Method and device for controlling vehicle, vehicle and storage medium

Abstract

The invention discloses a method and device for controlling a vehicle, the vehicle and a storage medium. The method comprises the following steps: acquiring environment information of a vehicle; determining the number of idle parking spaces corresponding to each road in the target parking lot based on the environment information of the vehicle; determining the driving difficulty of the vehicle on each road based on the environment information of the vehicle; based on the number of idle parking spaces corresponding to each road in the target parking lot and the driving difficulty of the vehicle on each road, determining the probability that the vehicle completes automatic parking on each road; determining a target road based on the predicted probability that the vehicle completes automatic parking on each road; and controlling the vehicle to complete automatic parking based on the target road. According to the technical scheme provided by the embodiment of the invention, on the premise that an AVP system does not need to be arranged for the parking lot, an autonomous parking searching function is realized.

Description

Technical Field

[0001] The present application relates to the field of automatic parking technology, and in particular to a method and device for controlling a vehicle, a vehicle, and a storage medium. Background Art

[0002] As the number of private cars increases, parking in big cities and popular business districts faces problems such as complex road conditions and limited parking spaces, making automatic parking more difficult in the above locations.

[0003] In the related technology, the parking lot is equipped with an Automated Valet Parking (AVP) system, that is, parking sensors are arranged in the parking lot to detect the status of the parking space. When the vehicle enters the parking lot, the AVP system sends the parking space status to the vehicle through V2X or the mobile network and allocates an empty parking space for the vehicle. After that, the vehicle plans the route based on the allocated empty parking space and realizes automatic parking after arriving near the empty parking space.

[0004] The solution provided by the related technology requires the deployment of an AVP system in the parking lot, which is costly and difficult to implement on a large scale. Summary of the invention

[0005] The present application proposes a method, device, vehicle and storage medium for controlling a vehicle.

[0006] In a first aspect, an embodiment of the present application provides a method for controlling a vehicle, the method comprising: acquiring environmental information of the vehicle; determining the number of free parking spaces corresponding to each road in a target parking lot based on the environmental information of the vehicle; determining the difficulty of driving the vehicle on each road based on the environmental information of the vehicle; determining the probability of the vehicle completing automatic parking on each road based on the number of free parking spaces corresponding to each road in the target parking lot and the difficulty of driving the vehicle on each road; determining a target road based on the predicted probability of the vehicle completing automatic parking on each road; and controlling the vehicle to complete automatic parking based on the target road.

[0007] In a second aspect, an embodiment of the present application provides a device for controlling a vehicle, the device comprising: an information acquisition module for acquiring environmental information of the vehicle; a quantity determination module for determining the number of free parking spaces corresponding to each road in a target parking lot based on the environmental information of the vehicle; a difficulty determination module for determining the difficulty of driving the vehicle on each road based on the environmental information of the vehicle; a probability prediction module for determining the probability of the vehicle completing automatic parking on each road based on the number of free parking spaces corresponding to each road in the target parking lot and the difficulty of driving the vehicle on each road; a road determination module for determining a target road based on the predicted probability of the vehicle completing automatic parking on each road; and a vehicle control module for controlling the vehicle to complete automatic parking based on the target road.

[0008] In a third aspect, an embodiment of the present application provides a vehicle, comprising: a memory; one or more processors coupled to the memory; one or more programs, wherein one or more applications are stored in the memory and configured to be executed by one or more processors, and the one or more programs are configured to execute the method described in the first aspect.

[0009] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer program instructions are stored. The computer program instructions can be called by a processor to execute the method described in the first aspect.

[0010] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when instructions in the computer program product are executed, is used to implement the method described in the first aspect.

[0011] Compared with the prior art, the technical solution provided in the embodiment of the present application collects the vehicle's own environmental information, determines the number of vacant parking spaces corresponding to each road and the difficulty of the vehicle driving on each road based on the environmental information, and finally predicts the probability of completing automatic parking on each road in the target parking lot based on the number of vacant parking spaces corresponding to each road and the difficulty of the vehicle driving on each road. The above probability is used to determine the target road. Finally, the vehicle obtains a planned path based on the target road, drives according to the planned path and completes automatic parking. Compared with the technical solution provided by the related technology, the technical solution provided in the embodiment of the present application realizes the autonomous search parking function without setting up an AVP system in the parking lot. When the vehicle drives to an unfamiliar parking lot, the probability of completing automatic parking is greatly improved, thereby improving the scope of application of the automatic parking function, reducing costs, and making it easier to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 It is a schematic diagram of an implementation environment provided by an embodiment of the present application.

[0014] Figure 2 This is a flowchart of a method for controlling a vehicle provided by an embodiment of the present application.

[0015] Figure 3 yes Figure 2 An embodiment relates to a schematic diagram of determining the probability of a vehicle achieving automatic parking on each road.

[0016] Figure 4 yes Figure 2 A schematic diagram of a designated sign according to an embodiment.

[0017] Figure 5 This is a flowchart of a method for controlling a vehicle provided by another embodiment of the present application.

[0018] Figure 6 This is a flowchart of a method for controlling a vehicle provided by another embodiment of the present application.

[0019] Figure 7 It is a block diagram of a device for controlling a vehicle provided by an embodiment of the present application.

[0020] Figure 8 It is a structural block diagram of a vehicle provided by an embodiment of the present application. DETAILED DESCRIPTION

[0021] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0022] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0023] Please refer to Figure 1 , which shows a schematic diagram of an implementation environment provided by an embodiment of the present application. The implementation environment includes a vehicle 100. In the embodiment of the present application, the vehicle 100 has an autonomous parking search function, that is, a function of autonomously searching for available vacant parking spaces and automatically parking in the vacant parking spaces.

[0024] Specifically, vehicle 100 includes an intelligent driving system, which can obtain environmental information of vehicle 100, and predict the number of vacant parking spaces corresponding to each road in the parking lot of vehicle 100 and the difficulty of driving the vehicle on each road based on the environmental information of vehicle 100. Finally, based on the number of vacant parking spaces corresponding to each road and the difficulty of driving the vehicle on each road, the intelligent driving system predicts the probability of completing automatic parking on each road in the target parking lot. The above-mentioned predicted multiple probabilities are used to determine the target road. After that, vehicle 100 completes automatic parking based on the target road.

[0025] In an embodiment of the present application, the vehicle 100 includes an environmental perception system for sensing the environment in which the vehicle 100 is located, and the perception data obtained is input into the intelligent driving system as a basis for decision-making of the intelligent driving system. Optionally, the environmental perception system includes a plurality of cameras arranged around the vehicle 100 for collecting images of the environment in which the vehicle 100 is located, wherein the image acquisition ranges of the plurality of cameras are partially repeated, thereby achieving comprehensive monitoring of the environment around the vehicle 100. Optionally, the environmental perception system also includes a radar, such as a laser radar, a millimeter wave radar, an ultrasonic radar, etc., for detecting obstacle information in the environment in which the vehicle 100 is located.

[0026] In an embodiment of the present application, the vehicle 100 also includes a motion perception system for sensing the motion parameters of the vehicle 100, and the perception data obtained is also input into the intelligent driving system as another decision-making basis of the intelligent driving system. Optionally, the motion perception system includes an inertial measurement unit (IMU), a wheel speed sensor, a steering wheel angle sensor, and a steering wheel torque sensor. The IMU is used to measure the three-axis attitude angle and acceleration of the vehicle 100. The wheel speed sensor is used to measure the wheel speed of the vehicle 100. The steering wheel information angle sensor is used to measure the angle of the steering wheel. The steering wheel torque sensor is used to measure the torque of the steering wheel.

[0027] Please refer to Figure 2 , which shows a flow chart of a method for controlling a vehicle provided by an embodiment of the present application. The method includes the following process.

[0028] S201, obtaining vehicle environment information.

[0029] The vehicle obtains the vehicle's environmental information through the environmental perception system. Optionally, the vehicle collects the environment image of the vehicle through the camera set around itself, and detects obstacle information through radar. The above obstacle information includes the relative position relationship between the obstacle and the vehicle, the shape of the obstacle, and the motion parameters (movement direction, speed, acceleration, etc.).

[0030] In this embodiment, after obtaining environmental information, the vehicle constructs a road topology map. The road topology map includes elements such as the roads in the parking lot, the distribution of parking spaces, other vehicles, obstacles, and traffic facilities. Figure 3 Part (a) in the figure shows a schematic diagram of a road topology map 310 shown in an embodiment of the present application.

[0031] Furthermore, after the road topology map is constructed, the vehicle continues to generate a road topology skeleton map, wherein the road topology skeleton only includes road elements, and the intersections of the road are highlighted. Figure 3 Part (b) of FIG. 3 shows a schematic diagram of a road topology skeleton diagram 320 shown in an embodiment of the present application. The locations marked with circles are intersections, wherein hollow circles are areas that have not been detected by the vehicle, and solid circles are areas that have been detected by the vehicle. As the vehicle moves forward, the area it detects continues to expand, and the road topology skeleton diagram is also updated accordingly.

[0032] In some embodiments, when the vehicle is in an automatic parking scenario, the vehicle's environmental information is obtained. The vehicle determines that it is in an automatic parking scenario after recognizing that it has passed through a parking lot gate. Alternatively, when the distance between the vehicle and the end of the path is less than a preset distance and the vehicle recognizes that there is a parking sign in the environment where the vehicle is located, it is determined that it is in an automatic parking scenario. The parking sign may be "P", a parking space line sign, etc. The preset distance is set based on experiments or experience, for example, 20m.

[0033] Furthermore, when the vehicle is in an automatic parking scenario and the automatic parking search function is turned on, the vehicle's environmental information is obtained. The automatic parking search function refers to the function of automatically searching for an empty parking space and completing automatic parking. The switch state of the automatic parking search function can be customized by the user. Optionally, the vehicle's central control screen or instrument panel includes a switch control for the automatic parking search function. When the automatic parking search function is in the off state, if a trigger signal for the above switch control is obtained, the automatic parking search function is turned on.

[0034] Alternatively, when the vehicle is in an automatic parking scenario and an automatic parking search instruction is obtained, the vehicle's environmental information is obtained. The automatic parking search instruction may be generated by a user triggering a designated control on the vehicle's central control screen or dashboard, or may be a voice instruction, which is not limited in the present embodiment of the application.

[0035] S202: Determine the number of vacant parking spaces corresponding to each road in the target parking lot based on the environment information of the vehicle.

[0036] The target parking lot is the parking lot that the vehicle is currently in. The target parking lot usually includes multiple roads, and the multiple roads divide the target parking lot into different parking areas, and different roads allow vehicles to reach different parking areas.

[0037] The number of free parking spaces corresponding to a road refers to the number of free parking spaces included in the parking area accessible via the road. In some embodiments, the environmental information of the vehicle includes a target image corresponding to a designated sign, and the designated sign includes the number of free parking spaces in different directions of the target parking lot. At this time, the vehicle obtains the target image from the environmental information and performs image recognition on the target image to obtain the number of free parking spaces in different directions of the target parking lot. Then, based on the distribution of roads in the target parking lot and the number of free parking spaces in different directions, the number of free parking spaces corresponding to each road is determined.

[0038] In some embodiments, after determining the number of vacant parking spaces corresponding to each road, the vehicle marks the number of vacant parking spaces corresponding to each road on the road skeleton topology map. Figure 3 Part (c) of the

[0039] like Figure 4 As shown, it shows a schematic diagram of a designated sign 400 provided in an embodiment of the present application. The designated sign 400 includes the number of vacant parking spaces in different directions.

[0040] In other embodiments, when the vehicle's environmental information does not include a target image, the vehicle detects the number of vacant parking spaces corresponding to each road through an environmental perception system.

[0041] S203: Determine the difficulty of the vehicle traveling on each road based on the vehicle's environmental information.

[0042] In an embodiment of the present application, the difficulty of a vehicle traveling on a road is used to indicate the difficulty of the vehicle passing through the road. The higher the difficulty of a vehicle traveling on a road, the easier it is for the vehicle to pass through the road.

[0043] Optionally, for each road, obstacle information of the road and the congestion status of the road are obtained from the vehicle's environmental information; based on the obstacle information of the road, a first passing probability of the vehicle traveling on the road is determined; based on the congestion status of the road, a second passing probability of the vehicle traveling on the road is determined; based on the first passing probability of the vehicle traveling on the road and the second passing probability of the vehicle traveling on the road, the difficulty of the vehicle traveling on the road is determined.

[0044] Obstacles include static obstacles and moving obstacles. The obstacle information of static obstacles includes the relative position relationship between the obstacle and the vehicle, the shape of the obstacle (including the height and width of the obstacle, etc.). The obstacle information of dynamic obstacles includes the relative position relationship between the obstacle and the vehicle, the shape of the obstacle (including the height and width of the obstacle, etc.), and the movement parameters of the obstacle (movement direction, speed, acceleration).

[0045] In some embodiments, for a stationary obstacle, the vehicle determines whether it can cross the stationary obstacle based on the height of the stationary obstacle and the height of its own chassis. If the vehicle can cross the stationary obstacle, the first passing probability is assigned to a first specified value. Among them, if the height of the stationary obstacle is less than the height of the chassis, and the difference between the two is greater than a first preset value, it means that the vehicle can cross the stationary obstacle. The first specified value and the first preset value are both set based on experiments or experience. Exemplarily, the first specified value is 1, and the first preset value is 0.3 meters. That is, if the height of the vehicle chassis is more than 0.3 meters greater than the height of the stationary obstacle, it means that the vehicle can cross the stationary obstacle, and the first passing rate is 1.

[0046] If the vehicle cannot cross the stationary obstacle, it is determined whether the vehicle is allowed to pass based on the width of the road, the width of the obstacle and the width of the vehicle. If the vehicle is not allowed to pass, the first passing probability is assigned to the second specified value. Among them, if the width of the vehicle is greater than or equal to the specified width, or the width of the vehicle is less than the specified width, and the difference between the two is less than the second preset value, the vehicle is not allowed to pass. The specified width refers to the maximum value of the distance between the left side of the stationary obstacle and the left side of the road, and the distance between the right side of the stationary obstacle and the right side of the road. For example, the distance from the left side of the stationary obstacle to the left side of the road is 2.8 meters, and the distance from the right side of the stationary obstacle to the right side of the road is 0.3 meters, then the specified width is 2.8 meters. The first specified value and the second preset value are set according to experiments or experience, for example, the first specified value is 1, and the second preset value is 0.3 meters. That is, if the width of the vehicle is greater than the specified width, or the specified width is only 0.3 meters wider than the width of the vehicle, the vehicle cannot pass the stationary obstacle at this time, so the first passing probability is set to 0.

[0047] If the width of the vehicle is less than the specified width and the difference between the two is greater than a second preset value, the first pass rate is determined based on the difference between the two. The value of the first pass rate is between the first specified value and the second specified value, and the larger the difference between the two, the larger the first pass rate.

[0048] For a moving obstacle, the vehicle determines the collision position between itself and the moving obstacle. If there is no available parking space between its current position and the above collision position, the first passing probability is assigned to the second preset value. If there is an available parking space between its current position and the above collision position, the first passing probability is determined according to the number of available parking spaces between its current position and the above collision position. The larger the number of available parking spaces between its current position and the above collision position, the greater the first passing probability, and the maximum first passing probability is equal to the first preset value.

[0049] In other embodiments, the vehicle obtains the speed of the vehicle on each road, and determines the congestion state of the road according to the speed of the vehicle. If the speed of the vehicle is low, the road is more congested, and if the speed of the vehicle is high, the road is smoother. The vehicle can determine the second pass rate based on the speed of other vehicles and the preset mapping relationship. The lower the speed of other vehicles on the road, the lower the second pass rate of the vehicle traveling on the road, and the higher the speed of other vehicles on the road, the higher the second pass rate of the vehicle traveling on the road. The preset mapping relationship is set according to experiments or experience, and the embodiments of the present application are not limited to this.

[0050] In some embodiments, the vehicle performs a weighted summation of the first passing probability and the second passing probability to obtain the difficulty of the vehicle traveling on at least one road. The first coefficient corresponding to the first passing probability and the second coefficient corresponding to the second passing probability are set according to experiments or experience, and the embodiments of the present application are not limited to this.

[0051] In some embodiments, after determining the difficulty of driving on at least one road, the vehicle marks the difficulty of each road on the road skeleton topology map. Figure 3 Part (d) of the

[0052] It should be noted that the embodiment of the present application does not limit the execution order of S202 and S203. The vehicle may execute S202 first and then S203; or, execute S203 first and then S202; or, execute S202 and S203 simultaneously.

[0053] S204: Determine the probability of the vehicle completing automatic parking on each road based on the number of vacant parking spaces corresponding to each road in the target parking lot and the difficulty of the vehicle traveling on each road.

[0054] The greater the probability of a vehicle completing automatic parking on a road, the higher the success rate of the vehicle finding an available parking space and completing automatic parking on the road. The probability of a vehicle completing automatic parking on a road depends on two factors: whether the road is open to traffic and whether there are vacant parking spaces in the parking area corresponding to the road. Therefore, the vehicle predicts the probability of the vehicle completing automatic parking on each road based on the above two factors.

[0055] Optionally, the vehicle multiplies the number of free parking spaces corresponding to each road and the difficulty of driving on the road to obtain the probability of the vehicle completing automatic parking on the road.

[0056] In some embodiments, after determining the probability of the vehicle completing automatic parking on at least one road, the vehicle marks the probability of achieving automatic parking on each road on the road skeleton topology map. Figure 3 Part (e) of the

[0057] S205: Determine a target road based on the predicted probability of the vehicle completing automatic parking on each road.

[0058] The target road is a road that the vehicle is about to travel on. The vehicle travels on the target road and selects a parking space from the vacant parking spaces corresponding to the target road for the vehicle to complete automatic parking. The method of determining the target road will be described in the following embodiments.

[0059] S206, controlling the vehicle to complete automatic parking based on the target road.

[0060] The vehicle then searches for an available parking space on the target road and completes automatic parking.

[0061] To sum up, the technical solution provided in the embodiment of the present application collects its own environmental information through the vehicle, determines the number of vacant parking spaces corresponding to each road and the difficulty of the vehicle driving on each road based on the environmental information, and finally predicts the probability of completing automatic parking on each road in the target parking lot based on the number of vacant parking spaces corresponding to each road and the difficulty of the vehicle driving on each road. The above probability is used to determine the target road. Finally, the vehicle obtains a planned path based on the target road, drives according to the planned path and completes automatic parking. Compared with the technical solutions provided by related technologies, the technical solution provided in the embodiment of the present application realizes the autonomous search parking function without setting up an AVP system in the parking lot. When the vehicle drives to an unfamiliar parking lot, the probability of completing automatic parking is greatly improved, thereby improving the scope of application of the automatic parking function, reducing costs, and making it easier to implement.

[0062] Please refer to Figure 5, which shows a flow chart of a method for controlling a vehicle provided by an embodiment of the present application, the method includes the following process.

[0063] S501, obtaining vehicle environment information.

[0064] S502: Determine the number of vacant parking spaces corresponding to each road in the target parking lot based on the environment information of the vehicle.

[0065] S503: Determine the difficulty of the vehicle traveling on each road based on the vehicle's environmental information.

[0066] S504: Determine the probability of the vehicle completing automatic parking on each road based on the number of free parking spaces corresponding to each road in the target parking lot and the difficulty of the vehicle traveling on each road.

[0067] S505: Determine a target road based on the predicted probability of the vehicle completing automatic parking on each road.

[0068] In some embodiments, the vehicle selects a road with the highest probability of completing automatic parking by itself as the target road.

[0069] In other embodiments, a road where the probability that the vehicle will complete automatic parking is greater than a preset probability and includes a designated parking space is determined as a target road.

[0070] The preset probability is set based on experiments or experience, and the embodiments of the present application do not limit this. The designated parking space includes: the available parking space closest to the vehicle, or the available parking space closest to the entrance of the designated place. The available parking space refers to an empty parking space without obstacles that affect automatic parking.

[0071] In the case where the designated parking spaces include the available parking spaces closest to the vehicle, the vehicle can quickly complete automatic parking after determining the target road. Optionally, the vehicle determines a road with a probability of completing automatic parking greater than a preset probability as a first candidate road, and then calculates the distance between the first available parking space of each first candidate road and the current position of the vehicle, and then determines the first candidate road with the smallest distance between the first available parking space and the current position of the vehicle as the target road. The first available parking space of a candidate road refers to the first available parking space that the vehicle reaches when driving on the candidate road.

[0072] In the case where the designated parking spaces include the available parking spaces closest to the designated place entrance, the vehicle can complete automatic parking at a location closer to the designated place entrance after determining the target road, so that the user can enter the designated place more quickly. Optionally, the vehicle determines a road with a probability of completing automatic parking greater than a preset probability as a second candidate road, and then calculates the distance between the available parking space closest to the designated place entrance and the designated place entrance in each second candidate road, and then determines the second candidate road with the smallest distance between the available parking space closest to the designated place entrance and the designated place entrance as the target road.

[0073] In some other embodiments, the vehicle highlights a third candidate road whose probability of completing automatic parking is greater than a preset probability. The third candidate road is in a selectable state, and the user can select a target road from the third candidate roads according to his or her own needs.

[0074] S506: Determine a target parking space among the available parking spaces corresponding to the target road.

[0075] In some embodiments, the target parking space is an available parking space in the target road that is closest to the current position of the vehicle. In other embodiments, the target parking space is an available parking space in the target road that is closest to the entrance of a designated place. In still other embodiments, the target parking space is an available parking space that a user has customized and selected in the target road.

[0076] S507, performing global path planning with the current position of the vehicle as the starting point and the target parking space as the end point to obtain a planned path.

[0077] The vehicle takes the current position of the vehicle as the starting point and the target parking space as the end point, and uses a preset path planning algorithm to perform global path planning to obtain a planned path. The planned path includes multiple path points, each of which corresponds to the planned driving direction, planned speed, and planned acceleration of the vehicle. The algorithms used by the vehicle for global path planning include: Dyke Tesla algorithm, A* algorithm, Bellman-Ford algorithm, etc., which are not limited in the embodiments of the present application.

[0078] S508, controlling the vehicle to drive to the target parking space according to the planned path and completing automatic parking.

[0079] The vehicle controls the parameters of its own braking system (such as braking torque), the parameters of the driving system (such as driving torque) and the parameters of the steering system (steering wheel angle) so that the speed of the vehicle at each path point on the planned path reaches the above-mentioned planned speed, the acceleration at each path point reaches the above-mentioned planned acceleration, and the driving direction at each path point reaches the above-mentioned planned driving direction, thereby achieving driving along the planned path. After the vehicle reaches the end point of the planned path, automatic parking is completed through the automatic parking function.

[0080] In addition, when the vehicle is driving along the planned path, it will also perform local path planning based on the detected dynamic obstacles (such as vehicles, pedestrians, small animals, etc.) to achieve autonomous obstacle avoidance during the search for parking. Local path planning algorithms include but are not limited to: dynamic window method, exploration method, etc.

[0081] To sum up, the technical solution provided in the embodiment of the present application collects its own environmental information through the vehicle, determines the number of vacant parking spaces corresponding to each road and the difficulty of the vehicle driving on each road based on the environmental information, and finally predicts the probability of completing automatic parking on each road in the target parking lot based on the number of vacant parking spaces corresponding to each road and the difficulty of the vehicle driving on each road. The above probability is used to determine the target road. Finally, the vehicle obtains a planned path based on the target road, drives according to the planned path and completes automatic parking. Compared with the technical solutions provided by related technologies, the technical solution provided in the embodiment of the present application realizes the autonomous search parking function without setting up an AVP system in the parking lot. When the vehicle drives to an unfamiliar parking lot, the probability of completing automatic parking is greatly improved, thereby improving the scope of application of the automatic parking function, reducing costs, and making it easier to implement.

[0082] Please refer to Figure 6 , which shows a flow chart of a method for controlling a vehicle according to an embodiment of the present application. The method includes the following steps.

[0083] S601, obtaining vehicle environment information.

[0084] S602, detecting whether there is an available parking space in the designated area.

[0085] The designated area refers to the parking area corresponding to the route between the current position of the vehicle and the target intersection, that is, the parking area on one side or both sides of the above route. When the vehicle is traveling on the route, it can complete automatic parking in the above parking area. The target intersection is the intersection closest to the vehicle on the road where the vehicle is currently traveling.

[0086] In some embodiments, the vehicle first detects whether there is an empty parking space in the above-mentioned designated area, that is, a parking space where no vehicle is parked. When an empty parking space is detected, the vehicle further detects whether the empty parking space meets the parking requirement. If the parking requirement is met, the empty parking space is determined to be an available parking space. If the parking requirement is not met, the empty parking space is not an available parking space. Detecting whether an empty parking space meets the parking requirement refers to detecting whether there are obstacles that affect the parking of the vehicle (such as safety cones, ground locks, etc.). If there are no obstacles that affect the parking of the vehicle, it is determined that the parking requirement is met.

[0087] In some embodiments, when the distance between the vehicle's current position and the intersection of at least one road is greater than a specified distance, the vehicle detects whether there is an available parking space in the specified area. When the distance between the vehicle's current position and the intersection of at least one road is less than or equal to the specified distance, the vehicle starts executing by predicting the probability of the vehicle completing automatic parking on at least one road in the target parking lot based on the vehicle's environmental information. The specified distance is set based on experiments or experience, and is not limited to this in the embodiments of the present application, for example, 10 meters. If the distance between the vehicle's current position and the intersection of at least one road is less than or equal to the specified distance, it means that the vehicle is about to enter the intersection, and even if there is an available parking space in the specified area, the vehicle will not complete parking. Therefore, there is no need to determine whether there is an available parking space.

[0088] S603: When it is detected that there are no available parking spaces in the designated area, the number of free parking spaces corresponding to each road in the target parking lot is determined based on the environmental information of the vehicle.

[0089] If it is detected that there is no available parking space in the designated area, the vehicle needs to continue driving to find an available parking space, and needs to decide on a target road from multiple roads in the target parking lot, so the vehicle executes the step of predicting the probability of the vehicle completing automatic parking on at least one road in the target parking lot based on the vehicle's environmental information. However, if it is detected that there is an available parking space in the designated area, the vehicle can directly select an available parking space to complete automatic parking without continuing to drive and executing subsequent steps, which can save the vehicle's energy consumption.

[0090] S604: Determine the difficulty of the vehicle traveling on each road based on the vehicle's environmental information.

[0091] S605: Determine the probability of the vehicle completing automatic parking on each road based on the number of vacant parking spaces corresponding to each road in the target parking lot and the difficulty of the vehicle traveling on each road.

[0092] S606: Determine a target road based on the predicted probability of the vehicle completing automatic parking on each road.

[0093] S607, controlling the vehicle to complete automatic parking based on the target road.

[0094] To sum up, the technical solution provided in the embodiment of the present application executes subsequent steps when it is detected that there are no available parking spaces in the designated area, thereby avoiding the situation where the vehicle has selected an available parking space before arriving at the intersection and continuing to execute subsequent steps, thereby saving the vehicle's energy consumption.

[0095] Please refer to Figure 7, which shows a block diagram of a device for controlling a vehicle shown in an embodiment of the present application. The device includes: an information acquisition module 710, a quantity determination module 720, a difficulty determination module 730, a probability prediction module 740, a road determination module 750, and a vehicle control module 760.

[0096] The information acquisition module 710 is used to acquire the vehicle's environmental information.

[0097] The quantity determination module 720 is used to determine the number of vacant parking spaces corresponding to each road in the target parking lot based on the environmental information of the vehicle.

[0098] The difficulty determination module 730 is used to determine the difficulty of the vehicle traveling on each road based on the vehicle's environmental information.

[0099] The probability prediction module 740 is used to determine the probability of the vehicle completing automatic parking on each road based on the number of vacant parking spaces corresponding to each road in the target parking lot and the difficulty of the vehicle traveling on each road.

[0100] The road determination module 750 is used to determine a target road based on the predicted probability of the vehicle completing automatic parking on each road.

[0101] The vehicle control module 760 is used to control the vehicle to complete automatic parking based on the target road.

[0102] In some embodiments, the difficulty determination module 730 is used to obtain, for each road, obstacle information of the road and the congestion status of the road from the vehicle's environmental information; determine a first passing probability of the vehicle traveling on the road based on the obstacle information of the road; determine a second passing probability of the vehicle traveling on the road based on the congestion status of the road; and determine the difficulty of the vehicle traveling on the road based on the first passing probability of the vehicle traveling on the road and the second passing probability of the vehicle traveling on the road.

[0103] In some embodiments, the quantity determination module 720 is used to obtain a target image corresponding to a designated sign from the vehicle's environmental information, where the designated sign includes the number of vacant parking spaces corresponding to multiple directions in the target parking lot; and perform image recognition on the target image to determine the number of vacant parking spaces corresponding to each road.

[0104] In some embodiments, the device further includes: an available parking space detection module (not shown in the figure). The available parking space detection module is used to detect whether there are available parking spaces in a specified area, and the specified area refers to the parking area corresponding to the route between the current position of the vehicle and the target intersection. The quantity determination module 720 is used to perform the step of determining the number of free parking spaces corresponding to each road in the target parking lot based on the vehicle's environmental information when it is detected that there are no available parking spaces in the specified area.

[0105] In some embodiments, the road determination module 750 is used to determine the road with the highest probability of the vehicle completing automatic parking as the target road.

[0106] In some embodiments, the road determination module 750 is used to determine a road where the probability of the vehicle completing automatic parking is greater than a preset probability and includes a designated parking space as a target road; wherein the designated parking space includes: an available parking space that is closest to the vehicle, or an available parking space that is closest to the entrance of a designated place.

[0107] In some embodiments, the vehicle control module 760 is used to determine the target parking space among the available parking spaces corresponding to the target road; perform global path planning with the current position of the vehicle as the starting point and the target parking space as the end point to obtain a planned path; control the vehicle to travel to the target parking space according to the planned path and complete automatic parking.

[0108] To sum up, the technical solution provided in the embodiment of the present application collects its own environmental information through the vehicle, determines the number of vacant parking spaces corresponding to each road and the difficulty of the vehicle driving on each road based on the environmental information, and finally predicts the probability of completing automatic parking on each road in the target parking lot based on the number of vacant parking spaces corresponding to each road and the difficulty of the vehicle driving on each road. The above probability is used to determine the target road. Finally, the vehicle obtains a planned path based on the target road, drives according to the planned path and completes automatic parking. Compared with the technical solutions provided by related technologies, the technical solution provided in the embodiment of the present application realizes the autonomous search parking function without setting up an AVP system in the parking lot. When the vehicle drives to an unfamiliar parking lot, the probability of completing automatic parking is greatly improved, thereby improving the scope of application of the automatic parking function, reducing costs, and making it easier to implement.

[0109] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0110] In several embodiments provided in the present application, the coupling between modules may be electrical, mechanical or other forms of coupling.

[0111] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software functional modules.

[0112] See also Figure 8 , which shows that the embodiment of the present application further provides a vehicle 800, which includes: one or more multi-core processors 810, a memory 820, and one or more applications. Among them, the one or more applications are stored in the memory 820 and are configured to be executed by the one or more multi-core processors 810, and the one or more applications are configured to execute the method described in the above embodiment.

[0113] The multi-core processor 810 may include one or more processing cores. The multi-core processor 810 uses various interfaces and lines to connect various parts of the entire battery management system, and executes various functions and processes data of the battery management system by running or executing instructions, programs, code sets or instruction sets stored in the memory 820, and calling data stored in the memory 820. Optionally, the multi-core processor 810 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The multi-core processor 810 can integrate one or more combinations of a central multi-core processor 810 (Central Processing Unit, CPU), an image multi-core processor 810 (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the multi-core processor 810, and may be implemented separately through a communication chip.

[0114] The memory 820 may include a random access memory 820 (Random Access Memory, RAM), and may also include a read-only memory 820 (Read-Only Memory). The memory 820 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 820 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the various method embodiments described below, etc. The data storage area may also store data created by the vehicle map during use (such as a phone book, audio and video data, chat record data), etc.

[0115] An embodiment of the present application further provides a computer-readable storage medium, in which computer program instructions are stored. The computer program instructions can be called by a processor to execute the method described in the above embodiment.

[0116] The computer-readable storage medium may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. Optionally, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium has storage space for computer program instructions for executing any method step of the above method. These computer program instructions may be read from or written to one or more computer program products. The computer program instructions may be compressed in an appropriate form.

[0117] The above are only preferred embodiments of the present application, and are not intended to limit the present application in any form. Although the present application has been disclosed as above with preferred embodiments, it is not intended to limit the present application. Any technical personnel in the field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present application. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application are still within the scope of the technical solution of the present application.

Claims

1. A method for controlling a vehicle, characterized in that: The method comprises: Obtain vehicle environmental information; Determining the number of free parking spaces corresponding to each road in the target parking lot based on the environmental information of the vehicle; determining the difficulty of the vehicle traveling on each of the roads based on the environmental information of the vehicle; Determining the probability of the vehicle completing automatic parking on each road based on the number of vacant parking spaces corresponding to each road in the target parking lot and the difficulty of the vehicle traveling on each road; Determining a target road based on the predicted probability of the vehicle completing automatic parking on each of the roads; The vehicle is controlled to complete automatic parking based on the target road.

2. The method according to claim 1, characterized in that: The determining the difficulty of the vehicle traveling on each of the roads based on the environmental information of the vehicle includes: For each of the roads, obtaining obstacle information of the road and congestion status of the road from the environmental information of the vehicle; Determining a first passing probability of the vehicle traveling on the road based on obstacle information of the road; Determining a second passing probability of the vehicle traveling on the road based on the congestion state of the road; The difficulty of the vehicle traveling on the road is determined based on a first passing probability of the vehicle traveling on the road and a second passing probability of the vehicle traveling on the road.

3. The method according to claim 1, characterized in that The determining the number of vacant parking spaces corresponding to each road in the target parking lot based on the environmental information of the vehicle includes: Acquire a target image corresponding to a designated sign from the environmental information of the vehicle, wherein the designated sign includes the number of vacant parking spaces corresponding to different directions in the target parking lot; Image recognition is performed on the target image to determine the number of vacant parking spaces corresponding to each of the roads in the target parking lot.

4. The method according to any one of claims 1 to 3, characterized in that Before determining the number of vacant parking spaces corresponding to each road in the target parking lot based on the environmental information of the vehicle, the method further includes: Detecting whether there is an available parking space in a designated area, wherein the designated area refers to a parking area corresponding to a route between the current position of the vehicle and a target intersection; In the case where it is detected that there is no available parking space in the designated area, the step of determining the number of free parking spaces corresponding to each road in the target parking lot based on the environmental information of the vehicle is performed.

5. The method according to any one of claims 1 to 3, characterized in that: The determining of the target road based on the predicted probability of the vehicle completing automatic parking on each of the roads includes: The road on which the probability of the vehicle completing automatic parking is greatest is determined as the target road.

6. The method according to any one of claims 1 to 3, characterized in that: The determining of the target road based on the predicted probability of the vehicle completing automatic parking on each of the roads includes: A road in which the probability of the vehicle completing automatic parking is greater than a preset probability and includes a designated parking space is determined as the target road; wherein the designated parking space includes: an available parking space closest to the vehicle, or an available parking space closest to an entrance to a designated place.

7. The method according to any one of claims 1 to 3, characterized in that The controlling the vehicle to complete automatic parking based on the target road includes: Determining a target parking space among the available parking spaces corresponding to the target road; Performing global path planning with the current position of the vehicle as the starting point and the target parking space as the end point to obtain a planned path; The vehicle is controlled to drive to the target parking space according to the planned path and complete automatic parking.

8. A device for controlling a vehicle, characterized in that: The device comprises: An information acquisition module, used to acquire environmental information of the vehicle; A quantity determination module, used to determine the number of vacant parking spaces corresponding to each road in the target parking lot based on the environmental information of the vehicle; A difficulty determination module, configured to determine the difficulty of the vehicle traveling on each of the roads based on the environmental information of the vehicle; a probability prediction module, configured to determine the probability of the vehicle completing automatic parking on each road based on the number of vacant parking spaces corresponding to each road in the target parking lot and the difficulty of the vehicle traveling on each road; A road determination module, configured to determine a target road based on the predicted probability of the vehicle completing automatic parking on each of the roads; A vehicle control module is used to control the vehicle to complete automatic parking based on the target road.

9. A vehicle, characterized in that: include: Memory; One or more processors coupled to the memory; One or more programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and the computer program instructions can be called by a processor to execute the method according to any one of claims 1 to 7.

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