Methods, devices, and equipment for station stop control based on autonomous vehicles

Abstract

This disclosure provides a method, apparatus, and device for station docking control based on autonomous vehicles, relating to the field of artificial intelligence, particularly to autonomous driving, autonomous parking, vehicle-to-everything (V2X) communication, intelligent cockpits, and intelligent transportation. The specific implementation scheme is as follows: Obtaining obstacle information corresponding to the current station on the driving path, wherein the obstacle information is information about obstacles within a preset geographical range corresponding to the current station; determining the optimal docking area based on the obstacle information corresponding to the current station and a preset set of docking areas; wherein the set of docking areas includes multiple adjacent docking areas, and the docking area is determined based on the vehicle information of the current autonomous vehicle and the lane line information corresponding to the current station; determining the docking path based on the optimal docking area and the current position of the current autonomous vehicle, and controlling the current autonomous vehicle to dock within the optimal docking area according to the docking path.

Description

Technical Field

[0001] This disclosure relates to fields such as autonomous driving, autonomous parking, vehicle-to-everything (V2X) communication, intelligent cockpits, and intelligent transportation in artificial intelligence, and particularly to a method, apparatus, and device for station parking control based on autonomous vehicles. Background Technology

[0002] With the development of autonomous driving technology, autonomous vehicles have already been applied in daily life. They can be used as public transportation to make people's lives more convenient.

[0003] In existing technologies, when autonomous vehicles are used as public transportation, they need to stop at stations so that passengers can board them.

[0004] However, in the above method, the stopping situation at the station is different every day. Each time the autonomous vehicle stops at the station, it needs to wait for the station to become available before driving to the station to stop. This method will cause the autonomous vehicle to spend a lot of time stopping at the station, resulting in passengers having to wait for a long time and causing a very poor passenger experience. Summary of the Invention

[0005] This disclosure provides a method, apparatus, and device for station docking control based on autonomous vehicles.

[0006] According to a first aspect of this disclosure, a station stopping control method based on an autonomous vehicle is provided, comprising:

[0007] Obtain obstacle information corresponding to the current station on the driving path, wherein the obstacle information is information about obstacles within a preset geographical range corresponding to the current station.

[0008] The optimal stopping area is determined based on the obstacle information corresponding to the current station and a preset set of stopping areas; wherein, the set of stopping areas includes multiple adjacent stopping areas, and the stopping area is determined based on the vehicle information of the current autonomous vehicle and the lane line information corresponding to the current station.

[0009] Based on the optimal parking area and the current position of the autonomous vehicle, a parking path is determined, and the autonomous vehicle is controlled to park in the optimal parking area according to the parking path.

[0010] According to a second aspect of this disclosure, a station stop control device based on an autonomous vehicle is provided, comprising:

[0011] The first acquisition unit is used to acquire obstacle information corresponding to the current station on the driving path, wherein the obstacle information is information about obstacles within a preset geographical range corresponding to the current station.

[0012] The first determining unit is configured to determine the optimal stopping area based on the obstacle information corresponding to the current station and a preset set of stopping areas; wherein the set of stopping areas includes multiple adjacent stopping areas, and the stopping area is determined based on the vehicle information of the current autonomous vehicle and the lane line information corresponding to the current station.

[0013] The second determining unit is used to determine the parking path based on the optimal parking area and the current position of the current autonomous vehicle.

[0014] The third determining unit is used to control the current autonomous vehicle to stop in the optimal stopping area according to the stopping path.

[0015] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] 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 described in the first aspect.

[0019] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect.

[0020] According to a fifth aspect of this disclosure, a computer program product is provided, the computer program product comprising: a computer program stored in a readable storage medium, wherein at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to perform the method described in the first aspect.

[0021] According to a sixth aspect of this disclosure, an autonomous vehicle is provided, comprising: an apparatus as described in a second aspect of this disclosure.

[0022] The technology disclosed herein solves the problem of passengers having to wait for a long time, resulting in a poor passenger experience.

[0023] 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

[0024] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0025] Figure 1 This is a scenario diagram illustrating the implementation of the station stop control method based on autonomous vehicles according to the embodiments of this disclosure.

[0026] Figure 2 This is a schematic diagram based on the first embodiment of the present disclosure;

[0027] Figure 3 This is a schematic diagram according to the second embodiment of the present disclosure;

[0028] Figure 4 This is another application scenario diagram of the station stop control method based on autonomous vehicles disclosed in this paper;

[0029] Figure 5 This is another application scenario diagram of the station stop control method based on autonomous vehicles disclosed in this paper;

[0030] Figure 6 This is a schematic diagram according to the third embodiment of the present disclosure;

[0031] Figure 7 This is a schematic diagram according to the third embodiment of the present disclosure;

[0032] Figure 8 This is a schematic diagram according to the fifth embodiment of the present disclosure;

[0033] Figure 9 This is a block diagram of an electronic device used to implement the station stop control method based on an autonomous vehicle according to the embodiments of this disclosure. Detailed Implementation

[0034] 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.

[0035] Currently, with the continuous development of autonomous driving technology, autonomous vehicles have gradually been applied to daily life. Furthermore, autonomous driving technology can also be applied to public transportation, such as autonomous buses.

[0036] In the existing technology, when the autonomous vehicle is a public transportation vehicle with autonomous driving technology, the autonomous vehicle needs to stop at a station where passengers can easily board or disembark. The station is the stopping location of the autonomous vehicle.

[0037] Figure 1 This is a scenario diagram illustrating the implementation of the station stop control method based on autonomous vehicles, as described in the embodiments of this disclosure. Figure 1 As shown in the diagram, the area includes station 101, road edge 102, autonomous vehicle 103, first road center line 104, first lane line 105, second road center line 106, and second lane line 107. When the autonomous vehicle 103 is used as a public transportation vehicle, it operates within the first lane line 101 and the road edge 102. When the autonomous vehicle 103 is about to approach station 101, it needs to stop at station 101 according to the stopping requirements to allow passengers to get on and off.

[0038] However, traffic conditions at bus stops vary daily. When an autonomous vehicle needs to stop at a stop, it must wait for sufficient space before finally stopping. In other words, if the stop is occupied by other vehicles, pedestrians, or other obstacles, the autonomous vehicle will wait until those obstacles have left the stop before stopping to allow passengers to board and alight.

[0039] In another possible scenario, when an autonomous vehicle needs to stop at a station, it may randomly select another stop to allow passengers to get on and off in order to avoid obstacles at the station. However, the randomly selected stop may be far away from the original station, resulting in a poor riding experience for passengers.

[0040] To avoid at least one of the aforementioned technical problems, the inventors of this disclosure have, through creative labor, arrived at the inventive concept of this disclosure: based on the vehicle information of the current autonomous vehicle and the lane line information corresponding to the station, multiple adjacent parking areas are determined, and the multiple adjacent parking areas are used as a preset parking area set. After selecting the optimal parking area based on the obstacle information corresponding to the current station and the preset parking area set, the parking path is determined by combining the optimal parking area and the current position of the current autonomous vehicle so that the vehicle can park in the optimal parking area.

[0041] Based on the above-mentioned inventive concept, this disclosure provides a method, device, and equipment for station parking control based on autonomous vehicles, which are applicable to fields such as autonomous driving, autonomous parking, vehicle-to-everything (V2X) communication, intelligent cockpit, and intelligent transportation in artificial intelligence, in order to enable autonomous vehicles to park in the optimal parking area and improve the passenger travel experience.

[0042] Figure 2 This is a schematic diagram based on the first embodiment of the present disclosure. The station stop control method based on autonomous vehicles according to the embodiment of the present disclosure includes:

[0043] S201. Obtain obstacle information corresponding to the current station on the driving path, wherein the obstacle information is the information of obstacles within a preset geographical range corresponding to the current station.

[0044] For example, the execution subject in this embodiment can be a station docking control device based on an autonomous vehicle. This station docking control device can be a server (such as a cloud server or a local server), a processor, or a chip, etc., and this embodiment is not limited thereto. This embodiment takes an electronic device as an example for description.

[0045] During the operation of an autonomous vehicle, the current station on the autonomous vehicle's route is the parking location where the autonomous vehicle needs to stop.

[0046] When an autonomous vehicle needs to stop at a current station, its electronic devices will acquire obstacle information within a preset geographical area of ​​the station. This obstacle information refers to data on other objects within the preset geographical area, such as data on vehicles traveling on the road. Vehicle data may include information such as vehicle size and location.

[0047] S202. Determine the optimal stopping area based on the obstacle information corresponding to the current station and the preset stopping area set; wherein, the stopping area set includes multiple adjacent stopping areas, and the stopping area is determined based on the vehicle information of the current autonomous vehicle and the lane line information corresponding to the current station.

[0048] For example, the vehicle information of the current autonomous vehicle refers to the data of the current autonomous vehicle itself, such as the vehicle size, speed during driving, etc. The aforementioned vehicle information of the current autonomous vehicle can be data pre-stored in the autonomous vehicle, or it can be data collected in real time by the sensors on the current autonomous vehicle during driving.

[0049] The lane information corresponding to the current station can include the shape of the lanes in the road where the current station is located (e.g., whether the current lane is straight or curved when turning), the distance between the lane and the station, and the distance between different lanes, etc. There are no specific restrictions here.

[0050] The preset set of stopping areas includes multiple adjacent stopping areas. Each stopping area is determined based on the vehicle information of the current autonomous vehicle and the lane line information corresponding to the current station. The area occupied by each stopping area is greater than or equal to the area occupied by the current autonomous vehicle, meaning each stopping area can accommodate the current autonomous vehicle's stopping needs. It should be noted that this preset set of stopping areas can be calculated in real-time by electronic devices when the autonomous vehicle needs to stop, or it can be a pre-stored set of stopping areas corresponding to the current station; no specific restriction is placed here. Furthermore, the stopping areas in the preset set can be set before the station, i.e., the area before the autonomous vehicle has driven to the horizontal line where the station is located, or after the station, i.e., the area after the autonomous vehicle has driven past the horizontal line where the station is located. The horizontal line where the station is located is the line perpendicular to the lane line direction and passes through the station.

[0051] When an electronic device determines an optimal parking area from a preset parking area set based on the obstacle information of the current station and the preset parking area set, the optimal parking area is the station that can accommodate the current autonomous vehicle and is closest to the current station.

[0052] In one example, the obstacle information acquired by the electronic device within a preset address range includes obstacle information within each of the preset parking areas in the current station's preset parking area set, as well as obstacle information outside the preset parking area set. For instance, if a portion of the area occupied by an obstacle is located in a parking area within the preset parking area set, and another portion is not located in any parking area within the preset parking area set, when determining whether the aforementioned parking area is the optimal parking area, it is necessary to consider not only the area occupied by the obstacle within that parking area, but also the area occupied by the obstacle not located in any parking area within the preset parking area set. This is to prevent the obstacle from completely entering the aforementioned parking area, making it unable to accommodate the current autonomous vehicle.

[0053] S203. Determine the parking route based on the optimal parking area and the current location of the autonomous vehicle.

[0054] For example, after the electronic device determines the optimal parking area, it determines the parking path of the current autonomous vehicle based on the current location of the autonomous vehicle and the location of the optimal parking area.

[0055] In one example, when the electronic device determines the stopping path, it can take the current position of the autonomous vehicle as the starting point and the optimal stopping area as the ending point, and combine the lane line information in the current driving path to plan multiple paths. The path with the optimal stopping area and the shortest time for the current autonomous vehicle can be selected as the stopping path.

[0056] S204. Control the current autonomous vehicle to stop in the optimal stopping area according to the stopping route.

[0057] For example, after determining the parking path in step S203, the electronic device controls the current autonomous vehicle to park in the optimal parking area according to the parking path.

[0058] In one example, while the electronic device is controlling the current autonomous vehicle to stop according to the determined stopping path, the electronic device can also adjust the previously determined stopping path in real time, thereby controlling the current autonomous vehicle to stop in the optimal stopping area.

[0059] In this embodiment, by using a preset set of parking areas composed of multiple parking areas determined based on the vehicle information of the current autonomous vehicle and the lane line information corresponding to the current station, and by obtaining the obstacle information corresponding to the current station, the optimal parking area is selected as the parking area of ​​the current autonomous vehicle. This avoids the problem in related technologies where the autonomous vehicle waits for space to become available at the station before parking, resulting in long parking times and a poor user experience.

[0060] Figure 3 This is a schematic diagram according to the second embodiment of the present disclosure. The station stop control method based on autonomous vehicles according to the embodiment of the present disclosure includes:

[0061] S301, Get the set of docking areas.

[0062] For example, the execution subject in this embodiment can be a station docking control device based on an autonomous vehicle. This station docking control device can be a server (such as a cloud server or a local server), a processor, or a chip, etc., and this embodiment is not limited thereto. This embodiment takes an electronic device as an example for description.

[0063] During the operation of an autonomous vehicle, electronic devices acquire a set of stopping areas along the vehicle's current path.

[0064] In one example, step S301 includes the following implementation methods:

[0065] In the first implementation of step S301, the lane line information corresponding to each station on the driving path is obtained, wherein the lane line information includes the lane line position of the lane line adjacent to the station; and the station category corresponding to each station on the driving path is obtained; and the set of parking areas corresponding to the station is determined based on the lane line information, the station category, and the vehicle footprint of the current autonomous vehicle; wherein the parking area in the set of parking areas is greater than or equal to the vehicle footprint of the current autonomous vehicle.

[0066] For example, a station category is used to indicate the shape of the road edge line corresponding to the current station. For instance, station categories can be divided into straight-ahead stations and bay-type stations.

[0067] In this embodiment, the vehicle footprint is shown in the vehicle information of the current autonomous vehicle.

[0068] For each station on the driving path, the electronic equipment will determine the set of stopping areas corresponding to the station based on the lane line position of the lane line adjacent to the station, the station type, and the vehicle footprint of the current autonomous vehicle. Furthermore, the stopping area corresponding to each stopping area in the set of stopping areas must meet the vehicle footprint of the current autonomous vehicle.

[0069] When electronic devices acquire lane line information, they can determine it by combining road network equipment or by using map information stored in the current electronic device.

[0070] The method for determining the parking area set in this embodiment avoids the problem of the autonomous vehicle being unable to park in the set if the parking areas in the set do not match the vehicle's footprint. Furthermore, this embodiment also considers station type when determining the parking area set to ensure that this method is applicable to different station types.

[0071] The phrase "determine the set of parking areas corresponding to the stations based on lane line information, station type, and the vehicle footprint of the current autonomous vehicle" includes: determining the parking areas including the stations based on lane line information, station type, and the vehicle footprint of the current autonomous vehicle; and determining the areas adjacent to the parking areas including the stations as other parking areas based on lane line information and the vehicle footprint of the current autonomous vehicle, thus obtaining the set of parking areas.

[0072] For example, in this embodiment, when determining the set of parking areas corresponding to a station, the first consideration is to determine the parking areas that include the station. When determining the parking areas that include the station, the station type, the area occupied by the current autonomous vehicle, and lane information are used to determine parking areas that include the station and can accommodate the current autonomous vehicle. After determining the parking areas that include the station, based on lane information and the vehicle area of ​​the current autonomous vehicle, areas adjacent to the parking areas that include the station are further determined as other parking areas. Then, the parking areas that include the station and other parking areas are used as the set of parking areas when the current autonomous vehicle needs to park at the current station. In other words, when determining other parking areas, they are determined based on the determined parking areas that include the station.

[0073] For example, when a stop area containing a station is identified, other stop areas can be selected in the adjacent lane or the same lane where the stop area contains the station.

[0074] In this embodiment, when determining the set of parking areas, a parking area containing a station is first determined based on the station type, lane line information, and the current area occupied by the autonomous vehicle. Then, based on the lane line information and the current area occupied by the autonomous vehicle, other parking areas are determined in the areas adjacent to the parking area containing the station. Thus, through the above method of determining the set of parking areas, the current autonomous vehicle can finally park at the station or in an area near the station, improving the passenger riding experience.

[0075] The second implementation of step S301 involves receiving a set of parking areas sent by a remote device; wherein the set of parking areas is determined based on the lane line information corresponding to each station on the driving path, the station category corresponding to each station on the driving path, and the vehicle footprint in the vehicle information of the current autonomous vehicle.

[0076] For example, in this embodiment, a set of stopping areas for the current autonomous vehicle along its driving path is pre-stored in a remote device. This driving path includes multiple stops. The set of stopping areas for each stop is determined based on the stop category and the vehicle's footprint as shown in the vehicle information.

[0077] When an autonomous vehicle needs to stop at a station, the electronic device can request the set of stopping areas corresponding to that station from a remote device. The electronic device will then receive the set of stopping areas for that station returned by the remote device. Furthermore, if the autonomous vehicle leaves the station, the electronic device will simultaneously delete the set of stopping areas corresponding to that station.

[0078] In this embodiment, when the electronic device obtains the set of docking areas corresponding to each station, it can directly receive the set of docking areas sent by the electronic device, thereby avoiding occupying the computing resources of the electronic device.

[0079] S302. Obtain obstacle information corresponding to the current station on the driving path, wherein the obstacle information is the information of obstacles within a preset geographical range corresponding to the current station.

[0080] In one example, step S302 includes the following implementation methods:

[0081] The first implementation of step S302 involves obtaining obstacle information corresponding to the current station on the driving path using millimeter-wave radar on the current autonomous vehicle.

[0082] In the second implementation of step S302, the obstacle information corresponding to the current station on the driving path is received from the acquisition device located at the current station.

[0083] In this embodiment, when acquiring obstacle information corresponding to the current station, in the first implementation, a millimeter-wave radar is installed on the current autonomous vehicle. The electronic device can determine the obstacle information corresponding to the current station on the driving path by acquiring the data sensed in real time by the millimeter-wave radar. Alternatively, the obstacle information can be collected by a data acquisition device installed at the platform corresponding to the station. After the electronic device requests the obstacle information from the data acquisition device, the data acquisition device transmits it to the electronic device in real time.

[0084] S303. The parking areas in the parking area set have priorities; the obstacle information includes the data information of obstacles in the parking area with the i-th priority in the parking area set; repeat steps S304-S305 until the optimal parking area is determined, where i is a positive integer greater than or equal to 1, and the initial value of i is 1.

[0085] The set of stopping areas includes multiple adjacent stopping areas, which are determined based on the vehicle information of the current autonomous vehicle and the lane line information corresponding to the current station.

[0086] For example, the priority of the stop area refers to the order in which the stop areas in the current station's set of stop areas are judged to determine whether they are the optimal stop areas when determining the optimal stop area.

[0087] In one example, the center point of the stop area in the stop area set is located on the line along a preset direction, which is the opposite direction of the driving direction of the driving path.

[0088] For example, in this example, when determining the stop areas in the set of stop areas, the center point of the stop area is located on the line in the opposite direction of the current autonomous vehicle's travel path. That is, in the same coordinate system, the coordinate value of the center point of any stop area in the set of stop areas in the current autonomous vehicle's travel direction lies between the coordinate values ​​of the station in the travel direction and the coordinate values ​​of the current autonomous vehicle in the travel direction. Furthermore, this avoids situations where the coordinate value of the center point of the stop area in the current autonomous vehicle's travel direction exceeds the interval formed by the coordinate values ​​of the station in the travel direction and the coordinate values ​​of the current autonomous vehicle in the travel direction. In such cases, the current autonomous vehicle would pass by the station during the stop process and continue traveling a considerable distance before stopping. Passengers needing to board the autonomous vehicle at that station might mistakenly believe that the autonomous vehicle does not stop at that station and miss their ride, resulting in a poor user experience.

[0089] S304. If, based on the obstacle data of the i-th priority parking area and the vehicle footprint of the current autonomous vehicle, it is determined that the current autonomous vehicle can park in the i-th priority parking area, then the i-th priority parking area is determined to be the optimal parking area.

[0090] For example, the obstacle information may include data such as the shape and volume of the obstacle.

[0091] When parking areas have priorities, determining the optimal parking area first checks if the current autonomous vehicle can park in the highest-priority area. In this embodiment, a smaller value of i indicates higher priority. Furthermore, determining whether the autonomous vehicle can park also considers obstacle data within the currently assessed parking area and the vehicle's footprint to determine if it can fit within the area. In one possible scenario, the ability to meet requirements such as vehicle movement and turning when entering or exiting the station can also be considered. In one example, parking areas closer to the station have higher priority.

[0092] In one example, step S304, which involves "determining whether the current autonomous vehicle can park in the parking area of ​​the i-th priority level based on the obstacle data information in the parking area of ​​the i-th priority level and the vehicle footprint of the current autonomous vehicle," can be implemented in the following way:

[0093] The first implementation of step S304: The data information includes the area occupied by obstacles; the remaining area of ​​the parking area of ​​the i-th priority level is determined based on the area of ​​the parking area of ​​the i-th priority level and the area occupied by obstacles in the parking area of ​​the i-th priority level; if the remaining area of ​​the parking area of ​​the i-th priority level is greater than or equal to the vehicle area occupied by the current autonomous vehicle, then it is determined that the current autonomous vehicle can park in the parking area of ​​the i-th priority level.

[0094] For example, when determining whether the current autonomous vehicle can park in a parking area of ​​priority level i, the difference between the area of ​​the parking area of ​​priority level i and the area occupied by obstacles within the parking area of ​​priority level i can be calculated to obtain the remaining area of ​​the parking area of ​​priority level i, i.e., the area not occupied by obstacles. Then, by comparing the size of the remaining unoccupied area of ​​the parking area of ​​priority level i with the area occupied by the current autonomous vehicle, it is determined whether parking is possible in the parking area of ​​priority level i. If the remaining area of ​​the parking area of ​​priority level i is greater than or equal to the vehicle's footprint, then the parking area is considered suitable for parking; otherwise, the parking area is considered unsuitable for parking.

[0095] In this embodiment, if the remaining area of ​​the unobstructed footprint in the parking area is greater than or equal to the footprint of the current autonomous vehicle, then the current autonomous vehicle is determined to be able to park. The method provided in this embodiment is easy to implement and requires relatively few computing resources.

[0096] The second implementation method in step S304: The data information includes the obstacle's footprint and the obstacle's moving speed; based on the area of ​​the parking area of ​​the i-th priority level and the obstacle's footprint within the parking area of ​​the i-th priority level, the remaining area of ​​the parking area of ​​the i-th priority level is determined; if the remaining area of ​​the parking area of ​​the i-th priority level is determined to be less than the vehicle footprint of the current autonomous vehicle, the moving distance of the obstacle within a preset time is determined based on the obstacle's moving speed; based on the moving distance and the remaining area of ​​the parking area of ​​the i-th priority level, the available area after the preset time is determined; if the available area is determined to be greater than or equal to the vehicle footprint of the current autonomous vehicle, it is determined that the current autonomous vehicle can park in the parking area of ​​the i-th priority level.

[0097] For example, in this embodiment, the obstacle information includes the area occupied by the obstacle and the speed at which the obstacle moves.

[0098] After determining the remaining area in the parking area, if the remaining area is less than the footprint of the current autonomous vehicle, this embodiment further considers the obstacle's movement speed. In this embodiment, the electronic device determines the obstacle's movement distance within a preset time based on its movement speed. Furthermore, after determining the obstacle's movement distance, the electronic device can also determine the available area in the parking area after the preset time, based on the obstacle's movement distance and the remaining area. Then, if the electronic device determines that the available area in the parking area is greater than or equal to the footprint of the current autonomous vehicle, it can determine that the current autonomous vehicle can park in that area. If the available area in the parking area is less than or equal to the footprint of the current autonomous vehicle, it continues to determine a parking area with a lower priority level.

[0099] In one example, if all stopping areas are determined to be non-stopping areas, the electronic device can notify the administrator or driver of the autonomous vehicle to manually control the current autonomous vehicle to stop.

[0100] For example, if an obstacle is moving out of the parking area, after determining the obstacle's speed, the current speed can be multiplied by a preset time value to obtain the distance traveled within the preset time. Then, based on the travel distance and the area the obstacle currently occupies within the parking area, the free area that will be vacated after the preset time is determined. This free area represents the difference between the area the obstacle currently occupies in the parking area and the area it will occupy after the preset time. Finally, summing the free area and the remaining area yields the remaining area of ​​the parking area after the preset time.

[0101] In one possible scenario, when determining the movement speed, it's also possible to determine whether the obstacle is currently moving into or leaving the parking area. If the obstacle is entering the parking area, and the remaining area in the current parking area is less than the vehicle's footprint, the electronic equipment can directly determine that the parking area is unsuitable for parking. If the obstacle is leaving the parking area, the electronic equipment can determine the movement distance within a preset time based on the movement speed. That is, it can determine whether it's necessary to determine the obstacle's movement distance and the remaining area of ​​the parking area based on the obstacle's movement direction.

[0102] In this embodiment, when determining whether a parking area is suitable for parking, if the remaining area in the parking area is less than the current footprint of the autonomous vehicle, the movement speed of obstacles in the parking area is further considered. Based on the obstacle's movement speed, the distance the obstacle will travel within a preset time is determined. Then, based on the travel distance, the remaining area in the parking area, and the current footprint of the autonomous vehicle, the remaining area in the parking area after the preset time is determined. Based on the remaining area and the current footprint of the autonomous vehicle, it is further determined whether parking is possible. Therefore, by considering the movement of obstacles in the parking area, the optimal parking area selection is more accurate, improving the user experience.

[0103] The following implementation method can be used when obtaining the moving speed of obstacles:

[0104] The first implementation method is to obtain the obstacle category of the obstacle in the docking area of ​​the i-th priority level; and determine the obstacle movement speed in the data information according to the preset correspondence between obstacle category and obstacle movement speed.

[0105] In this embodiment, for example, when the obstacle category can indicate different vehicle models, a correspondence between different vehicle models and their movement speeds can be established. For instance, the movement speeds of different vehicle models can satisfy the following conditions: a double-decker bus moves slower than a single-decker bus, and a multi-car bus moves slower than a single-car bus. Furthermore, the obstacle category can also indicate pedestrians, which can be further categorized into elderly people, young people, children, etc.

[0106] In one example, when determining the obstacle category, the obstacle category can be further determined based on the acquired image information of the obstacle.

[0107] In this embodiment, since different types of obstacles move at different speeds, the moving speed can be determined directly based on the correspondence between obstacle type and moving speed, thereby improving the efficiency of obtaining the optimal docking area.

[0108] The second implementation method is to obtain the road condition information corresponding to the current station and obtain the obstacle category of the obstacle in the i-th priority parking area; and determine the obstacle movement speed in the data information according to the preset correspondence between the road condition information, the obstacle category and the obstacle movement speed.

[0109] In this embodiment, the corresponding relationship between the current station's road condition information, obstacle type, and obstacle movement speed can be combined to make the determined obstacle movement speed more consistent with the real driving scenario and the obtained optimal stopping area more accurate.

[0110] S305. If, based on the data information of obstacles in the parking area of ​​the i-th priority level and the vehicle footprint of the current autonomous vehicle, it is determined that the current autonomous vehicle cannot park in the parking area of ​​the i-th priority level, then i is incremented by 1.

[0111] For example, if, when judging a parking area of ​​priority level i, it is determined that parking is not allowed based on the data information of obstacles in the parking area and the current vehicle footprint of the autonomous vehicle, then the judgment is continued to the parking area of ​​priority level i+1.

[0112] In this embodiment, the system determines whether the autonomous vehicle can park in a given parking area based on its priority from highest to lowest. If parking is possible, that area is designated as the optimal parking area, eliminating the need to evaluate other areas and improving efficiency. For each parking area, the system can determine whether the vehicle can park there based on data within the area and the vehicle's footprint. This method is simple, easy to implement, and time-efficient.

[0113] S306. Obtain traffic information corresponding to the current station.

[0114] For example, after the electronic device obtains the optimal parking area, in order to determine the parking path of the current autonomous vehicle, it is first necessary to obtain the traffic information corresponding to the current station. This traffic information is used to characterize relevant road traffic data at the current station, such as vehicle information in each lane, lane congestion information, and traffic light information.

[0115] S307. Starting from the current location of the autonomous vehicle and ending at the optimal stopping area, generate a stopping route based on the road condition information corresponding to the current station.

[0116] For example, when generating a parking route, the current location of the current autonomous vehicle is used as the starting point and the optimal parking area is used as the ending point. The parking route of the current autonomous vehicle is automatically planned by combining the obtained road condition information of the current station where the current autonomous vehicle needs to park, so that the current autonomous vehicle can park in the determined optimal parking area.

[0117] In this embodiment, the road condition information of the current station is obtained, and the stopping path of the autonomous vehicle is determined by combining the road condition information, so as to improve the effectiveness of the stopping control of the current autonomous vehicle.

[0118] S308: Control the current autonomous vehicle to stop in the optimal stopping area according to the stopping route.

[0119] For example, this step can refer to step S104 above, and will not be repeated here.

[0120] In this embodiment, the system determines which parking area the current autonomous vehicle can park in based on its priority from highest to lowest. If parking is possible, this area is identified as the optimal parking area, eliminating the need to evaluate other areas and improving efficiency. Furthermore, if the remaining area of ​​a parking area is less than the vehicle's footprint, the system calculates the obstacle's movement distance within a preset time based on its speed. Then, based on the movement distance and the remaining area of ​​the i-th priority parking area, the system determines the available area after the preset time. If the available area is greater than or equal to the vehicle's footprint, the system determines that the current autonomous vehicle can park in the i-th priority parking area. This improves the accuracy of determining the optimal parking area and enhances the user's riding experience.

[0121] For example, Figure 4 This is another application scenario diagram of the station stopping control method based on autonomous vehicles disclosed in this publication. For example... Figure 4 As shown in the diagram, the area includes: first lane line 401 (represented by a dashed line), second lane line 402, third lane line 403, first stop area 404, second stop area 405, third stop area 406, stop 407, and stop bay boundary 408 (represented by a solid line), and first lane 409 (the area between first lane line 401 and second lane line 402). When determining the set of stop areas corresponding to stop 407, the largest rectangular area including stop 407 within the first lane line 401 and bay boundary 408 is first stop area 404, and this first stop area 404 has the highest priority. The remaining stop areas can be determined within the first lane 401 near the first stop area 404. For example, in the diagram, the upper boundary 4041 and lower boundary 4042 of the first parking area 404 are extended into the first lane 409 to obtain the second parking area 405, which has a priority of 2nd priority. Then, a third parking area 406 is also set within the first lane 409, located in the opposite direction of the second parking area 405 along the vehicle's travel direction, and has a priority of 3rd priority. When an autonomous vehicle needs to park... Figure 4 When the vehicle reaches station 407 as shown, it will first determine whether it can stop in the first stop area 404. If not, it will continue to determine whether it can stop in the second stop area 405. And so on. If it is determined that the third stop area 406 is also not a stop, then the autonomous vehicle will be manually controlled to stop.

[0122] For example, Figure 5 This is another application scenario diagram of the station stop control method based on autonomous vehicles disclosed in this paper. For example... Figure 5 As shown in the diagram, the area includes: road edge line 501, first lane line 502, second lane line 503, first stop area 504, second stop area 505, third stop area 506, stop 507, first lane 508 (the area between first lane line 502 and road edge line 501 in the diagram), and second lane 509 (the area between first lane line 502 and second lane line 503 in the diagram). The first stop area 504 is formed by extending the same distance from the center of stop 507 in both the direction of vehicle travel and the opposite direction of vehicle travel, and has the highest priority (1st priority). The second stop area 505 is formed by extending the upper boundary 5041 and lower boundary 5042 of the first stop area to the second lane line 503, and has the highest priority (2nd priority). The third stop area 506 is obtained by moving the lower boundary 5042 of the first stop area 505 a certain distance in the opposite direction of vehicle travel within the first lane 508, and the third stop area 506 has the third priority. In one possible case, the distance between the upper boundary 5041 and the lower boundary 5042 of the first stop area 505 is 30 meters. The length of the third stop area 507 in the direction of vehicle travel is 20 meters. In this example, it can be seen that when determining the stop areas, the center point of the stop areas in the set of stop areas is located on the line in the opposite direction of the travel path.

[0123] Figure 6 This is a schematic diagram based on the third embodiment of the present disclosure, as shown below. Figure 6 As shown, the station stop control device based on autonomous vehicles according to an embodiment of this disclosure includes:

[0124] The first acquisition unit 601 is used to acquire obstacle information corresponding to the current station on the driving path, wherein the obstacle information is the information of obstacles within a preset geographical range corresponding to the current station.

[0125] The first determining unit 602 is used to determine the optimal stopping area based on the obstacle information corresponding to the current station and a preset set of stopping areas; wherein, the set of stopping areas includes multiple adjacent stopping areas, and the stopping area is determined based on the vehicle information of the current autonomous vehicle and the lane line information corresponding to the current station.

[0126] The second determining unit 603 is used to determine the parking path based on the optimal parking area and the current position of the current autonomous vehicle.

[0127] The third determining unit 604 is used to control the current autonomous vehicle to stop in the optimal stopping area according to the stopping path.

[0128] For example, the apparatus of this embodiment can execute the technical solutions in the above method, and its specific implementation process and technical principles are the same, which will not be repeated here.

[0129] Figure 7 This is a schematic diagram based on the third embodiment of the present disclosure, as shown below. Figure 7 As shown, the station stop control device based on autonomous vehicles according to an embodiment of this disclosure includes:

[0130] The first acquisition unit 701 is used to acquire obstacle information corresponding to the current station on the driving path, wherein the obstacle information is the information of obstacles within a preset geographical range corresponding to the current station.

[0131] The first determining unit 702 is used to determine the optimal stopping area based on the obstacle information corresponding to the current station and a preset set of stopping areas; wherein, the set of stopping areas includes multiple adjacent stopping areas, and the stopping area is determined based on the vehicle information of the current autonomous vehicle and the lane line information corresponding to the current station.

[0132] The second determining unit 703 is used to determine the parking path based on the optimal parking area and the current position of the current autonomous vehicle.

[0133] The third determining unit 704 is used to control the current autonomous vehicle to stop in the optimal stopping area according to the stopping path.

[0134] In one example, the center point of the stop area in the stop area set is located on the line along a preset direction, which is the opposite direction of the driving direction of the driving path.

[0135] In one example, the parking areas in the set of parking areas have priorities; the obstacle information includes data information of obstacles in the i-th priority parking area in the set of parking areas; the first determining unit 702 includes:

[0136] Execution module 7021 is used to repeat the following modules until the optimal docking area is determined, where i is a positive integer greater than or equal to 1, and the initial value of i is 1.

[0137] The first determining module 7022 is used to determine, based on the obstacle data information in the parking area of ​​the i-th priority level and the vehicle footprint of the current autonomous vehicle, whether the current autonomous vehicle can park in the parking area of ​​the i-th priority level.

[0138] The second determining module 7023 is used to determine the docking area of ​​the i-th priority level, which is the optimal docking area.

[0139] The third determining module 7024 is used to determine, based on the data information of obstacles in the parking area of ​​the i-th priority level and the vehicle footprint of the current autonomous vehicle, that the current autonomous vehicle cannot park in the parking area of ​​the i-th priority level.

[0140] The fourth determination module 7025 is used to determine i incremented by 1.

[0141] In one example, the data information includes the area occupied by the obstacle; the first determining module 7022 includes:

[0142] The first determining submodule 70221 is used to determine the remaining area of ​​the parking area of ​​the i-th priority based on the area of ​​the parking area of ​​the i-th priority and the obstacle area occupied by the obstacles in the parking area of ​​the i-th priority.

[0143] The second determining submodule 70222 is used to determine that the current autonomous vehicle can park in the parking area of ​​the i-th priority if the remaining area of ​​the parking area of ​​the i-th priority is greater than or equal to the vehicle footprint of the current autonomous vehicle.

[0144] In one example, the data information includes the obstacle's footprint and its moving speed; the first determining module 7022 includes:

[0145] The third determining submodule 70223 is used to determine the remaining area of ​​the parking area of ​​the i-th priority based on the area of ​​the parking area of ​​the i-th priority and the obstacle area occupied by the obstacles in the parking area of ​​the i-th priority.

[0146] The fourth determining submodule 70224 is used to determine the distance the obstacle moves within a preset time based on the obstacle's moving speed if the remaining area of ​​the parking area with the i-th priority is less than the vehicle footprint of the current autonomous vehicle.

[0147] The fifth determining submodule 70225 is used to determine the available area after a preset time based on the travel distance and the remaining area of ​​the docking area with the i-th priority.

[0148] The sixth determination submodule 70226 is used to determine if the available area is greater than or equal to the vehicle footprint of the current autonomous vehicle, and then determine that the current autonomous vehicle can park in the parking area of ​​the i-th priority level.

[0149] In one example, the apparatus provided in this embodiment further includes:

[0150] The second acquisition unit 705 is used to acquire the obstacle category of obstacles in the docking area of ​​the i-th priority level.

[0151] The fourth determining unit 706 is used to determine the obstacle movement speed in the data information based on the preset correspondence between obstacle type and obstacle movement speed.

[0152] In one example, the apparatus provided in this embodiment further includes:

[0153] The third acquisition unit 707 is used to acquire the traffic information corresponding to the current station.

[0154] The fourth acquisition unit 708 is used to acquire the obstacle category of obstacles in the parking area of ​​the i-th priority level.

[0155] The fifth determining unit 709 is used to determine the obstacle movement speed in the data information based on the preset correspondence between road condition information, obstacle type and obstacle movement speed.

[0156] In one example, the apparatus provided in this embodiment further includes:

[0157] The fifth acquisition unit 710 is used to acquire lane line information corresponding to each station on the driving path, wherein the lane line information includes the lane line position of the lane line adjacent to the station.

[0158] The sixth acquisition unit 711 is used to acquire the station category corresponding to each station on the driving route.

[0159] The sixth determining unit 712 is used to determine the set of parking areas corresponding to the station based on lane line information, station type, and the vehicle footprint of the current autonomous vehicle; wherein the parking area in the set of parking areas is greater than or equal to the vehicle footprint of the current autonomous vehicle.

[0160] In one example, the sixth determining unit 712 includes:

[0161] The fifth determining module 7121 is used to determine the parking area, including the station, based on lane line information, station type, and the vehicle footprint in the current autonomous vehicle's vehicle information.

[0162] The sixth determining module 7122 is used to determine the areas adjacent to the parking areas, including the station, based on the lane line information and the vehicle area in the current vehicle information of the autonomous vehicle, so as to obtain a set of parking areas.

[0163] In one example, the apparatus provided in this embodiment further includes:

[0164] The receiving unit 713 is used to receive the set of docking areas sent by the remote device.

[0165] The set of stopping areas is determined based on the lane line information corresponding to each station on the driving path, the station category corresponding to each station on the driving path, and the vehicle footprint in the vehicle information of the current autonomous vehicle.

[0166] In one example, the second determining unit 703 includes:

[0167] The first acquisition module 7031 is used to acquire the traffic information corresponding to the current station.

[0168] The generation module 7032 is used to generate a stopping path based on the current location of the current autonomous vehicle as the starting point and the optimal stopping area as the ending point, according to the road condition information corresponding to the current station.

[0169] In one example, the first acquisition unit 701 includes:

[0170] The second acquisition module 7011 is used to acquire obstacle information corresponding to the current station on the driving path through the millimeter-wave radar on the current autonomous vehicle.

[0171] Alternatively, the receiving module 7012 is used to receive obstacle information corresponding to the current station on the travel path from the acquisition device located at the current station.

[0172] For example, the apparatus of this embodiment can execute the technical solutions in the above method, and its specific implementation process and technical principles are the same, which will not be repeated here.

[0173] Figure 8 This is a schematic diagram based on the fifth embodiment of the present disclosure, as shown below. Figure 8 As shown, the electronic device 800 in this disclosure may include a processor 801 and a memory 802.

[0174] Memory 802 is used to store programs. Memory 802 may include volatile memory, such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random-access memory (DDR SDRAM), etc.; memory may also include non-volatile memory, such as flash memory. Memory 802 is used to store computer programs (such as application programs, functional modules, etc. that implement the above methods), computer instructions, etc. The computer programs, computer instructions, etc., can be partitioned and stored in one or more memories 802. Furthermore, the computer programs, computer instructions, data, etc., can be accessed by processor 801.

[0175] The aforementioned computer programs and instructions can be stored in one or more partitions of memory 802. Furthermore, the aforementioned computer programs and instructions can be invoked by processor 801.

[0176] The processor 801 is configured to execute the computer program stored in the memory 802 to implement the various steps in the methods described in the above embodiments.

[0177] For details, please refer to the relevant descriptions in the preceding method embodiments.

[0178] The processor 801 and the memory 802 can be independent structures or integrated structures. When the processor 801 and the memory 802 are independent structures, the memory 802 and the processor 801 can be coupled together via bus 803.

[0179] The electronic device in this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principle are the same, and will not be repeated here.

[0180] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0181] According to embodiments of this disclosure, this disclosure also provides a computer program product comprising: a computer program stored in a readable storage medium, at least one processor of an electronic device being able to read the computer program from the readable storage medium, and the at least one processor executing the computer program causing the electronic device to perform the scheme provided in any of the above embodiments.

[0182] Figure 9 A schematic block diagram of an example electronic device 900 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.

[0183] like Figure 9 As shown, device 900 includes a computing unit 901, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 902 or a computer program loaded from storage unit 908 into random access memory (RAM) 903. RAM 903 may also store various programs and data required for the operation of device 900. The computing unit 901, ROM 902, and RAM 903 are interconnected via bus 904. Input / output (I / O) interface 905 is also connected to bus 904.

[0184] Multiple components in device 900 are connected to I / O interface 905, including: input unit 906, such as keyboard, mouse, etc.; output unit 907, such as various types of monitors, speakers, etc.; storage unit 908, such as disk, optical disk, etc.; and communication unit 909, such as network card, modem, wireless transceiver, etc. Communication unit 909 allows device 900 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0185] The computing unit 901 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 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 901 performs the various methods and processes described above, such as a site-stopping control method based on an autonomous vehicle. For example, in some embodiments, the site-stopping control method based on an autonomous vehicle can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed on device 900 via ROM 902 and / or communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of the site-stopping control method based on an autonomous vehicle described above can be performed. Alternatively, in other embodiments, the computing unit 901 may be configured by any other suitable means (e.g., by means of firmware) to execute a method for station docking control based on autonomous vehicles.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0192] This disclosure provides an autonomous vehicle, including: a station stop control device based on an autonomous vehicle as described in any embodiment of this disclosure.

[0193] 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.

[0194] 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 station stopping control method based on an autonomous vehicle, comprising: obtaining obstacle information corresponding to a current station on a driving path, wherein the obstacle information is information of obstacles within a preset geographical range corresponding to the current station; determining an optimal stopping area according to the obstacle information corresponding to the current station and a preset stopping area set, wherein the stopping area set comprises a plurality of adjacent stopping areas, the stopping area is determined based on vehicle information of a current autonomous vehicle and lane line information corresponding to the current station, and in a same coordinate system, a coordinate value of a center point of any stopping area in the stopping area set in a driving direction of the current autonomous vehicle is between a coordinate value of the station in the driving direction and a coordinate value of the current autonomous vehicle in the driving direction; determining a stopping path according to the optimal stopping area and a current position of the current autonomous vehicle, and controlling the current autonomous vehicle to stop in the optimal stopping area according to the stopping path; the stopping areas in the stopping area set have priorities, and the obstacle information comprises data information of obstacles in an i-th priority stopping area in the stopping area set; determining the optimal stopping area according to the obstacle information corresponding to the current station and the preset stopping area set comprises: repeating the following steps until the optimal stopping area is determined, wherein i is a positive integer greater than or equal to 1, and an initial value of i is 1: if it is determined that the current autonomous vehicle can stop in the i-th priority stopping area according to the data information of the obstacles in the i-th priority stopping area and a vehicle footprint of the current autonomous vehicle, the i-th priority stopping area is determined as the optimal stopping area; if it is determined that the current autonomous vehicle cannot stop in the i-th priority stopping area according to the data information of the obstacles in the i-th priority stopping area and the vehicle footprint of the current autonomous vehicle, i is determined to be incremented by 1.

2. The method of claim 1, wherein, the data information comprises an obstacle footprint, and it is determined that the current autonomous vehicle can stop in the i-th priority stopping area according to the data information of the obstacles in the i-th priority stopping area and the vehicle footprint of the current autonomous vehicle comprises: determining a residual area of the i-th priority stopping area according to an area of the i-th priority stopping area and the obstacle footprint of the obstacles in the i-th priority stopping area; if it is determined that the residual area of the i-th priority stopping area is greater than or equal to the vehicle footprint of the current autonomous vehicle, it is determined that the current autonomous vehicle can stop in the i-th priority stopping area.

3. The method of claim 1, wherein, The data information comprises an obstacle area and an obstacle moving speed; and according to the data information of the obstacle in the i-th priority parking area and a vehicle area of the current autonomous vehicle, it is determined that the current autonomous vehicle can park in the i-th priority parking area, comprising: According to the area of the i-th priority parking area and the obstacle area of the obstacle in the i-th priority parking area, the remaining area of the i-th priority parking area is determined; If it is determined that the remaining area of the i-th priority parking area is less than the vehicle area of the current autonomous vehicle, then according to the obstacle moving speed, the moving distance of the obstacle within a preset time is determined; According to the moving distance and the remaining area of the i-th priority parking area, the empty area after the preset time is determined; if it is determined that the empty area is greater than or equal to the vehicle area of the current autonomous vehicle, it is determined that the current autonomous vehicle can park in the i-th priority parking area.

4. The method of claim 3, further comprising: obtaining an obstacle category of the obstacle in the i-th priority parking area; According to the corresponding relationship between the preset obstacle category and the obstacle moving speed, the obstacle moving speed in the data information is determined.

5. The method of claim 3, further comprising: obtaining road condition information corresponding to the current site and obtaining an obstacle category of the obstacle in the i-th priority parking area; According to the corresponding relationship between the preset road condition information, obstacle category and obstacle moving speed, the obstacle moving speed in the data information is determined.

6. The method of any one of claims 1-5, further comprising: obtaining lane line information corresponding to each site on the driving path, wherein the lane line information comprises a lane line position of a lane line adjacent to the site; and obtaining a site category corresponding to each site on the driving path; According to the lane line information, the site category, and the vehicle area in the vehicle information of the current autonomous vehicle, a parking area set corresponding to the site is determined; wherein the parking area in the parking area set is greater than or equal to the vehicle area of the current autonomous vehicle.

7. The method of claim 6, wherein, According to the lane line information, the site category, and the vehicle area in the vehicle information of the current autonomous vehicle, a parking area set corresponding to the site is determined, comprising: According to the lane line information, the site category, and the vehicle area in the vehicle information of the current autonomous vehicle, a parking area including the site is determined; According to the lane line information and the vehicle area in the vehicle information of the current autonomous vehicle, an area adjacent to the parking area including the site is determined as another parking area to obtain the parking area set.

8. The method of any one of claims 1-5, further comprising: receiving the parking area set sent by a remote device; The stop area set is determined according to lane line information corresponding to each station on the driving path, a station category corresponding to each station on the driving path, and a vehicle footprint in vehicle information of the current autonomous vehicle.

9. The method according to any one of claims 1-5, 7, wherein, According to the optimal stop area and a current position of the current autonomous vehicle, a stop path is determined, including: Obtaining road condition information corresponding to the current station; According to the road condition information corresponding to the current station, the stop path is generated with the current position of the current autonomous vehicle as a starting point and the optimal stop area as an ending point.

10. The method according to any one of claims 1-5, 7, wherein, Obtaining obstacle information corresponding to the current station on the driving path, including: Obtaining, by a millimeter wave radar on the current autonomous vehicle, the obstacle information corresponding to the current station on the driving path; Or, receiving, from a collection device located at the current station, the obstacle information corresponding to the current station on the driving path.

11. A station stop control device based on an autonomous vehicle, including: A first obtaining unit configured to obtain obstacle information corresponding to a current station on a driving path, wherein the obstacle information is information of obstacles within a preset geographical range corresponding to the current station; A first determining unit configured to determine an optimal stop area according to the obstacle information corresponding to the current station and a preset stop area set, wherein the stop area set includes a plurality of adjacent stop areas, the stop area is determined based on vehicle information of a current autonomous vehicle and lane line information corresponding to the current station, and in a same coordinate system, a coordinate value of a center point of any stop area in the stop area set in a driving direction of the current autonomous vehicle is between a coordinate value of the station in the driving direction and a coordinate value of the current autonomous vehicle in the driving direction; A second determining unit configured to determine a stop path according to the optimal stop area and a current position of the current autonomous vehicle; A third determining unit configured to control the current autonomous vehicle to stop in the optimal stop area according to the stop path; The stop areas in the stop area set have priorities, the obstacle information includes data information of obstacles in an i-th priority stop area in the stop area set, and the first determining unit includes: An executing module configured to repeat the following modules until the optimal stop area is determined, wherein i is a positive integer greater than or equal to 1, and an initial value of i is 1: A first determining module configured to determine, according to the data information of the obstacles in the i-th priority stop area and a vehicle footprint of the current autonomous vehicle, whether the current autonomous vehicle can stop in the i-th priority stop area; A second determining module configured to determine that the i-th priority stop area is the optimal stop area. a third determining module, configured to determine that the current autonomous vehicle cannot stop in the i-th priority parking area according to the data information of the obstacle in the i-th priority parking area and the vehicle footprint of the current autonomous vehicle; a fourth determining module, configured to determine i=i+1.

12. The apparatus of claim 11, wherein, The data information comprises an obstacle footprint; The first determining module comprises: a first determining submodule, configured to determine a remaining area of the i-th priority parking area according to an area of the i-th priority parking area and an obstacle footprint of the obstacle in the i-th priority parking area; a second determining submodule, configured to determine that the current autonomous vehicle can stop in the i-th priority parking area if it is determined that the remaining area of the i-th priority parking area is greater than or equal to the vehicle footprint of the current autonomous vehicle.

13. The apparatus of claim 12, wherein, The data information comprises an obstacle footprint and an obstacle moving speed; The first determining module comprises: a third determining submodule, configured to determine a remaining area of the i-th priority parking area according to an area of the i-th priority parking area and an obstacle footprint of the obstacle in the i-th priority parking area; a fourth determining submodule, configured to determine a moving distance of the obstacle within a preset time according to the obstacle moving speed if it is determined that the remaining area of the i-th priority parking area is less than the vehicle footprint of the current autonomous vehicle; a fifth determining submodule, configured to determine a free area after the preset time according to the moving distance and the remaining area of the i-th priority parking area; a sixth determining submodule, configured to determine that the current autonomous vehicle can stop in the i-th priority parking area if it is determined that the free area is greater than or equal to the vehicle footprint of the current autonomous vehicle.

14. The apparatus of claim 13, further comprising: a second obtaining unit, configured to obtain an obstacle category of the obstacle in the i-th priority parking area; a fourth determining unit, configured to determine the obstacle moving speed in the data information according to a preset correspondence between the obstacle category and the obstacle moving speed.

15. The apparatus of claim 14, further comprising: a third obtaining unit, configured to obtain road condition information corresponding to the current site; a fourth obtaining unit, configured to obtain an obstacle category of the obstacle in the i-th priority parking area; a fifth determining unit, configured to determine the obstacle moving speed in the data information according to a preset correspondence between the road condition information, the obstacle category and the obstacle moving speed.

16. The apparatus of any one of claims 11-15, further comprising: a fifth obtaining unit, configured to obtain lane line information corresponding to each site on the driving path, wherein the lane line information comprises a lane line position of a lane line adjacent to the site; a sixth obtaining unit, configured to obtain a site category corresponding to each site on the driving path. A sixth determining unit is configured to determine, according to the lane line information, the site category, and a vehicle footprint in the vehicle information of the current autonomous vehicle, a set of parking areas corresponding to the site; wherein each parking area in the set of parking areas is greater than or equal to the vehicle footprint of the current autonomous vehicle.

17. The apparatus of claim 16, wherein, The sixth determining unit comprises: A fifth determining module is configured to determine, according to the lane line information, the site category, and a vehicle footprint in the vehicle information of the current autonomous vehicle, a parking area including the site; A sixth determining module is configured to determine, according to the lane line information, and a vehicle footprint in the vehicle information of the current autonomous vehicle, an area adjacent to the parking area including the site as another parking area, to obtain the set of parking areas.

18. The apparatus of any one of claims 11-15, further comprising: A receiving unit is configured to receive the set of parking areas sent by a remote device; wherein the set of parking areas is determined according to the lane line information corresponding to each site on the driving path, the site category corresponding to each site on the driving path, and a vehicle footprint in the vehicle information of the current autonomous vehicle.

19. The apparatus of any of claims 11-15, 17, wherein, The second determining unit comprises: A first obtaining module is configured to obtain the road condition information corresponding to the current site; A generating module is configured to generate the parking path according to the road condition information corresponding to the current site, with the current position of the current autonomous vehicle as a starting point and the optimal parking area as an ending point.

20. The apparatus of any of claims 11-15, 17, wherein, The first obtaining unit comprises: A second obtaining module is configured to obtain, by a millimeter wave radar on the current autonomous vehicle, the obstacle information corresponding to the current site on the driving path; or, a receiving module is configured to receive the obstacle information corresponding to the current site on the driving path sent by a collection device located on the current site.

21. An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.

22. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1-10.

23. A computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of any one of claims 1-10.

24. An autonomous vehicle comprising: The apparatus of any one of claims 11 to 20.

Images