Autonomous vehicle and driving control method, device, equipment and medium thereof

Abstract

The present disclosure provides an autonomous vehicle and a driving control method, device, equipment and medium thereof, relates to the technical field of autonomous driving and high-precision map, in particular to the technical field of data processing and intelligent control, and the method comprises the following steps: obtaining a target shielding area corresponding to an autonomous vehicle at a driving intersection; determining a target shielding lane in the target shielding area and obtaining corresponding lane correlation information; obtaining obstacle correlation information of an obstacle in the target shielding area based on the lane correlation information; judging whether a preset speed limit condition is reached based on the obstacle correlation information, and if so, generating a target speed limit area at the intersection of a to-be-traveled lane of the autonomous vehicle and the target shielding lane; the autonomous vehicle is actively and timely speed-limited in advance, the vehicle speed is reduced before a visual blind area exists, sufficient reaction space and time are left for the occurrence of an emergency, and the safety and reliability of autonomous driving are effectively improved.

Description

Technical Field

[0001] This disclosure relates to the fields of autonomous driving and high-precision map technology, and particularly to the fields of data processing and intelligent control, providing an autonomous driving vehicle and its driving control method, device, equipment and medium. Background Technology

[0002] Currently, when approaching sharp bends, the crest of slopes, or other areas where visibility is limited, or when overtaking or encountering an emergency, motor vehicles should slow down and sound their horn to signal. When autonomous vehicles turn left at intersections, static obstructions from flowerbeds, shrubs, etc., on either side of the lane can affect their perception capabilities (i.e., lidar, cameras, etc.). Like human drivers, autonomous vehicles also need to proactively limit their speed in these dangerous areas prone to unexpected pedestrian appearances (like "ghost pedestrians"), reducing speed before blind spots appear to allow sufficient reaction time and space for unforeseen events. However, current technology cannot proactively identify and handle such obstacles appearing unexpectedly at intersections, increasing the collision risk and compromising the safety of autonomous vehicles. Summary of the Invention

[0003] The technical problem to be solved by this disclosure is to overcome the shortcomings of existing technologies in autonomous driving, which cannot efficiently and reasonably identify and respond to driving obstructions, resulting in the inability to guarantee the driving safety of autonomous driving. This disclosure provides an autonomous driving vehicle and its driving control method, device, equipment and medium.

[0004] This disclosure solves the above-mentioned technical problems through the following technical solution:

[0005] According to one aspect of this disclosure, a driving control method for an autonomous vehicle is provided, the driving control method comprising:

[0006] Obtain the target occlusion area corresponding to the autonomous vehicle at the intersection where it is driving;

[0007] Determine the target occluded lane within the target occlusion area and obtain the corresponding lane association information;

[0008] Obtain obstacle association information of obstacles in the target occlusion area based on the lane association information;

[0009] Based on the obstacle association information, it is determined whether the preset speed limit condition has been met. If it has been met, a target speed limit area is generated at the intersection of the lane to be driven by the autonomous vehicle and the target obstructed lane.

[0010] Wherein, the starting boundary of the target speed-limited area near the autonomous vehicle is located at or outside the boundary of the target obstructed lane;

[0011] When the autonomous vehicle enters the target speed-limited area, the autonomous driving is controlled to drive at the speed limit.

[0012] According to another aspect of this disclosure, a driving control device for an autonomous vehicle is provided, the driving control device comprising:

[0013] The target occlusion area acquisition module is used to acquire the target occlusion area corresponding to the autonomous vehicle at the intersection where it is driving;

[0014] The lane association information determination module is used to determine the target occluded lane in the target occluded area and obtain the corresponding lane association information;

[0015] An obstacle association information acquisition module is used to acquire obstacle association information of obstacles in the target occlusion area based on the lane association information;

[0016] The first judgment module is used to determine whether the preset speed limit condition has been met based on the obstacle association information. If it has been met, the target speed limit area generation module is called to generate a target speed limit area at the intersection of the lane to be driven by the autonomous vehicle and the target obstructed lane.

[0017] Wherein, the starting boundary of the target speed-limited area near the autonomous vehicle is located at or outside the boundary of the target obstructed lane;

[0018] The driving control module is used to control the autonomous driving vehicle to drive at the target speed limit area when the autonomous vehicle enters the target speed limit area.

[0019] According to another aspect of this disclosure, an autonomous vehicle is provided, the autonomous vehicle including the driving control device for an autonomous vehicle as described above.

[0020] According to another aspect of this disclosure, an electronic device is provided, comprising:

[0021] At least one processor; and

[0022] A memory communicatively connected to the at least one processor; wherein,

[0023] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described above.

[0024] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform the method according to the above description.

[0025] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method described above.

[0026] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0027] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0028] Figure 1 This is a first schematic diagram of a driving control method for an autonomous vehicle according to a first embodiment of the present disclosure;

[0029] Figure 2 This is a schematic diagram of a first scenario of an autonomous vehicle according to a first embodiment of the present disclosure;

[0030] Figure 3 This is a schematic diagram of a second scenario of an autonomous vehicle according to the first embodiment of this disclosure;

[0031] Figure 4 This is a second schematic diagram of a driving control method for an autonomous vehicle according to a first embodiment of the present disclosure;

[0032] Figure 5 This is a schematic diagram of a third scenario of an autonomous vehicle according to the first embodiment of this disclosure;

[0033] Figure 6 This is a third schematic diagram of a driving control method for an autonomous vehicle according to a first embodiment of the present disclosure;

[0034] Figure 7 This is a fourth schematic diagram of a driving control method for an autonomous vehicle according to the first embodiment of the present disclosure;

[0035] Figure 8 This is a fifth schematic diagram of a driving control method for an autonomous vehicle according to the first embodiment of the present disclosure;

[0036] Figure 9 This is a schematic diagram of the first module of the driving control device for an autonomous vehicle according to a second embodiment of the present disclosure;

[0037] Figure 10 This is a schematic diagram of the second module of the driving control device for an autonomous vehicle according to a second embodiment of the present disclosure;

[0038] Figure 11This is a block diagram of an electronic device used to implement the driving control method for an autonomous vehicle according to embodiments of the present disclosure. Detailed Implementation

[0039] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0040] Example 1

[0041] like Figure 1 As shown, the driving control method for an autonomous vehicle in this embodiment includes:

[0042] S101. Obtain the target occlusion area corresponding to the autonomous vehicle at the intersection where it is driving;

[0043] During the operation of an autonomous vehicle, such as Figure 2 and 3 As shown, for example, when an autonomous vehicle A needs to turn left at an intersection, static obstructions B (including but not limited to flower beds, shrubs, etc.) will affect the vehicle's perception of the current road environment. Therefore, it is necessary to consider the obstruction caused by these static obstructions in a timely manner and determine all obstruction areas C1 corresponding to the current autonomous vehicle A (see...). Figure 2 ) and C2 (see Figure 3 ).

[0044] S102. Determine the target occluded lane within the target occluded area and obtain the corresponding lane association information. The target occluded area generally corresponds to one or more lanes. Obstacles such as vehicles in a certain lane (e.g., the lane closest to the flower bed or shrubs) are related to the driving safety of the autonomous vehicle at the intersection. Therefore, it is necessary to identify the target occluded lane within the target occluded area and obtain the corresponding association information in a timely manner so as to further analyze the specific situation on the lane and thus achieve reliable processing and control of each control link of the autonomous vehicle's driving control.

[0045] S103. Obtain obstacle association information of obstacles in the target occlusion area based on lane association information; specifically, determine the obstacle situation in the corresponding target occlusion area by using lane association information combined with high-precision maps, real-time captured road surface images, etc.; wherein, obstacle association information includes whether there are obstacles, the distance of obstacles from the nearest occlusion point in the target occlusion area, etc.

[0046] S104. Determine whether the preset speed limit condition has been met based on the obstacle association information. If it has been met, proceed to step S105.

[0047] S105. Generate a target speed limit area at the intersection of the lane where the autonomous vehicle is waiting to drive and the target obscured lane;

[0048] Among them, the starting boundary of the target speed-limited area near the autonomous vehicle is located at or outside the boundary of the target obstructed lane;

[0049] To achieve better speed limit control, the starting boundary or starting point of the target speed-limited area near the autonomous vehicle is positioned at a preset distance outside the boundary of the target obstructed lane. This preset distance can be pre-set based on a range determined through practical experience, or randomly set within that range. The specific method used can be determined or adjusted according to the actual scenario requirements. Furthermore, this preset distance can also be personalized based on the passenger's riding experience needs, simultaneously achieving better vehicle control and enhancing the passenger's riding experience.

[0050] See details Figure 2 Speed ​​limit area S1 in Figure 3 Speed-limited area S2.

[0051] Specifically, the shape of the target speed limit area can be rectangular, arc-shaped, or even irregular. The specific shape of the speed limit area can be determined based on the specific situation of the overlapping area at the intersection of the actual lane to be driven and the target obscured lane.

[0052] S106. When an autonomous vehicle enters a target speed-limited area, control the autonomous driving to travel at the speed limit.

[0053] This speed-limited zone is used to determine whether to activate the vehicle speed-limiting function when an autonomous vehicle enters the waiting lane but has not yet reached the intersection. When entering the speed-limited zone, the vehicle speed is automatically limited; when the vehicle is about to leave the speed-limited zone or has completely left the speed-limited zone, the vehicle speed is controlled to return to normal, or it can drive normally automatically based on the most recently planned driving plan. This allows for early intervention to address situations that may cause blind spots in the vehicle's driving vision, ensuring the timeliness and rationality of the driving control of the autonomous vehicle.

[0054] In this solution, once an autonomous vehicle is about to turn at an intersection, it automatically detects all obstructed areas at that intersection that pose potential dangers and affect the vehicle's timely safety. For each obstructed area, it extracts the lanes that affect the vehicle's movement and then determines whether to activate a preset speed limit strategy based on the specific circumstances of the obstacles in the lanes. If it is determined that a necessary speed limit scenario is involved, the preset speed limit strategy is activated. That is, a corresponding speed limit area is determined at the intersection of the autonomous vehicle's lane and the target obstructed lane. Once the vehicle enters the speed limit area, the speed of the autonomous vehicle is controlled in advance to proactively and timely limit the speed of the autonomous vehicle in advance. This reduces the vehicle speed before blind spots appear, leaving sufficient reaction space and time in case of emergencies. It also provides advance judgment for the downstream speed planning module, reducing its planning difficulty and increasing the feasibility of successful planning. Ultimately, this reduces the collision risk of autonomous driving and effectively improves the safety and reliability of autonomous driving.

[0055] like Figure 4 As shown, the driving control method for autonomous vehicles in this embodiment is as follows: Figure 1 Further improvements to the technical solution shown are specifically:

[0056] In one feasible embodiment, step S101 includes:

[0057] S1011. Based on at least one of the following devices installed in the autonomous vehicle: lidar, position sensor, and speed sensor, obtain the initial occlusion area corresponding to the autonomous vehicle at the intersection.

[0058] Taking the acquisition of static occlusion areas at intersections by LiDAR as an example, LiDAR is used to monitor the occlusion areas around the autonomous vehicle in real time. When the autonomous vehicle is expected to turn at the intersection ahead according to the preset planned path, the LiDAR continuously emits rays to obtain all reachable and unreachable points in the surrounding area. Based on these unreachable points, the corresponding static occlusion areas are calculated in a timely manner through a preset algorithm.

[0059] Of course, other methods can be used to detect the corresponding occluded areas on the lane where the autonomous vehicle is about to drive, as long as the occluded areas can be identified in a timely and accurate manner. Therefore, they will not be elaborated on here.

[0060] S1012. Select the effective occlusion area in the initial occlusion area using the preset filtering rules, and use it as the target occlusion area.

[0061] Specifically, the target occlusion area of ​​the preset filtering rules meets at least one of the following conditions:

[0062] The criteria include the area where the target occludes the lane, the distance between the nearest occlusion point and the lane reference line of the lane to be driven is less than a second set threshold, and the corresponding area is within a preset area range.

[0063] In this solution, considering that the occlusion areas detected by LiDAR may be too large, too far away, or misjudged, rendering them unusable or unsuitable, all occlusion areas are traversed and filtered. The filtered occlusion areas contain the target occluded lane (oncoming straight lane, left straight lane, etc.) within their coverage area to avoid misjudgment. The distance between the nearest occlusion point of the filtered occlusion area and the lane reference line is small and cannot be too far. The area of ​​the filtered occlusion area is within a certain size range and cannot be too large. This ensures that the corresponding driving control logic is executed based on the filtered effective occlusion areas, guaranteeing the accuracy of subsequent speed limit control for autonomous vehicles and reducing unnecessary data processing to a certain extent. This effectively improves the efficiency of vehicle speed limit control and ensures the timeliness of speed limit control.

[0064] Specifically, see Figure 5 As shown, the distance L between the nearest occlusion point P1 and the lane reference line L1 in the occlusion area is shown. Region M1 corresponds to the initial occlusion area, and region M2 corresponds to the domain target occlusion area.

[0065] In one possible implementation, step S102 includes:

[0066] S1021. Obtain several first lanes corresponding to the location of the target occlusion area based on a high-precision map;

[0067] S1022. Select the lane closest to the lane obstruction from several first lanes as the target obstruction lane, and obtain the lane association information corresponding to the target obstruction lane;

[0068] Based on high-precision maps, the system can obtain information on all lanes and their specific conditions at the current intersection for autonomous vehicles. When a vehicle needs to turn left, it is crucial to consider the connections between the oncoming straight lane and the left-hand straight lane.

[0069] In this scheme, considering that each target occlusion area covers multiple lanes, and that only the lane closest to the occlusion object is generally considered to affect the driving of autonomous vehicles, the target occlusion area is selected as the target occlusion lane. Then, the specific situation on the target occlusion area is analyzed to control vehicle driving, thereby reducing unnecessary data processing. While ensuring the effectiveness of vehicle speed limit control, it also greatly improves the efficiency of vehicle speed limit control and ensures the timeliness of speed limit control.

[0070] Step S103 includes:

[0071] S1031. Based on high-precision maps and lane association information, obtain obstacle association information for each obstacle in the lane where the target is occluded.

[0072] Specifically, it can directly obtain the basic parameters of the lane based on the high-precision map, such as lane position and lane structure; it can also directly obtain the real-time location information of each vehicle traveling on the lane.

[0073] In one possible implementation, step S104 includes:

[0074] S10411. Determine the center point of the corresponding obstacle based on obstacle association information;

[0075] Specifically, such as Figure 5 As shown, the center point P2 of the obstacle is determined based on the position information of each obstacle in the target occlusion lane provided by the high-precision map.

[0076] S10412. Determine the nearest occlusion point corresponding to the target occlusion area based on the occlusion area information of the target occlusion area;

[0077] Specifically, the nearest occlusion point P1 corresponding to the target occlusion area, see details below. Figure 5 Among them, the nearest occlusion point P1 is the midpoint between the nearest and farthest points in the target occlusion area that are closest to the lane to be driven.

[0078] S10413. When the distance between the center point and the nearest occluded point is greater than the first set threshold, it is determined that the current driving scenario belongs to the necessary speed limit scenario and the preset speed limit condition is met.

[0079] In this solution, by comparing the center point of the obstacle with the nearest occlusion point in the target occlusion area, it is determined whether the obstacle in the corresponding target occlusion area will cause a driving hazard due to blind spots for the autonomous vehicle. If the distance is greater than a certain value, it is determined that the obstacle is far from the target occlusion area and exists in the occlusion area, posing a potential safety risk. If the autonomous vehicle needs to turn at this time, the speed limit function needs to be automatically activated to proactively and timely limit the speed of the autonomous vehicle in advance, reduce the speed before the blind spot exists, leave sufficient reaction space and time in case of emergencies, and effectively ensure the driving safety of the autonomous vehicle.

[0080] In one possible implementation, such as Figure 6 As shown, step S104 includes:

[0081] S10421. Determine the center point of the corresponding obstacle based on obstacle association information;

[0082] S10422. Determine the nearest occlusion point corresponding to the target occlusion area based on the occlusion area information of the target occlusion area;

[0083] S10423. When the distance between the center point and the nearest occluded point is less than or equal to the first set threshold, it is determined that the current driving scenario belongs to a non-necessary speed limit scenario.

[0084] S10424. Adopt a set driving control strategy to control the driving state of the autonomous vehicle along the lane to be driven.

[0085] In this solution, by comparing the center point of an obstacle with the nearest occlusion point in the target occlusion area, it is determined whether the obstacle in the corresponding target occlusion area will cause a driving hazard due to blind spots for the autonomous vehicle. If the distance is less than a certain value, it is determined that the obstacle is close enough to the current vehicle and does not constitute a potential safety risk caused by blind spots, so there is no need to activate the speed limit function. At this time, based on the position of the obstacle and the current path planning scheme, a matching driving control scheme is automatically generated to automatically control the autonomous vehicle to drive safely. This achieves automatic differentiation and identification of scenarios where speed limits are necessary and scenarios where speed limits are necessary. In scenarios where speed limits are necessary, the speed limit control of the autonomous vehicle is implemented in a timely and efficient manner. In scenarios where speed limits are not necessary, other driving control schemes are used to guide the autonomous vehicle. This greatly optimizes the driving control method of the autonomous vehicle, and the control process is efficient and timely, which can meet the control scenarios with higher requirements for autonomous driving.

[0086] It should be noted that the replanning of the driving path and determination of the vehicle's driving status based on the location of obstacles and the current path planning scheme are mature technologies in this field, and therefore will not be elaborated on here.

[0087] In one possible implementation, such as Figure 7 As shown, step S104 includes:

[0088] S1043. When there are no obstacles within the target occlusion area represented by obstacle association information, the current driving scenario is determined to be a necessary speed limit scenario, and the preset speed limit condition is met.

[0089] In this solution, when it is determined that there are no obstacles in the target occlusion area, in order to avoid misjudgment, delay in judgment results, and other situations, and to prevent the possibility that there are actually obstacles in the target occlusion area but they have not been detected, which poses a potential safety risk, this scenario is also identified as a necessary speed limit scenario. If the autonomous vehicle needs to turn at this time, the speed limit function needs to be automatically activated to proactively and timely limit the speed of the autonomous vehicle in advance, reduce the vehicle speed before the blind spot exists, leave sufficient reaction space and time in case of emergencies, and effectively ensure the driving safety of the autonomous vehicle.

[0090] In one possible implementation, such as Figure 8 As shown, step S105 includes:

[0091] S1051. Obtain the lane reference line of the lane to be driven by the autonomous vehicle, and the center line of the lane obscured by the target.

[0092] Among them, the lane reference line of the lane to be driven is the vehicle driving reference route generated by the front module based on the route and high-precision map.

[0093] S1052. Obtain the intersection of the lane reference line and the center line;

[0094] S1053. Using the intersection point as a reference, extend the first set area along the lane to be driven and in the direction closer to the autonomous vehicle to obtain the target speed limit area.

[0095] The outer edge of the first defined area is located at or outside the boundary of the target obstructed lane.

[0096] In this solution, the intersection of the lane reference line and the center line is used as a reference. The system extends along the lane to be driven and also extends in a direction perpendicular to the lane to be driven. For example, it extends X meters in the direction closer to the autonomous vehicle to exceed the boundary of the target obstructed lane, and extends Y meters on both sides in a direction perpendicular to the lane to be driven. This automatically generates a speed limit area that matches the lane in the current driving scenario without human intervention, thereby achieving the effect of timely and advance speed limit control for autonomous vehicles.

[0097] At this point, the target speed limit area only needs to meet the requirement that the speed limit be applied as soon as the vehicle enters the starting boundary of the target speed limit area when it is about to enter the intersection of the waiting lane and the target obstructed lane. As for when the speed limit control is lifted, it can be lifted in the second half of the overlapping area at the intersection, or it can be lifted after the vehicle leaves the overlapping area at the intersection.

[0098] In one possible implementation, step S1053 includes:

[0099] Based on the intersection point, extend the first set area along the lane to be driven and in the direction closer to the autonomous vehicle, and extend the second set area along the lane to be driven and in the direction away from the autonomous vehicle.

[0100] The common area of ​​the first and second set areas is taken as the target speed limit area.

[0101] The first set area is greater than or equal to the second set area.

[0102] In this scheme, the intersection of the lane reference line and the center line is used as a reference. The system extends along the lane to be driven and also extends in a direction perpendicular to the lane to be driven. For example, it extends X1 meters towards the autonomous vehicle to exceed the boundary of the target obstructed lane and extends Y1 meters on both sides in a direction perpendicular to the lane to be driven; it extends X2 meters towards the autonomous vehicle to exceed the boundary of the target obstructed lane and extends Y2 meters on both sides in a direction perpendicular to the lane to be driven. This automatically generates a speed limit area that matches the lane in the current driving scenario without human intervention, thereby achieving timely and advance speed limit control for autonomous vehicles.

[0103] At this point, the target speed limit area needs to meet the requirement that when a vehicle is about to enter the intersection of the waiting lane and the target obstruction lane, the speed limit is applied once it enters the starting boundary of the target speed limit area, and the speed limit control is lifted when it reaches the ending boundary. The ending boundary can be on the target obstruction lane, or at the other boundary of the target obstruction lane, or outside the other boundary.

[0104] To facilitate the generation of the target speed limit area, the intersection of the lane reference line and the center line can be used as a reference, and the same expansion method can be used to expand to both sides, i.e., X1=X2, Y1=Y2. This further improves the generation efficiency of the target speed limit area, enabling autonomous vehicles to prepare for speed limit control as early as possible, and further improves the speed limit control effect of autonomous vehicles.

[0105] In one possible implementation, the method further includes the following steps after step S105 and before step S106:

[0106] S10601. Obtain the actual driving speed of the autonomous vehicle;

[0107] S10602. When the actual driving speed exceeds the preset speed threshold, the target speed limit area is extended outward from the starting boundary of the autonomous vehicle to update the new target speed limit area.

[0108] In this solution, it is necessary to further differentiate between autonomous vehicles with different driving speeds. For vehicles within the set driving speed range, the target speed limit area is obtained uniformly using the speed limit area generation process described above. For vehicles whose driving speed exceeds the preset speed threshold, the unified speed limit area needs to be expanded to further ensure that speed limit control can continue in a timely and effective manner for different autonomous vehicles.

[0109] Example 2

[0110] like Figure 9 As shown, the driving control device for the autonomous vehicle in this embodiment includes:

[0111] Target Occlusion Area Acquisition Module 1 is used to acquire the target occlusion area corresponding to the autonomous vehicle at the intersection where it is driving.

[0112] During the operation of an autonomous vehicle, such as Figure 2 and 3 As shown, for example, when an autonomous vehicle A needs to turn left at an intersection, static obstructions B (including but not limited to flower beds, shrubs, etc.) will affect the vehicle's perception of the current road environment. Therefore, it is necessary to consider the obstruction caused by these static obstructions in a timely manner and determine all obstruction areas C1 corresponding to the current autonomous vehicle A (see...). Figure 2 ) and C2 (see Figure 3 ).

[0113] The association information determination module 2 is used to determine the target occluded lane in the target occluded area and obtain the corresponding lane association information. The target occluded area generally corresponds to one or more lanes. Obstacles such as vehicles in a certain lane (e.g., the lane closest to the flower bed or shrubs) are related to the driving safety of autonomous vehicles when driving at intersections. Therefore, it is necessary to identify the target occluded lane in the target occluded area in a timely manner and obtain the corresponding association information so as to further analyze the specific situation on the lane, thereby realizing the reliable processing and control of each control link of the autonomous vehicle's driving control.

[0114] The obstacle association information acquisition module 3 is used to acquire obstacle association information of obstacles in the target occlusion area based on lane association information. Specifically, it uses lane association information combined with high-precision maps and real-time captured road surface images to determine the obstacle situation in the corresponding target occlusion area. The obstacle association information includes whether there are obstacles and the distance between the obstacles and the nearest occlusion point in the target occlusion area.

[0115] The first judgment module 4 is used to determine whether the preset speed limit condition has been met based on the obstacle association information. If it has been met, the target speed limit area generation module 5 is called to generate the target speed limit area at the intersection of the lane to be driven by the autonomous vehicle and the target occluded lane.

[0116] Among them, the starting boundary of the target speed-limited area near the autonomous vehicle is located at or outside the boundary of the target obstructed lane;

[0117] To achieve better speed limit control, the starting boundary or starting point of the target speed-limited area near the autonomous vehicle is positioned at a preset distance outside the boundary of the target obstructed lane. This preset distance can be pre-set based on a range determined through practical experience, or randomly set within that range. The specific method used can be determined or adjusted according to the actual scenario requirements. Furthermore, this preset distance can also be personalized based on the passenger's riding experience needs, simultaneously achieving better vehicle control and enhancing the passenger's riding experience.

[0118] See details Figure 2 Speed ​​limit area S1 in Figure 3 Speed-limited area S2.

[0119] Specifically, the shape of the target speed limit area can be rectangular, arc-shaped, or even irregular. The specific shape of the speed limit area can be determined based on the specific situation of the overlapping area at the intersection of the actual lane to be driven and the target obscured lane.

[0120] The driving control module 6 is used to control the autonomous driving to travel at a limited speed when the autonomous vehicle enters the target speed-limited area.

[0121] This speed-limited zone is used to determine whether to activate the vehicle speed-limiting function when an autonomous vehicle enters the waiting lane but has not yet reached the intersection. When entering the speed-limited zone, the vehicle speed is automatically limited; when the vehicle is about to leave the speed-limited zone or has completely left the speed-limited zone, the vehicle speed is controlled to return to normal, or it can drive normally automatically based on the most recently planned driving plan. This allows for early intervention to address situations that may cause blind spots in the vehicle's driving vision, ensuring the timeliness and rationality of the driving control of the autonomous vehicle.

[0122] In this solution, once an autonomous vehicle is about to turn at an intersection, it automatically detects all obstructed areas at that intersection that pose potential dangers and affect the vehicle's timely safety. For each obstructed area, it extracts the lanes that affect the vehicle's movement and then determines whether to activate a preset speed limit strategy based on the specific circumstances of the obstacles in the lanes. If it is determined that a necessary speed limit scenario is involved, the preset speed limit strategy is activated. That is, a corresponding speed limit area is determined at the intersection of the autonomous vehicle's lane and the target obstructed lane. Once the vehicle enters the speed limit area, the speed of the autonomous vehicle is controlled in advance to proactively and timely limit the speed of the autonomous vehicle in advance. This reduces the vehicle speed before blind spots appear, leaving sufficient reaction space and time in case of emergencies. It also provides advance judgment for the downstream speed planning module, reducing its planning difficulty and increasing the feasibility of successful planning. Ultimately, this reduces the collision risk of autonomous driving and effectively improves the safety and reliability of autonomous driving.

[0123] like Figure 10 As shown, the driving control device for the autonomous vehicle in this embodiment is for... Figure 9 Further improvements to the technical solution shown are specifically:

[0124] In one possible implementation, the target occlusion area acquisition module 1 includes:

[0125] The initial occlusion area acquisition unit 7 is used to acquire the initial occlusion area of ​​the autonomous vehicle at the intersection based on at least one of the following devices: lidar, position sensor, and speed sensor installed in the autonomous vehicle.

[0126] Taking the acquisition of static occlusion areas at intersections by LiDAR as an example, LiDAR is used to monitor the occlusion areas around the autonomous vehicle in real time. When the autonomous vehicle is expected to turn at the intersection ahead according to the preset planned path, the LiDAR continuously emits rays to obtain all reachable and unreachable points in the surrounding area. Based on these unreachable points, the corresponding static occlusion areas are calculated in a timely manner through a preset algorithm.

[0127] Of course, other methods can be used to detect the corresponding occluded areas on the lane where the autonomous vehicle is about to drive, as long as the occluded areas can be identified in a timely and accurate manner. Therefore, they will not be elaborated on here.

[0128] The target occlusion area acquisition unit 8 is used to filter out the effective occlusion areas in the initial occlusion area using preset filtering rules, so as to use them as the target occlusion areas.

[0129] In one possible implementation, the target occlusion area of ​​the preset filtering rules meets at least one of the following conditions:

[0130] The criteria include the area where the target occludes the lane, the distance between the nearest occlusion point and the lane reference line of the lane to be driven is less than a second set threshold, and the corresponding area is within a preset area range.

[0131] In this solution, considering that the obstruction areas detected by LiDAR may be too large, too far away, or prone to misjudgment, making them unusable or unsuitable, all obstruction areas are traversed and filtered. The filtered obstruction areas are then selected if the target obstructing lane (oncoming straight lane, left-hand straight lane, etc.) exists within their coverage area to avoid misjudgment. The distance between the nearest obstruction point of the filtered obstruction area and the lane reference line (see details) is also considered. Figure 5As shown, the distance L between the nearest occlusion point P1 and the lane reference line L1 in the occlusion area is small and cannot be too far. The area covered by the selected occlusion area is within a certain range and cannot be too large. This ensures that the corresponding driving control logic is executed based on the selected effective occlusion area, thus guaranteeing the accuracy of the subsequent speed limit control of the autonomous vehicle. It also reduces unnecessary data processing to a certain extent, thereby effectively improving the efficiency of vehicle speed limit control and ensuring the timeliness of speed limit control.

[0132] In one possible implementation, the lane association information determination module 2 includes:

[0133] The first lane acquisition unit 9 is used to acquire several first lanes corresponding to the location of the target occlusion area based on a high-precision map.

[0134] The target occlusion lane determination unit 10 is used to select the lane closest to the lane occlusion object from a plurality of first lanes as the target occlusion lane.

[0135] Lane association information determination unit 11 is used to obtain lane association information corresponding to the target occluded lane;

[0136] Based on high-precision maps, the system can obtain information on all lanes and their specific conditions at the current intersection for autonomous vehicles. When a vehicle needs to turn left, it is crucial to consider the connections between the oncoming straight lane and the left-hand straight lane.

[0137] In this scheme, considering that each target occlusion area covers multiple lanes, and that only the lane closest to the occlusion object is generally considered to affect the driving of autonomous vehicles, the target occlusion area is selected as the target occlusion lane. Then, the specific situation on the target occlusion area is analyzed to control vehicle driving, thereby reducing unnecessary data processing. While ensuring the effectiveness of vehicle speed limit control, it also greatly improves the efficiency of vehicle speed limit control and ensures the timeliness of speed limit control.

[0138] The obstacle association information acquisition module 3 is also used to acquire obstacle association information for each obstacle in the target occluded lane based on high-precision map and lane association information.

[0139] Specifically, it can directly obtain the basic parameters of the lane based on the high-precision map, such as lane position and lane structure; it can also directly obtain the real-time location information of each vehicle traveling on the lane.

[0140] In one possible implementation, the first determination module 4 includes:

[0141] The center point determination unit 12 is used to determine the center point of the corresponding obstacle based on the obstacle association information;

[0142] Specifically, such as Figure 5 As shown, the center point P2 of the obstacle is determined based on the position information of each obstacle in the target occlusion lane provided by the high-precision map.

[0143] The nearest occlusion point determination unit 13 is used to determine the nearest occlusion point corresponding to the target occlusion area based on the occlusion area information of the target occlusion area;

[0144] Specifically, the nearest occlusion point P1 corresponding to the target occlusion area, see details below. Figure 5 Among them, the nearest occlusion point P1 is the midpoint between the nearest and farthest points in the target occlusion area that are closest to the lane to be driven.

[0145] The first judgment unit 14 is used to determine that the current driving scenario belongs to the necessary speed limit scenario and the preset speed limit condition is met when the distance between the center point and the nearest occluded point is greater than the first set threshold.

[0146] In this solution, by comparing the center point of the obstacle with the nearest occlusion point in the target occlusion area, it is determined whether the obstacle in the corresponding target occlusion area will cause a driving hazard due to blind spots for the autonomous vehicle. If the distance is greater than a certain value, it is determined that the obstacle is far from the target occlusion area and exists in the occlusion area, posing a potential safety risk. If the autonomous vehicle needs to turn at this time, the speed limit function needs to be automatically activated to proactively and timely limit the speed of the autonomous vehicle in advance, reduce the speed before the blind spot exists, leave sufficient reaction space and time in case of emergencies, and effectively ensure the driving safety of the autonomous vehicle.

[0147] In one possible implementation, the first judgment unit 14 is further configured to determine that the current driving scenario is a non-necessary speed limit scenario when the distance between the center point and the nearest occluded point is less than or equal to a first set threshold, and to call the driving control module to use the set driving control strategy to control the autonomous vehicle to drive along the lane to be driven.

[0148] In this solution, by comparing the center point of an obstacle with the nearest occlusion point in the target occlusion area, it is determined whether the obstacle in the corresponding target occlusion area will cause a driving hazard due to blind spots for the autonomous vehicle. If the distance is less than a certain value, it is determined that the obstacle is close enough to the current vehicle and does not constitute a potential safety risk caused by blind spots, so there is no need to activate the speed limit function. At this time, based on the position of the obstacle and the current path planning scheme, a matching driving control scheme is automatically generated to automatically control the autonomous vehicle to drive safely. This achieves automatic differentiation and identification of scenarios where speed limits are necessary and scenarios where speed limits are necessary. In scenarios where speed limits are necessary, the speed limit control of the autonomous vehicle is implemented in a timely and efficient manner. In scenarios where speed limits are not necessary, other driving control schemes are used to guide the autonomous vehicle. This greatly optimizes the driving control method of the autonomous vehicle, and the control process is efficient and timely, which can meet the control scenarios with higher requirements for autonomous driving.

[0149] It should be noted that the replanning of the driving path and determination of the vehicle's driving status based on the location of obstacles and the current path planning scheme are mature technologies in this field, and therefore will not be elaborated on here.

[0150] In one possible implementation, the first determination module 4 includes:

[0151] The second judgment unit 15 is used to determine that the current driving scenario is a necessary speed limit scenario and the preset speed limit condition is met when there is no obstacle in the target occlusion area represented by the obstacle association information.

[0152] In this solution, when it is determined that there are no obstacles in the target occlusion area, in order to avoid misjudgment, delay in judgment results, and other situations, and to prevent the possibility that there are actually obstacles in the target occlusion area but they have not been detected, which poses a potential safety risk, this scenario is also identified as a necessary speed limit scenario. If the autonomous vehicle needs to turn at this time, the speed limit function needs to be automatically activated to proactively and timely limit the speed of the autonomous vehicle in advance, reduce the vehicle speed before the blind spot exists, leave sufficient reaction space and time in case of emergencies, and effectively ensure the driving safety of the autonomous vehicle.

[0153] In one possible implementation, the target speed limit area generation module 5 includes:

[0154] Centerline acquisition unit 16 is used to acquire the lane reference line of the lane to be driven by the autonomous vehicle, and the centerline of the lane obscured by the target.

[0155] Among them, the lane reference line of the lane to be driven is the vehicle driving reference route generated by the front module based on the route and high-precision map.

[0156] Intersection acquisition unit 17 is used to acquire the intersection of the lane reference line and the center line;

[0157] The target speed limit area generation unit 18 is used to extend a first set area along the lane to be driven and toward the direction of the autonomous vehicle, based on the intersection point, to obtain the target speed limit area.

[0158] The outer edge of the first defined area is located at or outside the boundary of the target obstructed lane.

[0159] In this solution, the intersection of the lane reference line and the center line is used as a reference. The system extends along the lane to be driven and also extends in a direction perpendicular to the lane to be driven. For example, it extends X meters in the direction closer to the autonomous vehicle to exceed the boundary of the target obstructed lane, and extends Y meters on both sides in a direction perpendicular to the lane to be driven. This automatically generates a speed limit area that matches the lane in the current driving scenario without human intervention, thereby achieving the effect of timely and advance speed limit control for autonomous vehicles.

[0160] At this point, the target speed limit area only needs to meet the requirement that the speed limit be applied as soon as the vehicle enters the starting boundary of the target speed limit area when it is about to enter the intersection of the waiting lane and the target obstructed lane. As for when the speed limit control is lifted, it can be lifted in the second half of the overlapping area at the intersection, or it can be lifted after the vehicle leaves the overlapping area at the intersection.

[0161] In one possible implementation, the target speed limit area generation unit 18 is used to extend a first set area along the lane to be driven and toward the autonomous vehicle, with the intersection point as a reference, and extend a second set area along the lane to be driven and away from the autonomous vehicle, and take the common area of ​​the first set area and the second set area as the target speed limit area.

[0162] In one possible implementation, the first defined region is greater than or equal to the second defined region.

[0163] In this scheme, the intersection of the lane reference line and the center line is used as a reference. The system extends along the lane to be driven and also extends in a direction perpendicular to the lane to be driven. For example, it extends X1 meters towards the autonomous vehicle to exceed the boundary of the target obstructed lane and extends Y1 meters on both sides in a direction perpendicular to the lane to be driven; it extends X2 meters towards the autonomous vehicle to exceed the boundary of the target obstructed lane and extends Y2 meters on both sides in a direction perpendicular to the lane to be driven. This automatically generates a speed limit area that matches the lane in the current driving scenario without human intervention, thereby achieving timely and advance speed limit control for autonomous vehicles.

[0164] At this point, the target speed limit area needs to meet the requirement that when a vehicle is about to enter the intersection of the waiting lane and the target obstruction lane, the speed limit is applied once it enters the starting boundary of the target speed limit area, and the speed limit control is lifted when it reaches the ending boundary. The ending boundary can be on the target obstruction lane, or at the other boundary of the target obstruction lane, or outside the other boundary.

[0165] To facilitate the generation of the target speed limit area, the intersection of the lane reference line and the center line can be used as a reference, and the same expansion method can be used to expand to both sides, i.e., X1=X2, Y1=Y2. This further improves the generation efficiency of the target speed limit area, enabling autonomous vehicles to prepare for speed limit control as early as possible, and further improves the speed limit control effect of autonomous vehicles.

[0166] In one possible embodiment, the driving control device further includes:

[0167] The actual driving speed acquisition module is used to acquire the actual driving speed of the autonomous vehicle;

[0168] The second judgment module 19 is used to call the target speed limit area generation module to extend the target speed limit area outward from the starting boundary of the autonomous vehicle when the actual driving speed exceeds the preset speed threshold, so as to update and obtain a new target speed limit area after expansion.

[0169] In this solution, it is necessary to further differentiate between autonomous vehicles with different driving speeds. For vehicles within the set driving speed range, the target speed limit area is obtained uniformly using the speed limit area generation process described above. For vehicles whose driving speed exceeds the preset speed threshold, the unified speed limit area needs to be expanded to further ensure that speed limit control can continue in a timely and effective manner for different autonomous vehicles.

[0170] Example 3

[0171] This embodiment is an autonomous driving vehicle, which includes the driving control device in embodiment 2.

[0172] The autonomous vehicle in this solution integrates the driving control device described in the above embodiment. When the autonomous vehicle is about to turn at an intersection, it automatically detects all obstructions at that intersection that pose potential dangers and affect the vehicle's timely safety. For each obstruction, it extracts the lanes that affect the vehicle's movement and then determines whether to activate a preset speed limit strategy based on the specific circumstances of the obstacles in the lanes. If a necessary speed limit scenario is determined, the preset speed limit strategy is activated. This involves identifying a corresponding speed limit area at the intersection of the autonomous vehicle's lane and the target obstructed lane. Once the vehicle enters the speed limit area, it is preemptively speed-limited, proactively and timely reducing the vehicle's speed before blind spots appear. This provides sufficient reaction space and time for unexpected situations and provides advance judgment for the downstream speed planning module, reducing its planning difficulty and increasing the feasibility of successful planning. Ultimately, this reduces the collision risk of autonomous driving, effectively improving its safety and reliability, and enhancing the overall performance of the autonomous vehicle.

[0173] Example 4

[0174] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0175] Figure 11 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0176] like Figure 11 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0177] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0178] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as a driving control method for an autonomous vehicle. For example, in some embodiments, the driving control method for an autonomous vehicle can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the driving control method for an autonomous vehicle described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the driving control method for an autonomous vehicle by any other suitable means (e.g., by means of firmware).

[0179] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0180] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0181] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0182] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0183] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0184] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0185] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0186] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A driving control method for an autonomous vehicle, the driving control method comprising: Based on at least one of the following devices installed in the autonomous vehicle: lidar, position sensor, and speed sensor, the initial occlusion area corresponding to the autonomous vehicle at the intersection is obtained. The effective occlusion areas in the initial occlusion area are selected using preset filtering rules and used as target occlusion areas. Wherein, the target occlusion area of ​​the preset filtering rule meets at least one of the following conditions: The criteria include the area where the target occludes the lane, the distance between the nearest occlusion point and the lane reference line of the lane to be driven is less than the second set threshold, and the corresponding area is within the preset area range. Determine the target occluded lane within the target occlusion area and obtain the corresponding lane association information; Obtain obstacle association information of obstacles in the target occlusion area based on the lane association information; Based on the obstacle association information, it is determined whether the preset speed limit condition has been met. If it has been met, a target speed limit area is generated at the intersection of the lane to be driven by the autonomous vehicle and the target obstructed lane. Wherein, the starting boundary of the target speed-limited area near the autonomous vehicle is located at or outside the boundary of the target obstructed lane; When the autonomous vehicle enters the target speed-limited area, the autonomous driving is controlled to drive at the speed limit.

2. The driving control method for an autonomous vehicle as described in claim 1, wherein the step of determining whether a preset speed limit condition has been reached based on the obstacle association information includes: The center point of the corresponding obstacle is determined based on the obstacle association information; The nearest occlusion point corresponding to the target occlusion area is determined based on the occlusion area information of the target occlusion area; When the distance between the center point and the nearest occluded point is greater than a first preset threshold, the current driving scenario is determined to be a necessary speed limit scenario, and the preset speed limit condition is met.

3. The driving control method for an autonomous vehicle as described in claim 2, further comprising: When the distance between the center point and the nearest occlusion point is less than or equal to the first set threshold, it is determined that the current driving scenario belongs to a non-necessary speed limit scenario, and the set driving control strategy is used to control the driving state of the autonomous vehicle along the lane to be driven.

4. The driving control method for an autonomous vehicle as described in claim 1, wherein the step of determining whether a preset speed limit condition has been reached based on the obstacle association information includes: When the obstacle association information indicates that there is no obstacle within the target occlusion area, the current driving scenario is determined to be a necessary speed-limited scenario, and the preset speed limit condition is met.

5. The driving control method for an autonomous vehicle as described in any one of claims 1-4, wherein the step of generating a target speed limit area at the intersection of the lane to be driven by the autonomous vehicle and the target obstructed lane comprises: Obtain the lane reference line of the lane to be driven by the autonomous vehicle, and the center line of the target occluded lane; Obtain the intersection point of the lane reference line and the center line; Using the intersection point as a reference, a first set area is extended along the lane to be driven and toward the direction of the autonomous vehicle to obtain the target speed limit area; Wherein, the outer edge of the first set area is located at or outside the boundary of the target obstructed lane.

6. The driving control method for an autonomous vehicle as described in claim 5, wherein the step of extending a first predetermined area along the lane to be driven and in a direction closer to the autonomous vehicle, based on the intersection point, to obtain the target speed limit area, includes: Based on the intersection point, the first set area is extended along the lane to be driven and in a direction closer to the autonomous vehicle, and the second set area is extended along the lane to be driven and in a direction away from the autonomous vehicle. The common area of ​​the first set area and the second set area is taken as the target speed limit area.

7. The driving control method for an autonomous vehicle as described in claim 6, wherein the first set area is greater than or equal to the second set area.

8. The driving control method for an autonomous vehicle as described in claim 5, further comprising, before the step of controlling the autonomous driving at the speed limit when the autonomous vehicle enters the target speed-limited area: Obtain the actual driving speed of the autonomous vehicle; When the actual driving speed exceeds the preset speed threshold, the target speed limit area is extended outward from the starting boundary of the autonomous vehicle to update the new target speed limit area.

9. The driving control method for an autonomous vehicle as described in claim 1, wherein the step of determining the target occluded lane in the target occluded area and obtaining the corresponding lane association information includes: Based on a high-precision map, obtain several first lanes corresponding to the location of the target occlusion area; Select the lane closest to the lane obstruction from the plurality of first lanes as the target obstruction lane, and obtain the lane association information corresponding to the target obstruction lane; The step of obtaining obstacle association information of obstacles in the target occlusion area based on the lane association information includes: Based on the high-precision map and the lane association information, the obstacle association information of each obstacle in the target occluded lane is obtained.

10. A driving control device for an autonomous vehicle, the driving control device comprising: The target occlusion area acquisition module is used to acquire the target occlusion area corresponding to the autonomous vehicle at the intersection where it is driving; The target occlusion area acquisition module includes: The initial occlusion area acquisition unit is used to acquire the initial occlusion area of ​​the autonomous vehicle at the intersection based on at least one of the following devices: lidar, position sensor, and speed sensor installed in the autonomous vehicle. The target occlusion area acquisition unit is used to filter out the effective occlusion area in the initial occlusion area using a preset filtering rule, so as to use it as the target occlusion area; The target occlusion area of ​​the preset filtering rule meets at least one of the following conditions: The criteria include the area where the target occludes the lane, the distance between the nearest occlusion point and the lane reference line of the lane to be driven is less than the second set threshold, and the corresponding area is within the preset area range. The lane association information determination module is used to determine the target occluded lane in the target occluded area and obtain the corresponding lane association information; An obstacle association information acquisition module is used to acquire obstacle association information of obstacles in the target occlusion area based on the lane association information; The first judgment module is used to determine whether the preset speed limit condition has been met based on the obstacle association information. If it has been met, the target speed limit area generation module is called to generate a target speed limit area at the intersection of the lane to be driven by the autonomous vehicle and the target obstructed lane. Wherein, the starting boundary of the target speed-limited area near the autonomous vehicle is located at or outside the boundary of the target obstructed lane; The driving control module is used to control the autonomous driving vehicle to drive at the target speed limit area when the autonomous vehicle enters the target speed limit area.

11. The driving control device for an autonomous vehicle as described in claim 10, wherein the first determining module comprises: A center point determination unit is used to determine the center point of the corresponding obstacle based on the obstacle association information; The nearest occlusion point determination unit is used to determine the nearest occlusion point corresponding to the target occlusion area based on the occlusion area information of the target occlusion area; The first judgment unit is used to determine that the current driving scenario belongs to a necessary speed limit scenario and the preset speed limit condition is met when the distance between the center point and the nearest occlusion point is greater than a first preset threshold.

12. The driving control device for an autonomous vehicle as described in claim 11, wherein the first determining unit is further configured to determine that the current driving scenario belongs to a non-necessary speed limit scenario when the distance between the center point and the nearest occlusion point is less than or equal to the first set threshold, and to call the driving control module to control the autonomous vehicle to drive along the lane to be driven by adopting the set driving control strategy.

13. The driving control device for an autonomous vehicle as described in claim 10, wherein the first determining module comprises: The second judgment unit is used to determine that the current driving scenario is a necessary speed limit scenario and the preset speed limit condition is met when the obstacle association information indicates that there is no obstacle in the target occlusion area.

14. The driving control device for an autonomous vehicle as described in any one of claims 10-13, wherein the target speed limit area generation module comprises: A centerline acquisition unit is used to acquire the lane reference line of the lane to be driven by the autonomous vehicle, and the centerline of the target occluded lane. An intersection point acquisition unit is used to acquire the intersection point of the lane reference line and the center line; The target speed limit area generation unit is used to extend a first set area along the lane to be driven and toward the direction of the autonomous vehicle, with the intersection point as a reference, to obtain the target speed limit area. Wherein, the outer edge of the first set area is located at or outside the boundary of the target obstructed lane.

15. The driving control device for an autonomous vehicle as claimed in claim 14, wherein the target speed limit area generation unit is configured to extend the first set area along the lane to be driven and toward the autonomous vehicle, with the intersection point as a reference, and extend the second set area along the lane to be driven and away from the autonomous vehicle, and take the common area of ​​the first set area and the second set area as the target speed limit area.

16. The driving control device for an autonomous vehicle as described in claim 15, wherein the first set area is greater than or equal to the second set area.

17. The driving control device for an automated vehicle as described in claim 14, wherein the driving control device further comprises: The actual driving speed acquisition module is used to acquire the actual driving speed of the autonomous vehicle; The second judgment module is used to call the target speed limit area generation module to extend the target speed limit area outward from the starting boundary of the autonomous vehicle when the actual driving speed exceeds the preset speed threshold, so as to update and obtain the new target speed limit area after expansion.

18. The driving control device for an autonomous vehicle as described in claim 10, wherein the lane association information determination module comprises: The first lane acquisition unit is used to acquire several first lanes corresponding to the location of the target occlusion area based on a high-precision map. A target obstruction lane determination unit is used to select the lane closest to the lane obstruction from the plurality of first lanes as the target obstruction lane; A lane association information determination unit is used to obtain the lane association information corresponding to the target occluded lane; The obstacle association information acquisition module is also used to acquire the obstacle association information of each obstacle in the target occluded lane based on the high-precision map and the lane association information.

19. An autonomous vehicle, the autonomous vehicle comprising a driving control device for an autonomous vehicle as described in any one of claims 10-18.

20. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

21. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-9.

22. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-9.

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