detail as follows:

[0078] The SD navigation information preprocessing module 231 is configured to preprocess the SD map navigation information, and determine other information according to the existing information in the SD map navigation information. For example 1, when the SD map navigation information does not indicate the driving direction at the intersection, the driving direction at the intersection is determined according to other contents indicated by the SD map navigation information, for example, when the SD map navigation information only indicates the lane information and the driving bias, the driving direction of the intelligent driving device at the intersection is determined according to the lane information and the driving bias, more specifically: 1) when the lane information indicates that the current driving road includes three lanes, the leftmost lane only supports left turn, and the driving bias is to drive close to the left side of the road, it can be determined that the driving direction of the intelligent driving device at the intersection is left turn; 2) when the lane information indicates that the current driving road includes four lanes, and the driving bias is to drive along the middle lane, it can be determined that the driving direction of the intelligent driving device at the intersection is straight. For another example, when the SD map navigation information only indicates the intersection type and the driving bias, the driving direction of the intelligent driving device at the intersection is determined according to the intersection type and the driving bias, more specifically: 1) when the intersection type indicates that the front intersection is an on-ramp connection intersection (the main road is located on the left side of the current driving road), and the driving bias is to drive close to the left side of the road, it can be determined that the driving direction of the intelligent driving device at the intersection is to merge into the main road; 2) when the intersection type indicates that the front intersection is a crossroad, and the driving bias is to drive close to the left side of the road, it can be determined that the driving direction of the intelligent driving device at the intersection is left turn. For example 2, when the SD map navigation information does not indicate the guidance of the target lane, the guidance of the target lane is determined according to other contents indicated by the SD map navigation information, for example, when the SD map navigation information only indicates the driving direction at the intersection and the number of lanes, the guidance information of part of the lanes can be determined according to the driving direction at the intersection and the number of lanes, more specifically: 1) when the driving direction at the intersection is right turn, and the number of lanes is 3, then it can be determined that the rightmost lane (i.e. the target lane) of the three lanes supports right turn; 2) when the driving direction at the intersection is left turn, and the number of lanes is 2, then it can be determined that the leftmost lane (i.e. the target lane) of the three lanes supports left turn.

[0079] The lane pass probability determination module 232 is configured to determine the passable degree (or pass probability) of each of the plurality of portions of the sub-road according to the SD map navigation information. The plurality of portions of the sub-road are obtained by dividing the width direction of the sub-road, for example, the sub-road is equally divided along the width direction of the sub-road into M parts, and each part includes N divisions, where M can be 60, N can be 10, or M and N can also take other integer values. For example, the passable degree of each portion of the sub-road can be determined according to the driving direction at the intersection, or the passable degree of each lane can also be determined according to the driving direction at the intersection and the lane information (including the number of lanes, the guidance information of each lane). In some implementations, when the preset range of the intelligent driving device includes a plurality of intersections, i.e., a plurality of sub-roads in the preset range, the lane pass probability determination module 232 can also superimpose the passable degree information corresponding to each of the sub-roads to determine the passable degree of each portion of the sub-road closest to the intelligent driving device.

[0080] The perception result fusion module 233 can fuse the environment information perceived by the perception module 210 and the passable degree of each part of the sub-road in the vicinity of the intelligent driving device. More specifically, the perception result fusion module 233 can determine the projection plane lane of the road where the intelligent driving device is currently located and determine the position of the intelligent driving device in the projection plane lane according to the environment information perceived by the perception module 210. The projection plane lane refers to an ordered lateral lane sequence filtered within a preset distance (such as 200 meters, or 250 meters, or other numerical values) in the driving direction of the intelligent driving device according to the perceived environment information (such as images collected by the camera including lane topology). It should be noted that the "lateral" in the ordered lateral lane sequence refers to the direction perpendicular to the driving direction of the intelligent driving device, and the ordered lateral lane sequence can be the lane sequence from left to right of the intelligent driving device, or can also be the lane sequence from right to left of the intelligent driving device. Further, the navigation cost of each lane in the projection plane lane is determined according to the passable degree of each part of the sub-road in the vicinity of the intelligent driving device. The navigation cost of a lane indicates the difficulty of realizing the driving direction indicated by the SD map navigation information at the front intersection when driving along the lane to the intersection, and the greater the navigation cost, the greater the difficulty. For example, the SD map navigation information indicates that the intelligent driving device needs to turn right at the front intersection, and only the rightmost lane supports right turn. Obviously, when the intelligent driving device drives along the leftmost lane to the front intersection, it needs to gradually change lanes to the rightmost lane, otherwise it cannot complete the right turn at the front intersection, so in the above scenario, the navigation cost of the leftmost lane is greater than that of the rightmost lane. In some implementations, the perception result fusion module 233 can also traverse each lane in the initially identified projection plane lane and search a certain distance (such as 100 meters, or 150 meters, or other distances) in the direction of the target position. If the lane splits, replace the original lane with the split lane; if the lane merges, replace the original lane with the merged lane. Perform the above search until the intersection is reached or the search threshold is reached to determine the final projection plane lane. In yet some implementations, the perception result fusion module 233 can also delete invalid lanes in the initially identified projection plane lane to obtain the final projection plane lane, where the invalid lanes can be, for example, emergency lanes, escape lanes, non-motor vehicle lanes, etc. Further, the perception result fusion module 233 can send the lane navigation information including the navigation cost of each lane in the projection plane lane to the control module 240. Alternatively, the perception result fusion module 233 can send the navigation cost of each lane in the projection plane lane to the humanoid cost calculation module 234.

[0081] The human-like cost calculation module 234 can adjust the navigation cost of each lane according to human driving habits to obtain a final navigation cost. Example one, when there is a special lane (such as a tidal lane, a bus lane, a variable lane, etc.) in the projected planar lane, the navigation cost of the special lane is increased so that the intelligent driving device plans to travel in the lane. Example two, when there is a lane splitting or lane merging in the projected planar lane, the navigation cost of the auxiliary lane can be increased to control the intelligent driving device to travel in the high road right lane. Example three, when the intelligent driving device needs to go straight through the intersection, and the straight lane includes multiple lanes, compared with the lane that enters and exits the intersection more smoothly, the navigation cost of other straight lanes is increased to improve the smoothness of the intelligent driving device when going straight through the intersection. Example four, when the intelligent driving device turns at an intersection, the intelligent driving device is preferentially controlled to turn on the inner lane, for example, when the intelligent driving device turns right at an intersection, the navigation cost of the lanes other than the rightmost lane in the target road (i.e., the road after turning) is increased; when the intelligent driving device turns left or U-turns at an intersection, the navigation cost of the lanes other than the leftmost lane in the target road is increased.

[0082] (Four) regulation and control module 240: used to calculate a control amount for controlling the intelligent driving device to travel along the lane navigation information according to the lane navigation information, and output the control amount to an actuator. When the actuator executes the control amount, the intelligent driving device is controlled to travel along the planned path. In some possible implementation manners, the actuator can include a steering and braking control system in the intelligent driving device 100.

[0083] It should be understood that the above modules are only an example, and in actual applications, the above modules can be added or deleted according to actual needs. For example, in the system architecture shown in FIG. 2, the map information acquisition module 220 and the lane navigation information generation module 230 can be combined into one module; or the lane navigation information generation module 230 and the regulation and control module 240 can be combined into one module. For another example, the lane navigation information generation module can not include the human-like cost calculation module 234. For another example, the processor in the perception module 210 can be a separate processing module, and the processing module can be arranged in the intelligent driving device, or can also be arranged in a cloud server in communication with the intelligent driving device.

[0084] The above introduces the automatic driving system architecture provided by the embodiments of the present application, and the following details the flow of the automatic driving method provided by the embodiments of the present application based on the automatic driving system shown in FIG. 2.

[0085] The intelligent driving device can include a road vehicle, a water vehicle, an air vehicle, an industrial device, an agricultural device, or an entertainment device, etc. For example, the intelligent driving device can be a vehicle, which is a general concept of a vehicle, and can be a vehicle (such as a commercial vehicle, a passenger vehicle, a motorcycle, a flying vehicle, a train, etc.), an industrial vehicle (such as a forklift, a trailer, a tractor, etc.), an engineering vehicle (such as an excavator, a bulldozer, a crane, etc.), an agricultural device (such as a mower, a harvester, etc.), an amusement device, a toy vehicle, etc. The type of the vehicle is not limited in the embodiments of the present application. For ease of understanding, the intelligent driving device is taken as a vehicle as an example for description below.

[0086] FIG. 4 shows a schematic flowchart of an automatic driving method according to an embodiment of the present application. The method 400 can be applied to the intelligent driving device shown in FIG. 1, or the method can be executed by the system shown in FIG. 2. More specifically, the method 400 can be executed by the lane pass probability determination module 232, and the method 400 can include the following steps.

[0087] S401, obtaining SD map navigation information, the SD map navigation information at least indicating a driving bias in a first range of the vehicle and / or a driving direction at an intersection.

[0088] For example, the SD map navigation information can include the preprocessed SD map navigation information output by the SD map navigation information preprocessing module 231, and the first range can be the preset range in the above embodiments, for example, a range of 1 kilometer, 1.5 kilometers, or 2 kilometers in front of the vehicle in the driving direction of the vehicle.

[0089] It should be noted that the first range includes part of the road that the vehicle passes through during driving from the current position to the target position. In some implementations, the first range of the vehicle can include multiple intersections, that is, the vehicle passes through the multiple intersections in turn during driving to the target position, and the SD map navigation information can indicate lane information of each sub-road in the first range, such as the number of lanes, the guidance of each lane at the intersection, etc., in addition to indicating the driving direction at each intersection in the first range of the vehicle. Wherein, the sub-road is the road between two intersections, or the road between the vehicle and the nearest intersection in the first range. More specifically, the type of each intersection can include but is not limited to an intersection, a ramp-on intersection, a ramp-off intersection, a secondary road entry intersection, and a main road merging intersection.

[0090] S402, determining a passable degree of each of a plurality of parts of a road in a second range of the vehicle according to the SD map navigation information, wherein the plurality of parts are obtained by dividing the road in a direction perpendicular to the driving direction, and the second range is smaller than or equal to the first range.

[0091] Exemplarily, the second range can be a range located in front of the vehicle in the driving direction and away from the vehicle by 200 meters, or 250 meters, or other distances. For example, the second range can be a range between the current position of the vehicle and the nearest neighboring intersection in the first range, and the plurality of portions of the road in the second range of the vehicle are a plurality of portions of the sub-road between the current position of the vehicle and the nearest neighboring intersection in the first range. The second range can be the same distance as the preset distance in the foregoing embodiments.

[0092] In some implementations, the sub-road can be equally divided into M parts along the width direction of the sub-road, for example, 60 parts. The M parts can be further divided into six parts, each including 10 parts, for example, as shown in (a) and (b) of FIG. 5; or the M parts can be further divided into ten parts, each including 6 parts, for example, as shown in (c) of FIG. 5. In combination with FIG. 5, according to the driving bias of the vehicle in the first range and / or the driving direction at the intersection, determining the passable degree of each of the plurality of portions of the road in the second range of the vehicle can include any one of the following:

[0093] 1) When the SD map navigation information indicates that the driving bias of the vehicle is to drive along the main road in the first side (such as the left side or the right side) of the sub-road 1, it can be determined that the passable degree of 1 / 2 of the sub-road 1 close to the first side is 100%, and the passable degree of the remaining 1 / 2 is 0. The portion with a passable degree of 100% can be regarded as a passable portion. For example, as shown in (a) of FIG. 5, when the driving bias is to drive along the main road in the left side of the sub-road 1, it can be determined that the shaded portion of 1 / 2 of the sub-road 1 close to the left side is a passable portion. In actual implementation, when the SD map navigation information indicates that the vehicle drives along the main road in the first side of the sub-road 1, the passable portion in the sub-road 1 can also be marked as other equal parts or proportions, for example, it can be determined that the passable degree of 2 / 3 of the sub-road 1 close to the first side is 100%, and the passable degree of the remaining 1 / 3 is 0, that is, 2 / 3 close to the first side is a passable portion.

[0094] 2) When the SD map navigation information indicates that the vehicle is to exit the main road in the first side (e.g. left or right) of the sub-road 1, it can be determined that the passable degree of the 1 / 6 of the sub-road 1 near the first side is 100%, and the passable degree of the remaining 5 / 6 is 0. For example, as shown in (b) of FIG. 5, when the driving direction is to drive along the main road in the right side of the sub-road 1, it can be determined that the 1 / 6 of the sub-road 1 near the right side is the passable part. In actual implementation, when the SD map navigation information indicates that the vehicle is to exit the main road in the first side of the sub-road 1, the passable part of the sub-road 1 can also be marked as other equal divisions or proportions, for example, it can be determined that the passable degree of the 1 / 3 of the sub-road 1 near the first side is 100%, and the passable degree of the remaining 2 / 3 is 0, i.e. the 1 / 3 near the first side is the passable part.

[0095] 3) When the SD map navigation information indicates to go straight at the intersection 1 in front of the sub-road 1, it can be determined that the passable degree of the 4 / 5 of the middle of the sub-road 1 is 100%, and the passable degree of the 1 / 10 of the left and right sides is 0. For example, as shown in (c) of FIG. 5, the 4 / 5 of the middle of the sub-road 1 is the passable part. In actual implementation, when the SD map navigation information indicates that the vehicle is to go straight at the intersection 1 in front of the sub-road 1, the passable part of the sub-road 1 can also be marked as other equal divisions or proportions, for example, it can be determined that the passable degree of the 1 / 3 of the middle of the sub-road 1 is 100%, and the passable degree of the 1 / 1 of the left and right sides is 0, i.e. the 1 / 3 of the middle of the sub-road 1 is the passable part.

[0096] It should be noted that the above-mentioned sub-road 1 can be one of the multiple sub-roads in the first range, and the intersection 1 can be the nearest neighbor intersection in front of the sub-road 1.

[0097] In some implementations, the SD map navigation information further indicates lane information, i.e., the SD map navigation information further indicates the number of lanes included in the sub-road and the guidance of each lane. Then, each lane can be divided into multiple parts according to the guidance of each lane to obtain multiple parts of the sub-road. For example, as shown in FIG. 6, a sub-road includes lanes 1 to 3, and lane 1 supports straight and left turn at the nearest adjacent intersection in front, lane 2 only supports straight at the nearest adjacent intersection in front, and lane 3 supports straight, right turn and U-turn at the nearest adjacent intersection in front. For example, lane 1 can be divided into two equal parts, each occupying 1 / 6 of the entire sub-road width, for vehicle left turn and vehicle straight respectively; since lane 2 only supports straight, lane 2 is not further divided, i.e., lane 2 occupies 1 / 3 of the entire sub-road width; lane 3 can be divided into three equal parts, each occupying 1 / 9 of the entire sub-road width, for vehicle straight, right turn and U-turn respectively; since lane 2 only supports straight, lane 2 is not further divided, i.e., lane 2 occupies 1 / 3 of the entire sub-road width. Further, when the vehicle needs to go straight at the intersection in front of the sub-road, the passable part of the sub-road includes 50% of lane 1, 100% of lane 2, and 33.3% of lane 3; when the vehicle needs to turn left at the intersection in front of the sub-road, the passable part of the sub-road includes 50% of lane 1; when the vehicle needs to turn right or U-turn at the intersection in front of the sub-road, the passable part of the sub-road includes 33.3% or 66.7% of lane 3.

[0098] It should be noted that the above-mentioned sub-road division method is only an example, and in actual implementation, the sub-road can also be divided into other forms, for example, the sub-road can be divided according to the actual width of the sub-road and / or the actual width of each lane.

[0099] FIG. 7 shows another schematic flowchart of an automatic driving method according to an embodiment of the present application. The method 500 can be applied to the intelligent driving device shown in FIG. 1, or the method can be executed by the system shown in FIG. 2. More specifically, the method 500 can be executed by the perception result fusion module 233 and the control module 240, and the method 500 can include:

[0100] S501, determining multiple lane information of a road in a second range of the vehicle according to environment information obtained by a perception system.

[0101] For example, the multiple lane information includes the number and position of lanes. The multiple lane information can be the projected plane lane in the above-mentioned embodiments.

[0102] In some implementations, each of the defined multiple lanes can be traversed, and a certain distance (e.g., 100 meters, 150 meters, or other distances) can be searched towards the target location. If a lane splits, the split lane replaces the original lane; if lanes merge, the merged lane replaces the original lane. This search is performed until an intersection is encountered or a search threshold is reached, thus determining the final projected lane plan.

[0103] In some other implementations, invalid lanes in the initially identified projection plane lanes can be deleted to obtain the final projection plane lanes. Invalid lanes can be, for example, emergency lanes, escape lanes, non-motorized vehicle lanes, etc.

[0104] In some implementations, the positions of multiple lanes on the road the vehicle is currently traveling on, as well as the position of the lane the vehicle is in, are determined based on environmental information obtained by the perception system.

[0105] For example, the position of a vehicle in a lane can be determined by either of the following two methods:

[0106] Firstly, road boundaries can be determined based on environmental information, and then the number N of lanes on the left side of the vehicle can be determined based on the road boundaries. left And the number of lanes N on the right. right And then according to N left and N right Determine the vehicle's lane position, where N left and N right All values ​​are integers greater than or equal to 0. It is understood that a sub-road may include two road boundaries, and the area within these two road boundaries is the drivable area for the intelligent driving device. For example, these road boundaries may include, but are not limited to, road shoulders, double yellow lines, and central medians.

[0107] Secondly, it can also be based on the number of lanes N to the left of the vehicle. left The number of lanes on the right, N right And the total number of lanes N indicated by the SD map navigation information. sd Determine the vehicle's current lane position. For example, determine the total number N of lanes on the current travel route based on environmental information. total =N left +N right +1. Furthermore, since the number of lanes N recorded in the SD map... sd There may be an error, or the sensors of the intelligent driving device may be misreading the number of lanes, leading to N. total With N sd If they are not equal, then it can be determined according to N. total With N sd Based on the comparison results, determine the lane the vehicle is in: ① In Ntotal ≤N sd If the distance between the left lane line of the leftmost lane and the left road boundary is less than or equal to the preset distance threshold, the lane in which the vehicle is located is the N left +1thlane from the left; if the distance between the right lane line of the rightmost lane and the right road boundary is less than or equal to the preset distance threshold, the lane in which the vehicle is located is the N sd -N right thlane from the left. ② When N total >N sd , if lane splitting occurs in front of the road and the vehicle needs to pass through the right lane after splitting, the lane in which the vehicle is located is the N total -N right thlane from the left; if lane splitting occurs in front of the road and the vehicle needs to pass through the left lane after splitting, the lane in which the vehicle is located is the N left +1thlane from the left; if lane merging occurs in front of the road and the vehicle enters the merged lane from the right lane, the lane in which the vehicle is located is the N sd -N right thlane from the left; if lane merging occurs in front of the road and the vehicle enters the merged lane from the left lane, the lane in which the vehicle is located is the N left +1thlane from the left; in other cases, the lane in which the vehicle is located is considered to be the N left +1thlane from the left.

[0108] S502, determine the navigation cost of the plurality of lanes according to the information of the plurality of lanes and the passable degree of each of the plurality of sections.

[0109] In some implementations, the information of the plurality of lanes can include lane types, such as information indicating that a lane includes a bus lane, a tidal lane, a variable lane, etc., which can affect the normal driving of the vehicle. Then, the navigation cost of the plurality of lanes can be determined according to the position of each of the plurality of lanes, the passable degree of each of the plurality of sections, and the lane type.

[0110] S503, control the vehicle to drive in one of the plurality of lanes according to the navigation cost.

[0111] In some implementations, the vehicle is controlled to drive in the lane with the minimum navigation cost. For example, the vehicle is controlled to keep driving in the lane with the minimum navigation cost, or the vehicle is controlled to change to drive in the lane with the minimum navigation cost.

[0112] In order to better understand the method 400 and the method 500, the specific implementation of determining the passable degree of each of the plurality of sections in the sub-road and the navigation cost of the plurality of lanes in the sub-road is described in detail below in combination with FIG. 8 and FIG. 9.

[0113] FIG. 8 shows a schematic diagram of an application scenario of an embodiment of the present application. As shown in (a) of FIG. 8, the SD map navigation information indicates to drive on the right in a sub-road A between a current position of the ego vehicle and a front intersection, the front intersection is xx meters away from the ego vehicle (xx is a value less than 1000 or 2000), the sub-road A includes three lanes, and the three lanes all support straight driving. Then, the passable degree of the sub-road A can be determined according to the SD map navigation information, for example, the width direction of the sub-road A is equally divided into 60 parts (or each lane is equally divided into 20 parts), according to the indication of the SD map navigation information, the rightmost lane is a passable lane, and the following passable information (or passable probability 1) of the sub-road A can be obtained: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]. Each item represents the passable probability of an equal part of the sub-road A, the passable probability of 0.0 represents impassable, the passable probability of 1.0 represents passable, and the left to right represents the equal parts of the left side to the right side of the sub-road A.

[0114] In some implementations, the passable probability can be smoothed, for example, using the following formula (1) to smooth the passable probability:

[0115] wherein σ 2 is a variance determined according to the distance between each intersection and the current position of the ego vehicle, the greater the distance, the smaller the variance. For example, the specific value of the variance σ 2 in formula (1) can be determined according to the following formula (2).

[0116] Where, dis represents the distance between two navigation events indicated by the SD map navigation information (which can be understood as two intersections involved in the SD map navigation information), or dis represents the distance between the navigation event indicated by the SD map navigation information and the vehicle (or the distance between the vehicle and the nearest intersection ahead). σ0 is the baseline standard deviation, which can be 1.76, or other calibrated values; dis1 and dis2 are distance thresholds, which can be 0.5 km and 1 km respectively, or other calibrated values. For example, when driving on urban roads, dis1 and dis2 take the above values, and when driving on highways, dis1 and dis2 take 1 km and 2 km respectively; k, μ1, and μ2 are coefficients, which can be -2, 0.88, and -0.5 respectively, or other calibrated values.

[0117] For example, if the distance between the intersection ahead of sub-road A and the vehicle is 101 meters (i.e., 0.101 kilometers), the variance σ in formula (1) can be determined according to formula (2). 2 It is 2.5. Furthermore, inputting the above passage probability 1 into formula (1) yields the smoothed passage probability: [0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.05 0.11 0.19 0.30 0.43 1.14 1.14 1.14 1.14 1.14 ...

[0118] Furthermore, if the lane topology of sub-road A perceived by the vehicle is as shown in Figure 8(b), including lanes ① to ③, then the smoothed passage probability is scaled to lanes ① to ③, resulting in passage probabilities of 0.0, 0.07, and 1.0 for lanes ① to ③, respectively. Then, by mapping the passage probabilities of each lane using a pre-defined function (such as formula (3)), the navigation costs for lanes ① to ③ are obtained as 4000, 2000, and 0, respectively. Furthermore, if the vehicle is currently in lane ③, it is controlled to continue driving in lane ③; if the vehicle is not currently in lane ③, it is controlled to change to lane ③.

[0119] Wherein, p is the actual passing probability, p0 and p1 are the passing probability thresholds, which can be 0.05 and 0.9 respectively, or other calibrated values. cost0 and cost1 represent the navigation cost, which can be 4000 and 0 respectively, or other calibrated values. a and b are coefficients, which can be 100 and 2000 respectively, or other calibrated values.

[0120] Optionally, if it is determined according to the environment information perceived by the vehicle that the lane ③ is combined into the lane ⑥ after being split into the lane ④ and the lane ⑤, the lane ④ and the lane ⑤ can be regarded as the leftmost lanes of the sub-road A (i.e. corresponding to the leftmost 20 equal parts), that is, the passing probabilities of the lanes ①, ②, ③-④-⑥, ③-⑤-⑥ are 0.0, 0.07, 1.0, 1.0 respectively; the initial navigation costs determined according to the formula (3) are 4000, 2000, 0, 0 respectively. Further, the initial navigation costs are corrected according to the road rights of the lanes. Since the lane ⑤ is a lane split auxiliary lane, a split auxiliary lane cost of 200 is added; the lane ⑥ is a lane combined auxiliary lane, a combined auxiliary lane cost of 2000 is added, so the final navigation costs of the lanes ①, ②, ③-④-⑥, ③-⑤-⑥ are 4000, 2000, 0, 2200 respectively. Further, if the vehicle is currently in the lane ③-④-⑥, the vehicle is controlled to continue driving in the lane ③-④-⑥; if the vehicle is not currently in the lane ③-④-⑥, the vehicle is controlled to change to drive in the lane ③-④-⑥.

[0121] Fig. 9 shows a schematic diagram of another application scenario of the embodiments of the present application. As shown in (a) of Fig. 9, the SD map navigation information includes navigation information of two sub-roads, namely SD map navigation information 1 and SD map navigation information 2. As shown in (b) of Fig. 9, the SD map navigation information 2 indicates to drive along the main road on the left side in the sub-road b between the current position of the ego vehicle and the front intersection 2, the intersection 2 is yy meters away from the ego vehicle, and the SD map navigation information 2 does not include lane information; the SD map navigation information 1 indicates to exit the main road (or the off-ramp) at the intersection 1, i.e., to drive on the right side in the sub-road a between the intersection 2 and the intersection 1, the intersection 1 is zz meters away from the ego vehicle, the sub-road a includes three lanes, and the rightmost lane in the three lanes is straight and exits the main road at the intersection 1. Then, the passable degree of the sub-road a and the sub-road b can be determined according to the SD map navigation information, for example, the sub-road a and the sub-road b are equally divided in the width direction (or each lane is equally divided into 20 parts), according to the indication of the SD map navigation information 2, driving along the main road on the left side in the sub-road b, i.e., the left half (or 30 parts) of the sub-road b is the passable part, then the following passable information (or the passing probability b) of the sub-road b can be obtained: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], which represent the equally divided parts from left to right of the sub-road b. According to the indication of the SD map navigation information 1, driving on the right lane in the sub-road a to exit the main road, i.e., the right 1 / 6 (or 10 parts) of the sub-road a is the passable part, then the following passable information (or the passing probability a) of the sub-road a can be obtained: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], which represent the equally divided parts from left to right of the sub-road a.

[0122] In some implementations, in order to improve the continuity of lane navigation and the ability to respond to over-the-horizon navigation events, the passable results of the passable probability b and the passable probability a can be superimposed, and the passable result of the navigation event a (such as the navigation event indicated by the SD map navigation information 1) far away from the ego vehicle is nested into the passable interval of the navigation event b (such as the navigation event indicated by the SD map navigation information 2) close to the ego vehicle. The superposition operation can obtain the superposition of the two navigation events. For example, taking the distance between the navigation event a and the navigation event b as 1159 meters as an example, the variance σ 2may be 0.58, the smoothed passing probability a can be obtained by inputting the passing probability a into the above formula (1): [0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.04 0.04 0.04 0.04 0.05 0.05 0.05 0.06 0.06 0.06 0.07 0.07 0.07 0.08 0.08 0.09 0.09 0.09 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.13 0.13 0.13 0.14 0.14 0.14 0.15 0.15 0.15 0.15 0.16 0.16 0.16 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18], and since the navigation event b is driving along the main road on the left side, it can be determined that the vehicle passing interval corresponding to the navigation event b is 1 / 2 of the left side of the sub-road b, and thus after scaling the smoothed passing probability a to the effective interval of the navigation event b (i.e., 1 / 2 of the left side of the sub-road b), the scaled passing probability a can be: [0.01 0.02 0.02 0.02 0.03 0.03 0.04 0.04 0.05 0.06 0.06 0.07 0.08 0.08 0.09 0.10 0.11 0.12 0.12 0.13 0.14 0.15 0.15 0.16 0.16 0.18 0.18 0.18 0.18 0.18], that is, the scaled passing probability a is the passing probability corresponding to 1 / 2 of the left side of the sub-road b.After superposition processing and normalization processing are performed on the scaled passing probability a and the passing probability b, a passing probability c obtained by the navigation event a acting on the navigation event b is obtained: [0.08 0.08 0.10 0.11 0.14 0.16 0.20 0.22 0.27 0.32 0.36 0.38 0.42 0.45 0.49 0.55 0.58 0.65 0.71 0.75 0.77 0.82 0.85 0.86 0.88 0.96 1.00 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00], where the superposition processing performed on the scaled passing probability a and the passing probability b can be understood as: multiplying the 30 elements in the scaled passing probability a with the first 30 elements in the passing probability b one by one. It should be understood that the passing probability c indicates the passing probability of each part in the sub-road b. Taking the distance between the navigation event b and the ego vehicle as 89 meters as an example, the variance σ in formula (1) is determined according to formula (2). 2 The final passable probability of the sub-road a can be obtained by inputting the above passing probability c into formula (1): [0.11 0.13 0.16 0.18 0.22 0.25 0.32 0.35 0.43 0.51 0.56 0.61 0.66 0.71 0.77 0.87 0.92 1.03 1.13 1.18 1.22 1.30 1.33 1.36 1.40 1.51 1.58 1.58 1.58 1.58 0.21 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00] when the variance σ can be 2.57.

[0123] FIG. 10 shows a comparison between superimposition and smoothing of the passing probability a and the passing probability b associated with FIG. 9. As shown in FIG. 10, the dotted straight line represents a passing probability of 0 (or impassable), and the solid line represents an actual passing probability (or passable degree). The passing probability corresponding to the intersection 1 indicates that the ego vehicle travels along the rightmost lane (i.e., lane ③) between the intersection 1 and the intersection 2, and the passing probability corresponding to the intersection 2 indicates that the ego vehicle travels along the left lane of the road between the intersection 2 and the current position of the ego vehicle, and the ego vehicle travels along the lane ③. If the passing probability a and the passing probability b are not superimposed, the ego vehicle will change from the lane ③ to the lane ① while traveling to the intersection 2, and gradually change to the lane ③ when approaching the intersection 1 (e.g., the first range of the intersection 1). However, after superimposing the passing probability a and the passing probability b, the passing probability corresponding to the intersection 2 indicates that the ego vehicle travels along the lane ② while traveling to the intersection 2, and changes to the rightmost lane (i.e., lane ③) after passing through the intersection 2. That is, when the passing probability is not superimposed, the ego vehicle needs to change lanes four times when traveling from the intersection 2 to the intersection 1, i.e., from lane ③ to lane ②, from lane ② to lane ①, from lane ① to lane ②, and from lane ② to lane ③; after superimposing the passing probability, the ego vehicle only needs to change lanes twice when traveling from the intersection 2 to the intersection 1, i.e., from lane ③ to lane ② and from lane ② to lane ③. Through the superposition of multiple navigation events, a global view during travel to the target position can be obtained, the number of invalid lane changes can be reduced, and the lane change intention can be recommended to the vehicle in time, so that the vehicle can complete the lane change in time and reduce the probability of missing the exit during high-speed driving.

[0124] FIG. 11 shows a schematic diagram of an application scenario of the automatic driving method provided by the embodiments of the present application. As shown in FIG. 11, the ego vehicle needs to travel from the sub-road 1 to the target position of the sub-road 2 via the intersection a, wherein the sub-road 1 includes the lane a, the lane b, and the lane c; and the sub-road 2 includes the lane d and the lane e. Referring to the above method 400 and method 500, when the ego vehicle travels from the sub-road 1 to the intersection a, the navigation cost of the lane a will be lower than that of the lane b and the lane c, and therefore the ego vehicle is controlled to travel along the lane a to the intersection a. In particular, since the lane c includes a tidal lane, an additional navigation cost needs to be superimposed on the lane c to increase the navigation cost of the lane c, so as to avoid the ego vehicle traveling on the lane c. In addition, since the ego vehicle needs to turn right from the sub-road 1 to the sub-road 2, the navigation cost of the lane d in the sub-road 2 can be reduced, so that the ego vehicle travels from the lane a to the lane d, i.e., turns inwards.

[0125] It should be noted that the schematic shown in FIG. 10 and the application scenario shown in FIG. 11 are only exemplary. In actual implementation, more navigation events can be superimposed; in addition, other special lanes can be included in the road, or lane splitting and / or lane merging can exist.

[0126] FIG. 12 shows another exemplary flowchart of an automatic driving method provided by an embodiment of the present application. The method 600 can be applied to the intelligent driving device shown in FIG. 1, or the method can be executed by the system shown in FIG. 2. More specifically, the method 600 can include S610 to S630.

[0127] S610, obtaining SD map navigation information, the SD map navigation information at least indicating a driving bias in at least one sub-road within a first range of the intelligent driving device and / or a driving direction at at least one intersection.

[0128] The first sub-road in the at least one sub-road is a sub-road between two adjacent intersections, or a sub-road between a current position of the intelligent driving device and a nearest adjacent intersection in front.

[0129] Exemplarily, the intelligent driving device can include a vehicle (such as a self-vehicle) in the foregoing embodiments; the SD map navigation information can be the same as the foregoing SD map navigation information; the first range can be the preset range in the foregoing embodiments, for example, a range within 1 kilometer, or 1.5 kilometers, or 2 kilometers in front of the intelligent driving device in the driving direction of the vehicle, which can be the same as the first range in the method 400.

[0130] S620, determining first traffic degree information according to the SD map navigation information, the first traffic degree information indicating a traffic degree of each of a plurality of parts in a width direction of the first sub-road.

[0131] Exemplarily, the first traffic degree information can include the traffic degree of each of the plurality of parts of the road in the method 400. In an example, the SD map navigation information further indicates a number of lanes in the first sub-road and a guide of each lane, and then a plurality of parts in the width direction of the first sub-road are determined according to the guide of each lane in the first sub-road. In another example, the SD map navigation information does not indicate the number of lanes in the first sub-road and the guide of each lane, and then the method further includes: dividing the first sub-road into a plurality of parts along the width direction. For a more specific method of determining the first traffic degree information, reference can be made to the description in the method 400, which will not be repeated here.

[0132] Exemplarily, the first sub-road can include the sub-road 1 in the foregoing embodiments.

[0133] S630, controlling the intelligent driving device to travel in the first sub-road according to the perception information and the first traffic degree information of the intelligent driving device.

[0134] The perception information indicates positions of the plurality of lanes in the first sub-road and a position of a lane in which the intelligent driving device is located. Illustratively, the perception information can include the plurality of lane information of the road in the method 500, and a method for determining the perception information can refer to the description in S501, which will not be repeated here.

[0135] In some implementations, S630 can be refined as: determining a navigation cost of each lane in the plurality of lanes according to the first traffic degree information, the navigation cost indicating a difficulty of traveling in a travel direction indicated by the SD map navigation information at a first intersection when traveling along the lane to the first intersection, the first intersection being an intersection between the first sub-road and a nearest neighbor sub-road in front of the first sub-road; and controlling the intelligent driving device to travel in a lane in the plurality of lanes whose navigation cost is less than or equal to a cost threshold.

[0136] Illustratively, the navigation cost can be determined as described in the method 500, for example, by formula (3), and the cost threshold can be 200, or 500, or other numerical values.

[0137] In some implementations, the first sub-road is a sub-road in which the intelligent driving device is currently located, and the at least one sub-road further includes a second sub-road, the second sub-road being located between the target position and the first sub-road, and the first traffic degree information is determined according to the SD map navigation information, including: determining second traffic degree information according to a travel bias in the second sub-road and / or a travel direction at a second intersection, the second traffic degree information indicating a traffic degree of each of a plurality of portions in a width direction of the second sub-road, the second intersection being an intersection between the second sub-road and a nearest neighbor sub-road in front of the second sub-road; determining third traffic degree information according to a travel bias in the first sub-road and / or a travel direction at a first intersection, the third traffic degree information indicating a second traffic degree of each of a plurality of portions in a width direction of the first sub-road; and performing a first processing on the second traffic degree information and the third traffic degree information to obtain the first traffic degree information.

[0138] In some implementations, determining the third traffic degree information according to the travel bias in the first sub-road and / or the travel direction at the first intersection includes: determining a trafficable interval of the first sub-road according to the travel bias in the first sub-road and / or the travel direction at the first intersection, and mapping the traffic probability of the second sub-road to the trafficable interval of the first sub-road to obtain the mapped third traffic degree information.

[0139] For example, the first sub-road is a sub-road between the vehicle and intersection 2 in FIG. 9, and the second sub-road is a sub-road between intersection 1 and intersection 2. The second traffic degree information can be the traffic probability a in the foregoing embodiment, the third traffic degree information can be the traffic probability b in the foregoing embodiment, the first traffic degree information can be the traffic probability c in the foregoing embodiment, or the first traffic degree information can be the final traffic probability after the traffic probability c is smoothed. The first processing can be superposition processing, and more specific processing procedures and results can be referred to the corresponding description of method 500 and FIG. 10, which will not be described here.

[0140] In some implementations, the perception information further indicates a lane type of each lane in the first sub-road; and the determining the navigation cost of each lane in the plurality of lanes according to the first traffic degree information comprises: determining the navigation cost of each lane in the plurality of lanes according to the first traffic degree information and the lane type. More specifically, the determining the navigation cost of each lane in the plurality of lanes according to the first traffic degree information and the lane type comprises: when the lane type of the first lane in the plurality of lanes is at least one of the following: a tidal lane, a variable lane, a bus lane, a stop-allowed lane, an escape lane, and an emergency lane, increasing the navigation cost of the first lane.

[0141] In some implementations, the determining the navigation cost of each lane in the plurality of lanes according to the first traffic degree information comprises: when the first sub-road is connected to a third sub-road, and the intelligent driving device needs to turn from the third sub-road to the first sub-road, decreasing the navigation cost of a second lane in the plurality of lanes, the second lane being a lane on a side corresponding to a turning direction in the plurality of lanes.

[0142] For example, when the vehicle turns left, the second lane can be the leftmost lane of the first sub-road; and when the vehicle turns right, the second lane can be the rightmost lane of the first sub-road.

[0143] In some implementations, the navigation cost of a third lane and a fourth lane in the plurality of lanes are both less than or equal to a cost threshold, and the controlling the intelligent driving device to drive in the lane in the plurality of lanes whose navigation cost is less than or equal to the cost threshold comprises: when the road right of the third lane is higher than the road right of the fourth lane, controlling the intelligent driving device to drive in the third lane.

[0144] In the third lane has higher road right than the fourth lane, the control intelligent driving device to drive in the third lane, can be further refined as: in the third lane has higher road right than the fourth lane, increase the navigation cost of the fourth lane, control the intelligent driving device to drive in the third lane with lower navigation cost. Exemplarily, the third lane and the fourth lane can be lane ③-④-⑥ and lane ③-⑤-⑥ in FIG. 9 respectively. Since the lane ③-⑤-⑥ is a lane formed after the auxiliary lane splitting and merging, the road right is lower than the lane ③-④-⑥ located in the main road, so the intelligent driving device is controlled to drive in the lane ③-④-⑥ with higher road right.

[0145] In some implementations, when the first sub-road is a highway, the intelligent driving device is controlled to drive in a fast lane (such as the leftmost lane) of the first sub-road.

[0146] The automatic driving method provided by the embodiments of the present application controls the intelligent driving device to drive in a certain lane of a sub-road to a target position according to the SD map navigation information and the perception information of the intelligent driving device, without relying on high-precision maps, which helps to reduce the production and maintenance costs of maps required for automatic driving, helps to improve the geographical range to which the automatic driving technology can be applied, and can improve the robustness of the navigation function of the intelligent driving device. In addition, when determining the traffic degree information, the traffic degree information of multiple sub-roads can be superimposed, for the road beyond the perception distance of the intelligent driving device, timely and more accurate lane-level navigation guidance can be provided, redundant lane changing operations can be reduced, and the probability of being unable to navigate and drive due to lane changing can be reduced. The certain lane can be a lane with the lowest navigation cost or a lane with a navigation cost lower than a cost threshold, so as to improve the traffic efficiency of the intelligent driving device at the intersection and the success rate of navigating to the target position. In addition, the navigation cost of the lane can be adjusted in combination with the lane type, the road right, and the way of passing through the intersection (straight or turning), so as to improve the traffic efficiency, human-likeness, and safety of the intelligent driving device.

[0147] In various embodiments of the present application, the terms and / or descriptions of various embodiments are consistent and can be mutually referred to if there is no special description and logical conflict, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0148] The automatic driving method provided by the embodiments of the present application is described in detail above in combination with FIGS. 1 to 12. The device provided by the embodiments of the present application will be described in detail below in combination with FIGS. 13 and 14. It should be understood that the description of the device embodiments corresponds to the description of the method embodiments, and therefore, the content not described in detail can be referred to the method embodiments described above, which will not be described here again for brevity.

[0149] FIG. 13 shows a schematic block diagram of an automatic driving apparatus 2000 provided by the embodiments of the present application, which can include units for performing the method 400, the method 500, and the method 600. Each unit in the apparatus 2000 is configured to implement a corresponding flow in the methods provided by the embodiments of the present application. The apparatus 2000 includes an obtaining unit 2010, which can be configured to implement corresponding data obtaining or transceiving functions. The apparatus 2000 further includes a processing unit 2020, which can be configured to implement corresponding processing functions.

[0150] Optionally, the apparatus 2000 further includes a storage unit, which can be configured to store instructions and / or data. The processing unit 2020 can read the instructions and / or data in the storage unit, so that the apparatus implements the related actions in the foregoing various method embodiments.

[0151] It should be understood that the specific process by which each unit performs the corresponding steps described above has been described in detail in the foregoing method embodiments, and thus will not be described here again for the sake of brevity.

[0152] It should also be understood that the apparatus 2000 here is in the form of functional units. The term “module” or “unit” here can refer to an application-specific ASIC, an electronic circuit, a processor (for example, a shared processor, a dedicated processor, or a group of processors) and a memory for executing one or more software or firmware programs, and other suitable components that integrate logic circuitry and / or other circuitry for performing the described functions.

[0153] The apparatus of each of the above-described solutions has the functions of implementing the corresponding steps performed by the computing platform 150 in the above-described methods. The functions can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described functions; for example, the obtaining unit 2010 can be replaced by a transceiver, and other units such as the processing unit can be replaced by a processor, for performing the related processing operations in the various method embodiments.

[0154] For example, the obtaining unit 2010 and the processing unit 2020 can be arranged in the intelligent driving device 100 shown in FIG. 1, or can also be arranged in the system shown in FIG. 2. More specifically, the obtaining unit 2010 and the processing unit 2020 described above can be arranged in the regulation and control module 240, or can also be arranged in the road information generation module 230. For example, the operations performed by the obtaining unit 2010 and the processing unit 2020 described above can be performed by one processor, or can also be performed by different processors. In a specific implementation process, the one or more processors described above can be the processor arranged in the intelligent driving device 100 shown in FIG. 1; or the apparatus 2000 described above can be a chip arranged in the intelligent driving device 100.

[0155] In practical implementation, the units in the above apparatus can be integrated together in whole or in part, or can be independently implemented. In one implementation, the units are integrated together to be implemented in the form of a system on a chip (SoC).

[0156] FIG. 14 is another schematic block diagram of an automatic driving apparatus provided by an embodiment of the present application. The automatic driving apparatus 2100 shown in FIG. 14 can include a processor 2110, a transceiver 2120, and a memory 2130. The processor 2110, the transceiver 2120, and the memory 2130 are connected through an internal connection path. The memory 2130 is configured to store instructions, and the processor 2110 is configured to execute the instructions stored in the memory 2130 to implement the methods in the above embodiments. Alternatively, the memory 2130 can be coupled to the processor 2110 through an interface, or the memory 2130 can be integrated with the processor 2110.

[0157] It should be noted that the transceiver 2120 can include, but is not limited to, a transceiving device such as an input / output interface, to implement the communication between the apparatus 2100 and other devices or communication networks.

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

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

[0160] The embodiments of the present application further provide an intelligent driving device, which comprises the autonomous driving device 2000 or the autonomous driving device 2100 in the above embodiments.

[0161] The embodiments of the present application further provide a computer program product, which comprises computer program codes, and when the computer program codes are run on a computer, the computer is caused to implement the methods in the above embodiments of the present application.

[0162] The embodiments of the present application further provide a computer readable storage medium, which stores computer instructions, and when the computer instructions are run on a computer, the computer is caused to implement the methods in the above embodiments of the present application.

[0163] The embodiments of the present application further provide a chip, which comprises a circuit for implementing the methods in the above embodiments of the present application.

[0164] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices and units can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein.

[0165] In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" herein is a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can mean that A exists alone, A and B exist together, and B exists alone. In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or similar expressions means any combination of these items, including any combination of single item or multiple items. For example, at least one of a, b, or c can mean a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0166] The prefix of "first", "second" and the like in the embodiments of the present application are merely used to distinguish different description objects, and do not have the limitation on the position, order, priority, number or content of the described objects. The use of the prefix of ordinal numbers and the like for distinguishing the description objects in the embodiments of the present application does not constitute the limitation on the described objects, and the description of the described objects should refer to the description in the context of the claims or embodiments, and should not constitute the redundant limitation because of the use of the prefix.

[0167] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic, and the division of the units is merely a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0168] In each embodiment of the present application, the terms and / or descriptions between different embodiments are consistent and can be mutually referred to, and the technical features in different embodiments can be combined to form a new embodiment according to the inherent logical relationship, if there is no special description and logical conflict.

[0169] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e. can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

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

[0171] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An automatic driving method, characterized by, The method comprises: obtaining standard SD map navigation information, the SD map navigation information at least indicating a driving bias in at least one sub-road within a first range of the intelligent driving device and / or a driving direction at at least one intersection, wherein a first sub-road in the at least one sub-road is a sub-road between two adjacent intersections or a sub-road between a current position of the intelligent driving device and a nearest adjacent intersection in front; determining first traffic degree information according to the SD map navigation information, the first traffic degree information indicating a traffic degree of each of a plurality of parts in a width direction of the first sub-road; controlling the intelligent driving device to drive in the first sub-road according to perception information of the intelligent driving device and the first traffic degree information, wherein the perception information indicates positions of a plurality of lanes in the first sub-road and a position of a lane in which the intelligent driving device is located.

2. The method of claim 1, wherein, The controlling the intelligent driving device to drive in the first sub-road according to the perception information of the intelligent driving device and the first traffic degree information comprises: determining a navigation cost of each of the plurality of lanes according to the first traffic degree information, the navigation cost indicating a difficulty of driving along the lane to a first intersection and being able to drive in a driving direction indicated by the SD map navigation information at the first intersection, wherein the first intersection is an intersection between the first sub-road and a nearest adjacent sub-road in front of the first sub-road; controlling the intelligent driving device to drive in a lane in which the navigation cost is less than or equal to a cost threshold.

3. The method of claim 2, wherein, The first sub-road is a sub-road in which the intelligent driving device is currently located, the at least one sub-road further comprises a second sub-road, the second sub-road is located between a target position of the intelligent driving device and the first sub-road, and the determining the first traffic degree information according to the SD map navigation information comprises: determining second traffic degree information according to a driving bias in the second sub-road and / or a driving direction at a second intersection, the second traffic degree information indicating a traffic degree of each of a plurality of parts in a width direction of the second sub-road, wherein the second intersection is an intersection between the second sub-road and a nearest adjacent sub-road in front of the second sub-road; determining third traffic degree information according to a driving bias in the first sub-road and / or a driving direction at the first intersection, the third traffic degree information indicating a second traffic degree of each of a plurality of parts in a width direction of the first sub-road; performing a first processing on the second traffic degree information and the third traffic degree information to obtain the first traffic degree information.

4. The method according to claim 2 or 3, characterized in that, The perception information further indicates a lane type of each lane in the first sub-road. The determining the navigation cost of each of the plurality of lanes according to the first traffic degree information comprises: determining the navigation cost of each of the plurality of lanes according to the first traffic degree information and the lane type.

5. The method of claim 4, wherein, The method further includes: The navigation cost of each lane in the plurality of lanes is determined according to the first traffic degree information and the lane type, including: When the lane type of a first lane in the plurality of lanes is at least one of the following, the navigation cost of the first lane is increased:

6. The method according to any one of claims 2 to 5, characterized in that, Tidal lane, variable lane, bus lane, temporary parking lane, risk avoidance lane, emergency lane. The navigation cost of each lane in the plurality of lanes is determined according to the first traffic degree information, including:

7. The method according to any one of claims 2 to 6, characterized in that, When the first sub-road is connected to a third sub-road, and the intelligent driving device needs to turn from the third sub-road to the first sub-road, the navigation cost of a second lane in the plurality of lanes is reduced, the second lane being a lane on the side corresponding to the turning direction in the plurality of lanes. The navigation cost of a third lane and a fourth lane in the plurality of lanes is less than or equal to the cost threshold, and the method of controlling the intelligent driving device to drive in a lane in the plurality of lanes whose navigation cost is less than or equal to the cost threshold, including:

8. The method according to any one of claims 1 to 7, characterized in that, When the road right of the third lane is higher than the road right of the fourth lane, the intelligent driving device is controlled to drive in the third lane. The SD map navigation information further indicates the number of lanes in the first sub-road and the orientation of each lane, and the method further includes:

9. An automatic driving device characterized by comprising: According to the orientation of each lane in the first sub-road, the plurality of parts in the width direction of the first sub-road are determined. Including: An acquisition unit is configured to acquire standard SD map navigation information, the SD map navigation information indicating at least one of driving bias in at least one sub-road within a first range of an intelligent driving device and / or driving direction at at least one intersection, wherein a first sub-road in the at least one sub-road is a sub-road between two adjacent intersections, or a sub-road between a current position of the intelligent driving device and a nearest adjacent intersection in front of the intelligent driving device; A processing unit is configured to determine first traffic degree information according to the SD map navigation information, the first traffic degree information indicating a passable degree of each part of a plurality of parts in a width direction of the first sub-road; 10. The apparatus of claim 9, wherein, The processing unit is further configured to control the intelligent driving device to drive in the first sub-road according to perception information of the intelligent driving device and the first traffic degree information, wherein the perception information indicates positions of a plurality of lanes in the first sub-road and a position of a lane in which the intelligent driving device is located. The processing unit is configured to: Determine a navigation cost of each lane in the plurality of lanes according to the first traffic degree information, the navigation cost indicating a difficulty of driving according to a driving direction indicated by the SD map navigation information at a first intersection when driving along the lane to the first intersection, wherein the first intersection is an intersection between the first sub-road and a nearest adjacent sub-road in front of the first sub-road; Control the intelligent driving device to drive in a lane in the plurality of lanes whose navigation cost is less than or equal to a cost threshold.

11. The apparatus of claim 10, wherein, The first sub-road is a sub-road currently located by the intelligent driving device, and the at least one sub-road further includes a second sub-road, the second sub-road is located between a target position of the intelligent driving device and the first sub-road, and the processing unit is configured to: determine second passability information according to a driving direction at the second intersection and / or a driving bias in the second sub-road, the second passability information indicating a passability of each of a plurality of portions in a width direction of the second sub-road, wherein the second intersection is an intersection between the second sub-road and a nearest neighbor sub-road in front of the second sub-road; determine third passability information according to a driving direction at the first intersection and / or a driving bias in the first sub-road, the third passability information indicating a second passability of each of a plurality of portions in a width direction of the first sub-road; perform a first processing on the second passability information and the third passability information to obtain the first passability information.

12. The apparatus of claim 10 or 11, wherein, The perception information further indicates a lane type of each lane in the first sub-road, and the processing unit is configured to: determine a navigation cost of each lane in the plurality of lanes according to the first passability information and the lane type.

13. The apparatus of claim 12, wherein, The processing unit is configured to: increase the navigation cost of a first lane in the plurality of lanes when the lane type of the first lane is at least one of the following: a tidal lane, a variable lane, a bus lane, a temporarily stop-allowed lane, an escape lane, and an emergency lane.

14. The apparatus of any one of claims 10-13, wherein, The processing unit is configured to: decrease a navigation cost of a second lane in the plurality of lanes when the first sub-road is connected to a third sub-road and the intelligent driving device needs to turn from the third sub-road to the first sub-road, the second lane being a lane corresponding to a turning direction in the plurality of lanes.

15. The apparatus of any one of claims 10-14, wherein, The navigation cost of a third lane and a fourth lane in the plurality of lanes is less than or equal to the cost threshold, and the processing unit is configured to: control the intelligent driving device to drive in the third lane when a road right of the third lane is higher than a road right of the fourth lane.

16. The apparatus of any one of claims 9 to 15, wherein, The SD map navigation information further indicates a number of lanes in the first sub-road and a guide of each lane, and the processing unit is further configured to: determine the plurality of portions in the width direction of the first sub-road according to the guide of each lane in the first sub-road.

17. An automatic driving device characterized by comprising: The apparatus comprises: a processor configured to execute a computer program stored in a memory to cause the apparatus to perform the method of any one of claims 1 to 8.

18. The apparatus of claim 17, wherein, The apparatus further comprises the memory.

19. An intelligent driving device, characterized by comprising: The apparatus comprises any one of claims 9 to 18.

20. A computer-readable storage medium, characterized in that, The computer program product comprises computer program code which, when executed by a processor, implements the method of any one of claims 1 to 8.

21. A computer program product, characterised in that, The chip comprises a circuit configured to perform the method of any one of claims 1 to 8.

22. A chip, characterized by ​

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