Autopilot Method, Device, Autopilot Vehicle, and Electronic Device

Abstract

The present disclosure provides an autonomous driving method, apparatus, autonomous driving vehicle, and electronic device, relating to the field of autonomous driving, and particularly to the field of exception handling in autonomous driving. The specific implementation solution is as follows: The autonomous driving method includes: when controlling the vehicle to travel along an initial path based on initial path planning data and the vehicle has a preset exception, performing path planning according to the initial path planning data to obtain parking path planning data, where the parking path planning data is used to control the vehicle to park along a target path, and the target path is a partial path of the initial path; controlling the vehicle to park along the target path based on the parking path planning data. The present disclosure can improve the safety of an autonomous driving vehicle during the parking process.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving, and more particularly to the field of exception handling in autonomous driving. Specifically, it relates to an autonomous driving method, device, autonomous vehicle, and electronic device. Background Art

[0002] Existing autonomous vehicles typically include a fault detection system. When the autonomous vehicle is in the autonomous driving mode, the fault detection system usually detects the operating state of the autonomous vehicle. When the fault detection system detects a fault in the autonomous vehicle, to ensure driving safety, it usually controls the vehicle to stop or switches the driving mode of the vehicle to the manual takeover mode. Summary of the Invention

[0003] The present disclosure provides an autonomous driving method, device, autonomous vehicle, and electronic device.

[0004] According to a first aspect of the present disclosure, there is provided an autonomous driving method, including:

[0005] When controlling the vehicle to travel along an initial path based on initial path planning data and the vehicle has a preset exception, perform path planning according to the initial path planning data to obtain parking path planning data, where the parking path planning data is used to control the vehicle to park along a target path, and the target path is a partial path of the initial path;

[0006] Control the vehicle to park along the target path based on the parking path planning data.

[0007] According to a second aspect of the present disclosure, there is provided an autonomous driving device, including:

[0008] A path planning module, configured to perform path planning according to the initial path planning data to obtain parking path planning data when controlling the vehicle to travel along an initial path based on the initial path planning data and the vehicle has a preset exception, where the parking path planning data is used to control the vehicle to park along a target path, and the target path is a partial path of the initial path;

[0009] A control module, configured to control the vehicle to park along the target path based on the parking path planning data.

[0010] According to a third aspect of the present disclosure, there is provided an autonomous vehicle, including: the autonomous driving device described in the second aspect above.

[0011] According to a fourth aspect of the present disclosure, there is provided an electronic device, including:

[0012] At least one processor; and

[0013] A memory communicatively coupled to the at least one processor; wherein,

[0014] The memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, enable the at least one processor to execute the autonomous driving method described in the first aspect above.

[0015] According to a fifth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the autonomous driving method described in the first aspect above.

[0016] According to a sixth aspect of the present disclosure, there is provided a computer program product including a computer program which, when executed by a processor, implements the autonomous driving method described in the first aspect.

[0017] In the embodiments of the present disclosure, in the case where a preset abnormality occurs in the vehicle, path planning is performed based on initial path planning data to obtain parking path planning data, and the vehicle is controlled to park along a target path based on the parking path planning data. Since the target path is a partial path in the initial path, that is, in the case of an abnormality, the vehicle can be controlled to park along the initial path, thereby improving the safety of the autonomous driving vehicle during the parking process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings are used to better understand the solution and do not limit the present disclosure. Among them:

[0019] Figure 1 is a flowchart of an autonomous driving method provided by an embodiment of the present disclosure;

[0020] Figure 2 is a schematic diagram of an initial path in an embodiment of the present disclosure;

[0021] Figure 3 is a schematic diagram of the section where the vehicle is currently located in an embodiment of the present disclosure;

[0022] Figure 4 is a schematic diagram of a target path in an embodiment of the present disclosure;

[0023] Figure 5 is a schematic structural diagram of an autonomous driving device provided by an embodiment of the present disclosure;

[0024] Figure 6 is a schematic structural diagram of a path planning module in the autonomous driving device in an embodiment of the present disclosure;

[0025] Figure 7Block diagram of an electronic device for implementing an autonomous driving method provided by an embodiment of the present disclosure. Detailed implementation manners

[0026] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, descriptions of well-known functions and structures are omitted below for clarity and conciseness.

[0027] Please refer to Figure 1 , Figure 1 which is a schematic structural diagram of an autonomous driving method provided by the present disclosure. The autonomous driving method includes the following steps:

[0028] Step S101: When controlling the vehicle to travel along the initial path based on the initial path planning data and the vehicle has a preset abnormality, perform path planning according to the initial path planning data to obtain parking path planning data, where the parking path planning data is used to control the vehicle to park along the target path, and the target path is a partial path of the initial path;

[0029] Step S102: Control the vehicle to park along the target path based on the parking path planning data.

[0030] Among them, the vehicle can be various types of autonomous driving vehicles. For example, it can be a driverless vehicle. The initial path planning data can be path planning data generated based on the starting position and ending position input by the user. It can be understood that since the vehicle is in the autonomous driving mode, the initial path planning data can include control parameters of the vehicle at various positions in the initial path. In this way, the control unit of the vehicle can control the vehicle to travel along the initial path based on the initial path planning data.

[0031] During the driving process of the vehicle, various abnormal situations may occur to the vehicle, which can specifically include: vehicle self-abnormalities, abnormalities caused by the external environment, or abnormalities caused by the driver's driving behavior, etc. When the vehicle is in the autonomous driving mode and the vehicle's abnormality detection system detects an abnormality of the vehicle, one of the abnormal handling means is to control the vehicle to stop. In the related art, during the process of controlling the vehicle to stop due to the vehicle's abnormality, it is usually to control the vehicle to brake in a straight line. However, if the vehicle is currently driving to a curve position, during the process of controlling the vehicle to brake, due to the vehicle having a certain braking distance, it may cause the problem that the vehicle rushes out of the current lane, and further may cause the risk of vehicle collision.

[0032] Based on this, in the embodiments of the present disclosure, when a preset abnormality of the vehicle is detected, path planning is performed based on the initial path planning data to obtain parking path planning data, and then, the vehicle is controlled to park along the target path based on the parking path planning data. Since the target path is a partial path in the initial path, therefore, controlling the vehicle to park along the target path will not cause the problem that the vehicle rushes out of the current lane during the parking process. For example, please refer to Figure 2 , path AB represents the initial path, path CD represents the target path. By controlling the vehicle to park along the target path, the problem that the vehicle rushes out of the current lane at the bend can be avoided.

[0033] It can be understood that the parking path planning data includes relevant control parameters during the vehicle parking process.

[0034] The above-mentioned preset abnormality can be an abnormality of the vehicle itself, an abnormality caused by the external environment, or an abnormality caused by the driver's driving behavior, etc. For example, it can be an abnormality in the vehicle's own control system, a collision risk caused by an external obstacle, the driver not wearing a seat belt, etc.

[0035] In this embodiment, when a preset abnormality of the vehicle occurs, path planning is performed based on the initial path planning data to obtain parking path planning data, and the vehicle is controlled to park along the target path based on the parking path planning data. Since the target path is a partial path in the initial path, that is, in the case of an abnormality, the vehicle can be controlled to park along the initial path. In this way, the safety of the autonomous vehicle during the parking process can be improved.

[0036] Optionally, the performing path planning based on the initial path planning data to obtain parking path planning data includes:

[0037] Obtain the current position coordinates of the vehicle;

[0038] Determine the target state information in the initial path planning data based on the current position coordinates, where the target state information is the vehicle state information corresponding to the current position coordinates;

[0039] Perform path planning along the initial path based on the target state information to obtain the parking path planning data.

[0040] Wherein, the initial path planning data may include the vehicle state information at each position of the vehicle in the initial path, and the vehicle state information includes information such as vehicle speed, acceleration value, vehicle head orientation, position coordinates, and time when arriving at the corresponding position.

[0041] Specifically, the current position information can be obtained through the positioning module of the vehicle. Then, based on the current position information, the target vehicle state information at this position is determined. Since the target vehicle state information records information such as the current vehicle speed, acceleration value, vehicle head orientation, position coordinates, and time of the vehicle. Therefore, starting from the current position of the vehicle, with the target vehicle state information and the end vehicle speed equal to 0 as conditions, braking path planning is performed along the initial path to obtain the parking path planning data.

[0042] In this embodiment, by obtaining the target state information based on the current position of the vehicle and performing path planning along the initial path based on the target state information, in this way, the vehicle can brake along the initial path, thereby avoiding the problem that the vehicle rushes out of the current lane during the parking process.

[0043] Optionally, the step of performing path planning along the initial path based on the target state information to obtain the parking path planning data includes:

[0044] Based on the target state information and a preset deceleration, determine the target path in the initial path, and determine the vehicle state information of the vehicle in the target path to obtain the parking path planning data, where the parking path planning data includes: the vehicle state information of the vehicle in the target path, the starting point of the target path is located at the position indicated by the current position coordinates, and the vehicle speed at the end point of the target path is 0.

[0045] The above-mentioned determination of the target path based on the target state information and the preset deceleration can be: first, determine the braking distance of the vehicle based on the vehicle speed and the preset deceleration, and then, starting from the starting point, determine a section of the initial path with a length equal to the braking distance, and use the determined section as the target path.

[0046] Among them, the above-mentioned preset deceleration can be determined according to the current state of the vehicle or the driving environment where the vehicle is located.

[0047] Specifically, since the target state information includes the initial speed of the vehicle at the starting point of the target section, and the deceleration of the vehicle in the target section is the preset deceleration, therefore, according to the target state information and the preset deceleration, the speed of the vehicle at any position point in the target section can be calculated. At the same time, according to the extension direction of the target section, the vehicle head orientation at different positions can be determined, thereby obtaining the parking path planning data.

[0048] In this embodiment, a target path is determined based on the current vehicle speed and a preset deceleration, and then, based on the target state information, vehicle state information of the vehicle on the target path is determined, so as to obtain the parking path planning data.

[0049] Optionally, the parking path planning data includes: vehicle state information of at least two first position points 302 on the target path, the at least two first position points 302 are arranged at intervals along the target path, and determining the vehicle state information of the vehicle on the target path to obtain the parking path planning data includes:

[0050] Based on the target state information, the preset deceleration, and a first distance, vehicle state information of the at least two first position points 302 is calculated to obtain the parking path planning data, and the first distance includes the distance between any one of the first position points 302 and the starting point.

[0051] Among them, please refer to Figure 4 , the at least two first position points 302 may be arranged at equal intervals between the starting point and the ending point of the target path, where the distance value between any two adjacent first position points 302 may be a second distance, and the second distance value may be a relatively small distance value, for example, it may be 10 m.

[0052] In this embodiment, by making the parking path planning data include: vehicle state information of at least two first position points 302 on the target path, in this way, when the vehicle travels to the first position point 302, it can be determined whether the current state of the vehicle matches the vehicle state information of the first position point 302, and in the case of non - matching, the control parameters of the vehicle are adjusted so that the state of the vehicle matches the vehicle state information of the first position point 302, thereby realizing the control of the parking process of the vehicle based on the parking path planning data. At the same time, since only the vehicle state information of the at least two first position points 302 in the target section needs to be calculated to obtain the parking path planning data, in this way, the calculation amount in the path planning process can be reduced.

[0053] Optionally, the preset deceleration corresponds to the abnormal type of the preset abnormality, where one abnormal type corresponds to one preset deceleration;

[0054] Alternatively, the preset deceleration is determined based on the distance between the vehicle and a target obstacle, where the target obstacle includes an obstacle in front of the vehicle.

[0055] In an embodiment of the present disclosure, the preset deceleration can correspond to the type of the preset abnormality. For example, different decelerations can be set for the vehicle according to different abnormalities. In this way, when a preset abnormality occurs in the vehicle, the corresponding preset deceleration can be determined according to the type of the preset abnormality, and then the parking path planning data can be determined. For example, when the preset abnormality is that the vehicle has a collision risk, the vehicle can be controlled to enter an emergency braking mode. In this case, the preset deceleration can be a first preset value. When the preset abnormality is that the driver does not fasten the seat belt, the vehicle can be controlled to enter a gentle braking mode to improve the driving experience of the driver. In this case, the preset deceleration can be a second preset value.

[0056] In another embodiment of the present disclosure, the preset deceleration can also be determined according to the driving environment outside the vehicle. For example, by calculating the distance between an obstacle in front of the vehicle and the vehicle, when the distance value is less than a preset safety distance, the vehicle can be controlled to enter an emergency braking mode. When the distance value is greater than or equal to the preset safety distance, the vehicle can be controlled to enter the above gentle braking mode.

[0057] Wherein, the target obstacle can refer to the first vehicle in front of the vehicle, and the first preset value is greater than the second preset value.

[0058] In addition, in another embodiment of the present disclosure, the preset deceleration can also be determined by combining the type of the abnormality of the vehicle and the distance between the vehicle and the target obstacle. For example, when the distance between the vehicle and the target obstacle is less than the preset safety distance, or when the preset abnormality is that the vehicle has a collision risk, the vehicle is controlled to enter an emergency braking mode. When the distance between the vehicle and the target obstacle is greater than or equal to the preset safety distance and the preset abnormality is that the driver does not fasten the seat belt, the vehicle can be controlled to enter a gentle braking mode.

[0059] In this embodiment, by determining the preset deceleration according to the type of the abnormality of the vehicle and the distance between the vehicle and the target obstacle, it is beneficial to improve the driving experience of the driver while ensuring safe parking.

[0060] Optionally, the initial path planning data includes at least two frames of path planning sub-data obtained by performing path planning along the initial path. The at least two frames of path planning sub-data correspond to different road segments in the initial path, and the path planning sub-data is used to control the vehicle to travel in the corresponding road segment. Determining the target state information in the initial path planning data based on the current position coordinates includes:

[0061] Determine the target state information in the target path planning sub-data based on the current position coordinates, where the target path planning sub-data is the path planning sub-data corresponding to the current road section where the vehicle is located among the at least two frames of path planning sub-data, and the target path planning sub-data is used to control the vehicle to drive on the current road section where it is located.

[0062] Specifically, since an autonomous vehicle usually calculates the path planning data at a certain distance ahead based on the current position of the vehicle during the path planning process, the initial path planning data may actually only include the path planning data of the road sections that the vehicle has passed through and the path planning data at a certain distance ahead of the vehicle. When determining the parking path planning data, only the vehicle state information at the current position of the vehicle in the initial path planning data needs to be obtained, that is, the target state information. And the target path planning sub-data is used to control the vehicle to drive along the current road section where the vehicle is located. Therefore, the vehicle state information at the current position of the vehicle is recorded in the target path planning sub-data. In the embodiments of the present disclosure, the target path planning sub-data can be determined according to the current position coordinates of the vehicle, and the target state information can be determined in the target path planning sub-data.

[0063] In this embodiment, the process of obtaining the target state information is realized by determining the target state information in the target path planning sub-data based on the current position coordinates.

[0064] Optionally, the target path planning sub-data includes the vehicle state information of at least two second position points 301 in the current road section where the vehicle is located. The at least two second position points 301 are arranged at intervals along the current road section where the vehicle is located. Determining the target state information in the target path planning sub-data based on the current position coordinates includes:

[0065] Determine the target state information based on the vehicle state information of the target position point, where the target position point is the second position point 301 closest to the current position of the vehicle among the at least two second position points 301.

[0066] It can be understood that the target path planning sub-data is the path planning data used to control the vehicle to travel normally along the current section before the vehicle encounters a preset anomaly. Since the target path planning sub-data includes the vehicle state information of at least two second position points 301 in the current section where the vehicle is located, before the vehicle encounters the preset anomaly, when the vehicle travels to the second position point 301, it is possible to determine whether the current state of the vehicle matches the vehicle state information of the second position point 301, and in the case of non-matching, adjust the control parameters of the vehicle so that the state of the vehicle matches the vehicle state information of the second position point 301, thereby realizing the normal driving of the vehicle along the current section based on the target path planning sub-data.

[0067] Please refer to Figure 2 , section EF is the current section where the vehicle is located, and position point C is the position where the vehicle encounters a preset anomaly, that is, the current position of the vehicle. Since the target path planning sub-data includes the vehicle state information of each second position point 301 in section EF, the vehicle state information of position point C can be calculated based on the target path planning sub-data. After calculating the vehicle state information of the vehicle at position point C, a parking path planning can be performed with position point C as the starting point, position point D as the ending point, the vehicle state information of position point C as the initial state of the vehicle, and the vehicle speed at position point D being 0 as the condition, to obtain the parking path planning data.

[0068] Specifically, please refer to Figure 4 , since it is necessary to control the vehicle to park along section CD, several first position points 302 can be inserted into section CD, and then the vehicle state information of each first position point 302 during the parking phase can be calculated respectively. In this way, the vehicle can be controlled to park along section CD based on the vehicle state information of the first position point 302.

[0069] Among them, the vehicle state information of the above-mentioned second position point 301 is the path planning data used to control the vehicle to travel normally along section EF during the normal driving phase of the vehicle, while the vehicle state information of the first position point 302 is the path planning data used to control the vehicle to park along section CD during the parking phase after the vehicle encounters a preset anomaly. Since section CD is a section within section EF, section CD includes both the second position points 301 during the normal driving phase and the first position points 302 during the parking phase. In section CD, the position of the second position point 301 can coincide with the position of the first position point 302. In addition, in section CD, the position of the second position point 301 can also be offset relative to the position of the first position point 302.

[0070] The above-mentioned first position point 302 and second position point 301 only indicate one position coordinate in the road section. The definitions of the first position point 302 and the second position point 301 in the present disclosure are only used to distinguish the path planning data in different stages in the same road section, and distinguish the target path planning sub-data from the parking path planning data through the first position point 302 and the second position point 301.

[0071] Specifically, since the vehicle state information at the target position point includes the vehicle speed, acceleration, and position coordinate of the vehicle at the target position point, therefore, the vehicle state information of the vehicle's current position can be calculated according to the distance between the target position point and the vehicle's current position, so as to obtain the target state information. In addition, since the distance between the target position point and the vehicle's current position is relatively close, the vehicle state information of the target position point can also be directly determined as the target state information. For example, several second position points 301 can be pre-set in the road section EF, and then, the vehicle state information of each second position point 301 is determined respectively, so as to obtain the target path planning sub-data.

[0072] In this embodiment, by determining the target state information based on the vehicle state information at the target position point, since the distance between the target position point and the vehicle's current position is relatively close, the accuracy of the determined target state information can be improved.

[0073] Please refer to Figure 5 An automatic driving device 200 provided by the present disclosure, the automatic driving device 200 includes:

[0074] A path planning module 201, configured to perform path planning according to the initial path planning data to obtain parking path planning data when controlling the vehicle to travel along the initial path based on the initial path planning data and the vehicle has a preset abnormality, wherein the parking path planning data is used to control the vehicle to park along the target path, and the target path is a partial path in the initial path;

[0075] A control module 202, configured to control the vehicle to park along the target path based on the parking path planning data.

[0076] Optionally, the initial path planning data includes the vehicle state information of the vehicle in the initial path, please refer to Figure 6 , the path planning module 201 includes:

[0077] An acquisition sub-module 2011, configured to acquire the current position coordinate of the vehicle;

[0078] Determination sub-module 2012, configured to determine target state information in the initial path planning data based on the current position coordinates, where the target state information is vehicle state information corresponding to the current position coordinates;

[0079] Path planning sub-module 2013, configured to perform path planning along the initial path based on the target state information to obtain the parking path planning data.

[0080] Optionally, the path planning sub-module 2013 is specifically configured to determine the target path in the initial path based on the target state information and a preset deceleration, and determine vehicle state information of the vehicle in the target path to obtain the parking path planning data;

[0081] Wherein, the parking path planning data includes: vehicle state information of the vehicle in the target path, the starting point of the target path is located at the position indicated by the current position coordinates, and the vehicle speed at the end point of the target path is 0.

[0082] Optionally, the parking path planning data includes: vehicle state information of at least two first position points 302 in the target path, the at least two first position points 302 are arranged at intervals along the target path, and the path planning sub-module 2013 is specifically configured to calculate vehicle state information of the at least two first position points 302 based on the target state information, the preset deceleration, and a first distance to obtain the parking path planning data, where the first distance includes the distance between any one of the first position points 302 and the starting point.

[0083] Optionally, the preset deceleration corresponds to the abnormal type of the preset abnormality, where one abnormal type corresponds to one preset deceleration;

[0084] Alternatively, the preset deceleration is determined based on the distance between the vehicle and a target obstacle, where the target obstacle includes an obstacle in front of the vehicle.

[0085] Optionally, the initial path planning data includes at least two frames of path planning sub-data obtained by performing path planning along the initial path, the at least two frames of path planning sub-data correspond to different sections in the initial path, and the path planning sub-data is used to control the vehicle to travel in the corresponding section. The determination sub-module 2012 is specifically configured to determine target state information in the target path planning sub-data based on the current position coordinates, where the target path planning sub-data is the path planning sub-data corresponding to the section where the vehicle is currently located among the at least two frames of path planning sub-data.

[0086] Optionally, the target path planning sub-data includes vehicle state information of at least two second position points 301 in the current section where the vehicle is located. The at least two second position points 301 are arranged at intervals along the current section where the vehicle is located. The determining sub-module 2012 is specifically configured to determine the target state information based on the vehicle state information of the target position point, where the target position point is the second position point 301 closest to the current position of the vehicle among the at least two second position points 301.

[0087] It should be noted that the autonomous driving device 200 provided in this embodiment can implement all the technical solutions of the above-mentioned autonomous driving method embodiment, and thus can at least achieve all the above technical effects, which will not be elaborated here.

[0088] In the technical solutions of the present disclosure, the acquisition, storage, and application of user personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0089] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0090] Figure 7 A schematic block diagram of an exemplary electronic device 400 that can be used to implement the embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0091] As Figure 7 shown, the electronic device 400 includes a computing unit 401, which can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 402 or the computer program loaded from the storage unit 408 into the random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0092] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disc, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0093] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated 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 401 executes the various methods and processes described above, such as the autonomous driving method. For example, in some embodiments, the autonomous driving method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the autonomous driving method described above are executed. Alternatively, in other embodiments, the computing unit 401 can be configured to execute the autonomous driving method in any other suitable way (e.g., by means of firmware).

[0094] The various embodiments of the systems and technologies described above in this article 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 can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] The program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, a special purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0096] In the context of the present disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, 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 a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] In order to provide interaction with a user, the systems and techniques described herein may be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, speech input, or tactile input).

[0098] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected with each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

[0099] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, a server of a distributed system, or a server incorporating blockchain.

[0100] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this is not limited herein.

[0101] The above specific implementation manners do not constitute a limitation on the protection scope 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 principle of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. An autonomous driving method, comprising: When controlling the vehicle to travel along an initial path based on initial path planning data and a preset abnormality occurs to the vehicle, performing path planning according to the initial path planning data to obtain parking path planning data, wherein the parking path planning data is used to control the vehicle to park along a target path, and the target path is a partial path in the initial path; Controlling the vehicle to park along the target path based on the parking path planning data; The initial path planning data includes at least two frames of path planning sub-data obtained by performing path planning along the initial path. The at least two frames of path planning sub-data correspond to different road segments in the initial path, and the path planning sub-data is used to control the vehicle to travel in the corresponding road segment. The performing path planning according to the initial path planning data to obtain parking path planning data includes: Obtaining the current position coordinates of the vehicle; Determining target state information in target path planning sub-data based on the current position coordinates, wherein the target path planning sub-data is the path planning sub-data corresponding to the road segment where the vehicle is currently located among the at least two frames of path planning sub-data, and the target state information is the vehicle state information corresponding to the current position coordinates; Performing path planning along the initial path based on the target state information to obtain the parking path planning data.

2. The method according to claim 1, wherein The performing path planning along the initial path based on the target state information to obtain the parking path planning data includes: Determining the target path in the initial path based on the target state information and a preset deceleration, and determining the vehicle state information of the vehicle in the target path to obtain the parking path planning data; Wherein, the parking path planning data includes: the vehicle state information of the vehicle in the target path, the starting point of the target path is located at the position indicated by the current position coordinates, and the vehicle speed at the end point of the target path is 0.

3. The method according to claim 2, wherein The parking path planning data includes: the vehicle state information of at least two first position points in the target path, and the at least two first position points are arranged at intervals along the target path. The determining the vehicle state information of the vehicle in the target path to obtain the parking path planning data includes: Calculating the vehicle state information of the at least two first position points based on the target state information, the preset deceleration and a first distance to obtain the parking path planning data, and the first distance includes the distance between any one of the first position points and the starting point.

4. The method according to claim 2, wherein, The preset deceleration corresponds to the type of the preset abnormality, wherein one type of abnormality corresponds to one preset deceleration; Alternatively, the preset deceleration is determined based on the distance between the vehicle and a target obstacle, wherein the target obstacle includes an obstacle in front of the vehicle.

5. The method according to claim 1, wherein The target path planning sub-data includes vehicle state information of at least two second position points in the section where the vehicle is currently located. The at least two second position points are arranged at intervals along the section where the vehicle is currently located. Determining the target state information from the target path planning sub-data based on the current position coordinates includes: Determining the target state information based on the vehicle state information of the target position point, where the target position point is the second position point closest to the current position of the vehicle among the at least two second position points.

6. An autonomous driving device, comprising: A path planning module, configured to perform path planning according to the initial path planning data to obtain parking path planning data when controlling the vehicle to travel along the initial path based on the initial path planning data and when the vehicle has a preset abnormality, where the parking path planning data is used to control the vehicle to park along a target path, and the target path is a partial path of the initial path; A control module, configured to control the vehicle to park along the target path based on the parking path planning data; The initial path planning data includes at least two frames of path planning sub-data obtained by performing path planning along the initial path. The at least two frames of path planning sub-data correspond to different sections of the initial path, and the path planning sub-data is used to control the vehicle to travel in the corresponding section. The path planning module includes: An acquisition sub-module, configured to acquire the current position coordinates of the vehicle; A determination sub-module, configured to determine target state information from the target path planning sub-data based on the current position coordinates, where the target path planning sub-data is the path planning sub-data corresponding to the section where the vehicle is currently located among the at least two frames of path planning sub-data, and the target state information is the vehicle state information corresponding to the current position coordinates; A path planning sub-module, configured to perform path planning along the initial path based on the target state information to obtain the parking path planning data.

7. The apparatus according to claim 6, wherein, The path planning sub-module is specifically configured to determine the target path in the initial path based on the target state information and a preset deceleration, and determine the vehicle state information of the vehicle in the target path to obtain the parking path planning data; wherein the parking path planning data includes: the vehicle state information of the vehicle in the target path, the starting point of the target path is located at the position indicated by the current position coordinates, and the vehicle speed at the end point of the target path is 0.

8. The apparatus according to claim 7, wherein The parking path planning data includes: the vehicle state information of at least two first position points in the target path. The at least two first position points are arranged at intervals along the target path. The path planning sub-module is specifically configured to calculate the vehicle state information of the at least two first position points based on the target state information, the preset deceleration, and a first distance to obtain the parking path planning data, and the first distance includes the distance between any one of the first position points and the starting point.

9. The apparatus according to claim 7, wherein, The preset deceleration corresponds to the abnormal type of the preset abnormality, where one abnormal type corresponds to one preset deceleration; Alternatively, the preset deceleration is determined based on the distance between the vehicle and the target obstacle, where the target obstacle includes the obstacle in front of the vehicle.

10. The apparatus according to claim 6, wherein, The target path planning sub-data includes the vehicle state information of at least two second position points in the section where the vehicle is currently located, and the at least two second position points are arranged at intervals along the section where the vehicle is currently located. The determining sub-module is specifically configured to determine the target state information based on the vehicle state information of the target position point, where the target position point is the second position point closest to the current position of the vehicle among the at least two second position points.

11. An autonomous vehicle, comprising: The automatic driving device according to any one of claims 6 to 10.

12. 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 so that the at least one processor can execute the automatic driving method according to any one of claims 1-5.

13. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to execute the automatic driving method according to any one of claims 1-5.

14. A computer program product, comprising a computer program which, when executed by a processor, implements the automatic driving method according to any one of claims 1-5.

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