Vehicle control method, device, storage medium and vehicle

Abstract

The present disclosure relates to a vehicle control method, device, storage medium and vehicle. The method comprises: in response to a vehicle parking-out instruction, obtaining surrounding environment information of the vehicle; determining one or more to-be-used parking-out data from pre-stored historical parking-out data according to the surrounding environment information; determining target parking-out data from the one or more to-be-used parking-out data according to a confidence level of each to-be-used parking-out data; and controlling the vehicle to park out from a current parking position according to the target parking-out data. The present disclosure can effectively improve the reliability of the parking-out data, thereby effectively reducing the parking-out failure phenomenon and further improving the safety performance of the vehicle and the driving experience of the vehicle user.

Description

Vehicle control methods, devices, storage media and vehicles Technical Field

[0001] This disclosure relates to the field of intelligent driving technology, specifically to a vehicle control method, device, storage medium, and vehicle. Background Technology

[0002] In recent years, with the continuous development of vehicle intelligence technology, intelligent driving functions have gradually become standard features in most vehicles. These functions provide some assistance to users during parking and exiting the parking area, thus improving the user experience to some extent. However, the reliability of current vehicle intelligent driving functions, especially the control methods during parking and exiting the parking area, remains low, and control errors are prone to occur, which is detrimental to further improving vehicle safety performance and the user's driving experience. Summary of the Invention

[0003] To achieve the above objectives, this disclosure provides a vehicle control method, apparatus, storage medium, and vehicle.

[0004] According to a first aspect of the present disclosure, a vehicle control method is provided, the method comprising:

[0005] In response to receiving a vehicle parking command, the vehicle's surrounding environment information is obtained;

[0006] Based on the surrounding environment information, one or more pending berthing data are determined from pre-stored historical berthing data;

[0007] Based on the confidence level of each of the pending berthing data, determine the target berthing data from the one or more pending berthing data;

[0008] The vehicle is controlled to exit from its current parking position based on the target parking data.

[0009] Optionally, the historical berthing data includes historical berthing environment information, berthing trajectory, and the correspondence between the historical berthing environment information and the berthing trajectory. The step of determining one or more pending berthing data from the pre-stored historical berthing data based on the surrounding environment information includes:

[0010] Determine one or more target berthing environment information that matches the surrounding environment information from the historical berthing data;

[0011] The berthing trajectory corresponding to each of the target berthing environment information is used as the berthing data to obtain the one or more berthing data to be used corresponding to the one or more target berthing environment information.

[0012] Optionally, the confidence level includes a first confidence level and a second confidence level, and determining the target berthing data from the one or more candidate berthing data based on the confidence level of each candidate berthing data includes:

[0013] The first confidence level corresponding to the waiting berthing data is determined based on the surrounding environment information and the target berthing environment information in each waiting berthing data;

[0014] Based on the surrounding environment information, a baseline berthing trajectory is determined using a preset trajectory algorithm;

[0015] The second confidence level of the historical berthing trajectory corresponding to the target berthing environment information in the pending berthing data is determined based on the benchmark berthing trajectory.

[0016] The target berthing data is determined from the one or more pending berthing data based on the first confidence level and the second confidence level corresponding to each pending berthing data.

[0017] Optionally, determining the target berthing data from the one or more candidate berthing data based on the first confidence level and the second confidence level corresponding to each candidate berthing data includes:

[0018] Obtain the surrounding environment information and the preset weights of the parking trajectory;

[0019] The first confidence level and the second confidence level corresponding to each piece of data to be used for berthing are weighted and summed according to the preset weights to obtain the target confidence level corresponding to the data to be used for berthing.

[0020] If the target confidence level of the pending berthing data is determined to be greater than or equal to a preset confidence threshold, the pending berthing data will be used as the designated berthing data.

[0021] The target berthing data is determined based on the specified berthing data.

[0022] Optionally, determining the target berthing data based on the specified berthing data includes:

[0023] If it is determined that a specified berthing data exists, the specified berthing data shall be used as the target berthing data;

[0024] If it is determined that there are multiple specified berthing data, the target berthing data is determined from the multiple specified berthing data according to a preset recommended order.

[0025] Optionally, the method further includes:

[0026] If the target confidence level of each of the one or more pending berthing data is determined to be less than a preset confidence threshold, a manual berthing prompt message is output.

[0027] Optionally, the method further includes:

[0028] Acquire information about the vehicle's parking environment, including tire steering angle, wheel speed pulse, vehicle gear, yaw angle, and inertial measurement unit information;

[0029] The parking trajectory corresponding to the parking environment information is determined based on the steering angle, the wheel speed pulse, the vehicle's gear position, the yaw angle, and the inertial measurement unit information.

[0030] The berthing environment information and the corresponding berthing trajectory are stored to update the historical berthing data.

[0031] Optionally, determining one or more pending berthing data from pre-stored historical berthing data based on the surrounding environment information includes:

[0032] If historical parking data is found to exist, the historical parking data is used as the parking data to be used for parking out, wherein the historical parking data is the parking data of the vehicle entering the current parking position when the vehicle is manually driven.

[0033] According to a second aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, performs the steps of the method described in the first aspect above.

[0034] According to a third aspect of the present disclosure, a vehicle control device is provided, comprising:

[0035] A memory on which computer programs are stored;

[0036] A processor for executing the computer program in the memory to implement the steps of the method described in the first aspect above.

[0037] According to a fourth aspect of the present disclosure, a vehicle is provided, including: a vehicle control device as described in the third aspect.

[0038] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0039] By acquiring the vehicle's current surrounding environment information, determining the parking data to be used from pre-stored historical parking data based on the current surrounding environment information, calculating the confidence level of the parking data to be used, determining the target parking data, and parking the vehicle based on the target parking data, the reliability of parking data can be effectively improved, thereby effectively reducing parking errors and further enhancing vehicle safety performance and the driving experience of vehicle users.

[0040] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0041] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0042] Figure 1 is a flowchart illustrating a vehicle control method according to an exemplary embodiment;

[0043] Figure 2 is a schematic diagram illustrating a lane line type according to an exemplary embodiment of the present disclosure;

[0044] Figure 3 is a flowchart illustrating a vehicle control method according to the embodiment shown in Figure 1 of this disclosure;

[0045] Figure 4 is a flowchart illustrating another vehicle control method according to the embodiment shown in Figure 1 of this disclosure;

[0046] Figure 5 is a block diagram illustrating a vehicle according to an exemplary embodiment. Detailed Implementation

[0047] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0048] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0049] The technical solution of this disclosure will be described in detail below with reference to specific embodiments.

[0050] To address the aforementioned problems, this disclosure provides a vehicle control method, apparatus, storage medium, and vehicle.

[0051] Figure 1 is a flowchart illustrating a vehicle control method according to an exemplary embodiment. As shown in Figure 1, the vehicle control method may include the following steps:

[0052] In step S101, in response to a vehicle parking command, information about the vehicle's surrounding environment is obtained.

[0053] The surrounding environment information includes at least one of parking space type, lane line type, and obstacle location. The parking space type can include horizontal parking spaces, perpendicular parking spaces, and angled parking spaces. Figure 2 is a schematic diagram of a lane line type according to an exemplary embodiment of this disclosure. As shown in Figure 2, the lane line type can include lane lines parallel to the longitudinal centerline of the vehicle and lane lines perpendicular to the longitudinal centerline of the vehicle. The obstacle location can include the location of parking space limiters, the location of the green belt beside the lane, and the location of vehicles in adjacent parking spaces, etc.

[0054] In step S102, one or more berthing data to be used are determined from the pre-stored historical berthing data based on the surrounding environment information.

[0055] The historical parking data includes historical parking environment information, parking trajectory, and the correspondence between historical parking environment information and parking trajectory. The parking environment information can be understood as the surrounding environment information of the vehicle at the time of historical parking, which may include the parking space type, lane line type, and obstacle location at the time of historical parking.

[0056] In one embodiment of this step, one or more target berthing environment information that matches the surrounding environment information can be determined from historical berthing data; the berthing trajectory corresponding to each target berthing environment information is used as berthing data to obtain one or more berthing data to be used for one or more target berthing environment information.

[0057] It should be noted that the pre-stored historical parking data can be pre-set by the parking space type or it can be automatically stored when a vehicle leaves the parking space.

[0058] In another implementation, if historical parking data is determined to exist, the historical parking data can be used as parking exit data to be used. The historical parking data refers to parking data when a vehicle enters the current parking position under manual driving conditions.

[0059] It should be noted that when a vehicle enters the current parking position, if it is determined that the vehicle is in manual driving mode, the environmental information and parking trajectory of this parking process will be automatically stored to obtain historical parking data. The current parking position can be the location information of the parking space.

[0060] In step S103, target berthing data is determined from one or more candidate berthing data based on the confidence level of each candidate berthing data.

[0061] The confidence level may include a first confidence level and a second confidence level. The implementation of this step can be shown in Figure 3, which is a flowchart illustrating a vehicle control method according to the embodiment shown in Figure 1 of this disclosure. Step S103 in Figure 1 may include:

[0062] S1031, determine the first confidence level corresponding to the waiting berthing data based on the surrounding environment information and the target berthing environment information in each waiting berthing data.

[0063] Specifically, a preset image comparison algorithm can be used to obtain the similarity between the surrounding environment information and the target berthing environment information in each berthing data to be used. This similarity is used as the first confidence level corresponding to the berthing data to be used. The preset image comparison algorithm can be a similarity calculation method such as a hash algorithm or a structural similarity algorithm in the prior art.

[0064] S1032, determine the reference berthing trajectory based on the surrounding environment information using a preset trajectory algorithm.

[0065] The surrounding environment information is input into a preset trajectory algorithm to obtain the baseline exit trajectory output by the preset trajectory algorithm. The preset trajectory algorithm can be Dijkstra's algorithm or a fast random tree algorithm.

[0066] S1033, determine the second confidence level of the historical berthing trajectory corresponding to the target berthing environment information in the pending berthing data based on the benchmark berthing trajectory.

[0067] This step involves fitting the similarity between the baseline berthing trajectory and the historical berthing trajectory corresponding to the target berthing environment information in the berthing data to be used, and determining the second confidence level based on the similarity of the fitted trajectory.

[0068] S1034, determine the target berthing data from one or more candidate berthing data based on the first confidence level and the second confidence level corresponding to each candidate berthing data.

[0069] In this step, the surrounding environment information and the preset weights of the berthing trajectory can be obtained. The first and second confidence levels corresponding to each berthing data point are weighted and summed according to the preset weights to obtain the target confidence level for that berthing data point. If the target confidence level of the berthing data point is greater than or equal to a preset confidence threshold, the berthing data point is designated as the specified berthing data point. The target berthing data point is then determined based on the specified berthing data point. If the target confidence level of each berthing data point in one or more berthing data points is less than the preset confidence threshold, a manual berthing prompt is output. After outputting this manual berthing prompt, the system can respond to a user-triggered pause command to enter manual berthing mode.

[0070] For example, if the first confidence level is A1, the second confidence level is A2, the preset weight corresponding to the first confidence level is α1, and the preset weight corresponding to the second confidence level is α2, then the target confidence level can be A = α1*A1 + α2*A2. If the preset confidence threshold is B, then if A is greater than or equal to B, the data to be used for berthing will be used as the specified berthing data.

[0071] In addition, the above-described implementation method for determining the target berthing data based on the specified berthing data can be:

[0072] If a specified berthing data point is identified, it is used as the target berthing data point. If multiple specified berthing data points are identified, the target berthing data point is determined from these multiple specified berthing data points according to a preset recommended order. This preset recommended order can be based on confidence level from highest to lowest, the generation time of the specified berthing data from most recent to oldest, or any other specified recommended order.

[0073] In step S104, the vehicle is controlled to exit from the current parking position based on the target parking data.

[0074] The target parking data includes the target parking trajectory. This step can determine the vehicle control parameters based on the target parking trajectory. These vehicle control parameters may include tire steering, wheel speed, etc. The vehicle is then controlled to complete the parking process based on these vehicle control parameters.

[0075] The above technical solution, by acquiring the vehicle's current surrounding environment information, determines the parking data to be used from pre-stored historical parking data based on the current surrounding environment information, calculates the confidence level of the parking data to be used, determines the target parking data, and automatically parks based on the target parking data, which can effectively improve the reliability of parking data, thereby effectively reducing parking errors and further improving the vehicle's safety performance and the driving experience of vehicle users.

[0076] Figure 4 is a flowchart illustrating another vehicle control method according to the embodiment shown in Figure 1 of this disclosure. As shown in Figure 4, the method further includes:

[0077] S105 acquires information about the vehicle's parking environment, including tire steering angle, wheel speed pulses, vehicle gear position, yaw angle, and inertial measurement unit information.

[0078] It should be noted that this step can be performed when the vehicle is in manual parking mode. Specifically, it can be performed when the user has triggered a pause command for automatic parking. This pause command can be triggered by clicking a preset button, key, or switch.

[0079] S106, determine the parking trajectory corresponding to the parking environment information based on the steering angle, the wheel speed pulse, the vehicle gear position, the yaw angle, and the inertial measurement unit information.

[0080] In this step, the steering angle, wheel speed pulse, vehicle gear position, yaw angle, and inertial measurement unit information corresponding to the parking environment information can be input into a preset trajectory calculation algorithm; the parking trajectory output by the preset trajectory calculation algorithm is obtained to obtain the parking trajectory corresponding to the parking environment information.

[0081] S107, store the berthing environment information and the berthing trajectory corresponding to the berthing environment information, so as to update the historical berthing data.

[0082] The historical berthing data can be data stored in a preset database that includes berthing environment information, berthing trajectory, and the correspondence between the berthing environment information and the berthing trajectory.

[0083] The above technical solution acquires parking environment information and the vehicle's steering angle, wheel speed pulse, gear position, yaw angle, and inertial measurement unit information during parking. Based on the steering angle, wheel speed pulse, gear position, yaw angle, and inertial measurement unit information, it determines the parking trajectory corresponding to the parking environment information and stores the parking environment information and the corresponding parking trajectory to update the historical parking data. This allows for timely and effective optimization of historical parking data, thereby improving the reliability of the data and providing reliable data for parking control.

[0084] The above technical solution acquires parking environment information and the vehicle's steering angle, wheel speed pulse, gear position, yaw angle, and inertial measurement unit information during parking. Based on the steering angle, wheel speed pulse, gear position, yaw angle, and inertial measurement unit information, it determines the parking trajectory corresponding to the parking environment information and stores the parking environment information and the corresponding parking trajectory to update the historical parking data. This allows for timely and effective optimization of historical parking data, thereby improving the reliability of the data and providing reliable data for parking control.

[0085] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0086] Figure 5 is a block diagram illustrating a vehicle according to an exemplary embodiment. For example, vehicle 500 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicles. Vehicle 500 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0087] As shown in Figure 5, the vehicle 500 may include various subsystems, such as an infotainment system 510, a perception system 520, a decision control system 530, a drive system 540, and a computing platform 550. The vehicle 500 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and component of the vehicle 500 can be interconnected via wired or wireless means.

[0088] In some embodiments, the infotainment system 510 may include a communication system, an entertainment system, and a navigation system, etc.

[0089] The perception system 520 may include several sensors for sensing information about the environment surrounding the vehicle 500. For example, the perception system 520 may include a global positioning system (which may be GPS, BeiDou, or other positioning systems), an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera device.

[0090] The decision control system 530 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0091] The drive system 540 may include components that provide powered motion to the vehicle 500. In one embodiment, the drive system 540 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of internal combustion engines, electric motors, and compressed air engines. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0092] Some or all of the functions of vehicle 500 are controlled by computing platform 550. Computing platform 550 may include at least one processor 551 and memory 552, and processor 551 may execute instructions 553 stored in memory 552.

[0093] Processor 551 can be any conventional processor, such as a commercially available CPU. Processors may also include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), systems-on-chips (SoCs), application-specific integrated circuits (ASICs), or combinations thereof.

[0094] The memory 552 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0095] In addition to instruction 553, memory 552 can also store data, such as road maps, route information, vehicle position, direction, speed, and other data. The data stored in memory 552 can be used by computing platform 550.

[0096] In this embodiment of the disclosure, the processor 551 may execute instructions 553 to complete all or part of the steps of the vehicle control method described above.

[0097] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the vehicle control method described above when executed by the programmable device.

[0098] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0099] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0100] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A vehicle control method, characterized in that, The method includes: in response to a vehicle parking command, acquiring surrounding environmental information of the vehicle; determining one or more pending parking data from pre-stored historical parking data based on the surrounding environmental information, wherein the historical parking data includes historical parking environment information, parking trajectory, and the correspondence between the historical parking environment information and the parking trajectory; determining target parking data from the one or more pending parking data based on the confidence level of each pending parking data; and controlling the vehicle to park from the current parking position based on the target parking data; wherein the confidence level includes a first confidence level and a second confidence level. The step of determining target berthing data from one or more candidate berthing data based on the confidence level of each candidate berthing data includes: obtaining a preset weight between the surrounding environment information and the berthing trajectory; weighting and summing the first confidence level and the second confidence level corresponding to each candidate berthing data according to the preset weight to obtain the target confidence level corresponding to the candidate berthing data; if the target confidence level of the candidate berthing data is determined to be greater than or equal to a preset confidence threshold, using the candidate berthing data as designated berthing data; and determining the target berthing data based on the designated berthing data.

2. The vehicle control method according to claim 1, characterized in that, The step of determining one or more pending berthing data from pre-stored historical berthing data based on the surrounding environment information includes: determining one or more target berthing environment information that matches the surrounding environment information from the historical berthing data; and using the berthing trajectory corresponding to each target berthing environment information as pending berthing data.

3. The vehicle control method according to claim 2, characterized in that, The method further includes: determining the first confidence level corresponding to the waiting berthing data based on the surrounding environment information and the target berthing environment information in each waiting berthing data; determining the reference berthing trajectory based on the surrounding environment information using a preset trajectory algorithm; and determining the second confidence level of the berthing trajectory corresponding to the target berthing environment information in the waiting berthing data based on the reference berthing trajectory.

4. The vehicle control method according to claim 1, characterized in that, The step of determining the target berthing data based on the specified berthing data includes: if it is determined that there is one specified berthing data, using the specified berthing data as the target berthing data; if it is determined that there are multiple specified berthing data, determining the target berthing data from the multiple specified berthing data according to a preset recommended order.

5. The vehicle control method according to claim 1, characterized in that, The method further includes: when it is determined that the target confidence level of each of the one or more pending parking data is less than a preset confidence threshold, outputting a manual parking vehicle prompt message.

6. The vehicle control method according to claim 1, characterized in that, The method further includes: acquiring vehicle parking environment information, tire steering angle, wheel speed pulse, vehicle gear position, yaw angle, and inertial measurement unit information; determining the parking trajectory corresponding to the parking environment information based on the steering angle, wheel speed pulse, vehicle gear position, yaw angle, and inertial measurement unit information; and storing the parking environment information and the parking trajectory corresponding to the parking environment information to update the historical parking data.

7. The vehicle control method according to claim 1, characterized in that, The step of determining one or more pending parking data from pre-stored historical parking data based on the surrounding environment information includes: if it is determined that historical parking data exists, using the historical parking data as the pending parking data, wherein the historical parking data is parking data of the vehicle entering the current parking position when the vehicle is manually driven.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-7.

9. A vehicle control device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-7.

10. A vehicle, characterized in that, include: The vehicle control device as described in claim 9.

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