CN114655250A

CN114655250A - Data generation method and device for automatic driving

Info

Publication number: CN114655250A
Application number: CN202210346190.7A
Authority: CN
Inventors: 代东; 刘盛翔; 赵军
Original assignee: Apollo Zhilian Beijing Technology Co Ltd
Current assignee: Apollo Zhilian Beijing Technology Co Ltd
Priority date: 2022-03-31
Filing date: 2022-03-31
Publication date: 2022-06-24

Abstract

The disclosure provides a data generation method and device for automatic driving, relates to the technical field of data processing, and particularly relates to automatic driving. The method of the present disclosure comprises: acquiring historical driving data of at least one test vehicle when the test vehicle runs in a plurality of geographic areas; for each of a plurality of geographic areas, determining at least one takeover type corresponding to the geographic area from historical driving data, wherein the corresponding at least one takeover type is the at least one takeover type to which a driving takeover event occurring on at least one test vehicle within the geographic area belongs; generating correspondence data for subsequent generation of an autonomous driving maneuver, wherein the correspondence data includes each of the plurality of geographic areas and its corresponding at least one takeover type.

Description

Data generation method and device for automatic driving

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to an automatic driving method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product.

Background

The automatic driving utilizes sensor technology, signal processing technology, communication technology, computer technology and the like, and various vehicle-mounted sensors such as vision, laser radar, ultrasonic sensors, microwave radar, global positioning system, odometer, magnetic compass and the like are integrated to identify the environment and the state of the automobile. The automatic driving system analyzes and judges according to the acquired road information, the information of the traffic signal, the vehicle position and the obstacle information, and controls the vehicle steering and the driving speed according to the judgment result.

In an actual driving process, due to the complex road conditions or environments in a geographic area, an automatic driving system may not be able to generate a more optimal driving strategy. Therefore, how to adaptively generate driving strategies according to characteristics of different geographic areas has become an important research direction in the field.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.

Disclosure of Invention

The present disclosure provides a data generation method and apparatus for autonomous driving, an electronic device, a computer-readable storage medium, and a computer program product.

According to an aspect of the present disclosure, there is provided a data generation method for autonomous driving, including: acquiring historical driving data of at least one test vehicle when the test vehicle runs in a plurality of geographic areas; for each of a plurality of geographic areas, determining at least one takeover type corresponding to the geographic area from historical driving data, wherein the corresponding at least one takeover type is the at least one takeover type to which a driving takeover event occurring on at least one test vehicle within the geographic area belongs; generating correspondence data for subsequent generation of an autonomous driving maneuver, wherein the correspondence data includes each of the plurality of geographic areas and its corresponding at least one takeover type.

According to another aspect of the present disclosure, there is provided a data generating apparatus for automatic driving, including: an acquisition unit configured to acquire historical driving data of at least one test vehicle while traveling in a plurality of geographical areas; a first determining unit configured to determine, for each of a plurality of geographic areas, at least one takeover type corresponding to the geographic area from historical driving data, wherein the corresponding at least one takeover type is at least one takeover type to which a driving takeover event occurring on at least one test vehicle within the geographic area belongs; a first generating unit configured to generate correspondence data for subsequent generation of an autonomous driving maneuver, wherein the correspondence data comprises each of the plurality of geographic areas and its corresponding at least one takeover type.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method described above.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the above-described method.

According to another aspect of the disclosure, a computer program product is provided, comprising a computer program, wherein the computer program realizes the above-described method when executed by a processor.

In accordance with one or more embodiments of the present disclosure, historical driving data for an autonomous vehicle used for road testing is first obtained, and then a takeover type for each of a plurality of geographic areas is determined based on the data. Since the driver (or tester) can take over the automatic driving system selectively according to the actual road condition or environment of each geographic area, the determined taking over type is an important index for measuring the road condition or driving environment in the geographic area. Therefore, when the subsequent vehicle travels to a certain geographic area, the driving strategy can be automatically generated according to the takeover type of the geographic area, so that the generated driving strategy is more suitable for the regional characteristics of the geographic area.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;

FIG. 2 shows a flow diagram of a data generation method for autonomous driving according to an embodiment of the present disclosure;

FIG. 3 shows a flow chart of a data generation method for autonomous driving according to an embodiment of the disclosure;

FIG. 4 shows a flow chart of a method of determining at least one takeover type in accordance with an embodiment of the present disclosure;

fig. 5 shows a block diagram of a data generation apparatus for automatic driving according to an embodiment of the present disclosure;

fig. 6 shows a block diagram of a data generation apparatus for automatic driving according to an embodiment of the present disclosure;

FIG. 7 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", and the like to describe various elements is not intended to limit the positional relationship, the temporal relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.

The terminology used in the description of the various examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an example system 100 in which various methods and apparatus described herein may be implemented in accordance with embodiments of the present disclosure. Referring to fig. 1, the system 100 includes a motor vehicle 110, a server 120, and one or more communication networks 130 coupling the motor vehicle 110 to the server 120.

In embodiments of the present disclosure, motor vehicle 110 may include a computing device and/or be configured to perform a method in accordance with embodiments of the present disclosure.

Server 120 may run one or more services or software applications that enable methods of generating data for autonomous driving and generating autonomous driving strategies. In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user of motor vehicle 110 may, in turn, utilize one or more client applications to interact with server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

In some embodiments, server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from motor vehicle 110. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of motor vehicle 110.

Network 130 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, one or more networks 110 may be a satellite communication network, a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (including, e.g., bluetooth, WiFi), and/or any combination of these and other networks.

The system 100 may also include one or more databases 150. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 150 may be used to store information such as audio files and video files. The data store 150 may reside in various locations. For example, the data store used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data store 150 may be of different types. In certain embodiments, the data store used by the server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to the command.

In some embodiments, one or more of the databases 150 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or conventional stores supported by a file system.

Motor vehicle 110 may include sensors 111 for sensing the surrounding environment. The sensors 111 may include one or more of the following sensors: a vision camera, an infrared camera, an ultrasonic sensor, a millimeter wave radar, and a laser radar (LiDAR). Different sensors may provide different detection accuracies and ranges. The camera may be mounted in front of, behind, or otherwise on the vehicle. The visual camera may capture conditions inside and outside the vehicle in real time and present to the driver and/or passengers. In addition, by analyzing the picture captured by the visual camera, information such as traffic light indication, intersection situation, other vehicle running state, and the like can be acquired. The infrared camera can capture objects under night vision conditions. The ultrasonic sensors can be arranged around the vehicle and used for measuring the distance between an object outside the vehicle and the vehicle by utilizing the characteristics of strong ultrasonic directionality and the like. The millimeter wave radar may be installed in front of, behind, or other positions of the vehicle for measuring the distance of an object outside the vehicle from the vehicle using the characteristics of electromagnetic waves. The lidar may be mounted in front of, behind, or otherwise of the vehicle for detecting object edges, shape information, and thus object identification and tracking. The radar apparatus can also measure a speed variation of the vehicle and the moving object due to the doppler effect.

Motor vehicle 110 may also include a communication device 112. The communication device 112 may include a satellite positioning module capable of receiving satellite positioning signals (e.g., beidou, GPS, GLONASS, and GALILEO) from the satellites 141 and generating coordinates based on these signals. The communication device 112 may also include modules to communicate with a mobile communication base station 142, and the mobile communication network may implement any suitable communication technology, such as current or evolving wireless communication technologies (e.g., 5G technologies) like GSM/GPRS, CDMA, LTE, etc. The communication device 112 may also have a Vehicle-to-Vehicle (V2X) networking or Vehicle-to-Vehicle (V2X) module configured to enable, for example, Vehicle-to-Vehicle (V2V) communication with other vehicles 143 and Vehicle-to-Infrastructure (V2I) communication with the Infrastructure 144. Further, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to a smartphone, tablet, or wearable device such as a watch), for example, via wireless local area network using IEEE802.11 standards or bluetooth. Motor vehicle 110 may also access server 120 via network 130 using communication device 112.

Motor vehicle 110 may also include a control device 113. The control device 113 may include a processor, such as a Central Processing Unit (CPU) or a Graphics Processing Unit (GPU), or other special purpose processor, etc., in communication with various types of computer-readable storage devices or media. The control device 113 may include an autopilot system for automatically controlling various actuators in the vehicle. The autopilot system is configured to control a powertrain, steering system, and braking system, etc., of a motor vehicle 110 (not shown) via a plurality of actuators in response to inputs from a plurality of sensors 111 or other input devices to control acceleration, steering, and braking, respectively, without human intervention or limited human intervention. Part of the processing functions of the control device 113 may be realized by cloud computing. For example, some processing may be performed using an onboard processor while other processing may be performed using the computing resources in the cloud. The control device 113 may be configured to perform a method according to the present disclosure. Furthermore, the control apparatus 113 may be implemented as one example of a computing device on the motor vehicle side (client) according to the present disclosure.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.

The present disclosure first provides a data generation method for automatic driving, and fig. 2 shows a flowchart of a data generation method 200 for automatic driving according to an embodiment of the present disclosure. As shown in fig. 2, the method 200 includes:

step 201, obtaining historical driving data of at least one test vehicle when the test vehicle runs in a plurality of geographic areas;

step 202, for each of a plurality of geographic areas, determining at least one takeover type corresponding to the geographic area according to historical driving data, wherein the corresponding at least one takeover type is at least one takeover type to which a driving takeover event occurring on at least one test vehicle in the geographic area belongs;

step 203, generating corresponding relation data for subsequently generating an automatic driving strategy, wherein the corresponding relation data comprises each geographic area in the plurality of geographic areas and at least one takeover type corresponding to the geographic area.

According to the method of an embodiment of the present disclosure, historical driving data of an autonomous vehicle for road testing is first obtained, and then a takeover type for each of a plurality of geographic areas is determined based on the data. Since the driver (or tester) can selectively take over the automatic driving system according to the actual road condition or environment of each geographic area, the determined take-over type is an important index for measuring the road condition or driving environment in the geographic area. Therefore, when the subsequent vehicle travels to a certain geographic area, the driving strategy can be automatically generated according to the takeover type of the geographic area, so that the generated driving strategy is more suitable for the regional characteristics of the geographic area.

With the development of automated driving technology, the industry has entered an era of exploring business model landings, and many companies or organizations have dispatched test vehicles to perform test driving in a specific geographical area. The driving vehicle may be, for example, the motor vehicle 110 shown in fig. 1. During the test driving, the driving data generated by the test vehicle terminal is uploaded to the server 120 for subsequent analysis by the server 120 and generation of relevant data, which is used to generate a driving strategy for the target vehicle.

In step 201, at least one dispatched test vehicle may be driven for testing in a plurality of geographic areas. The test vehicle is an autonomous vehicle. The test driving process is mainly an unmanned driving process, and the vehicle is provided with a safety driver and can take over an automatic driving system of the vehicle when active driving is required. The historical driving data is data recording relevant parameters of the vehicle, including, but not limited to, a running speed, an acceleration, a rotation angle of a steering wheel, an accelerator opening degree, and the like of the vehicle.

The historical driving data can be uploaded by means of a test case (case) which is only used for recording the historical driving data generated when the test vehicle travels a small part of a specific journey, so that the server can only acquire a small part of the historical driving data of the whole journey of the test vehicle at a time, and the total amount of data transmission is greatly reduced. An additional advantage of uploading historical driving data by way of a test case (case) is that the test case may be uploaded to the database 150 of the server 120 at any time, thereby enabling the server 120 to perform real-time updates and generate driving strategies each time using the updated data. The above-mentioned historical driving data may be log (log) data output by an automatic driving system of the vehicle.

The driving takeover event refers to that a tester in the test vehicle takes over the automatic driving system of the vehicle and operates the vehicle, namely the vehicle is converted from the unmanned driving mode to the active driving mode. The driving takeover event includes a plurality of takeover types that are classified according to the particular mechanism by which the tester is actively operating the vehicle. For example, the take-over types include a throttle take-over (i.e., the tester controls the throttle of the vehicle), a brake take-over (i.e., the tester controls the brakes of the vehicle), a steering wheel take-over (i.e., the tester controls the steering wheel of the vehicle), and so forth. Because the related mechanisms are operated manually, the automatic driving system can judge the occurrence of the driving takeover event, and the output log data can record the occurrence time of the driving takeover event, however, the log data cannot actively judge the takeover type of the driving takeover event. In step 202, a takeover type for each driving takeover event may be determined based on historical driving data, thereby determining a takeover type for all driving takeover events occurring within the geographic area. At least a portion of all the determined takeover types are then determined as at least one takeover type corresponding to the geographic area.

In step 203, the correspondence data may be used to represent each of a plurality of geographic areas and a respective at least one takeover type. It will be appreciated that each geographical area may correspond to two or more takeover types, i.e. a plurality of takeover types of driving takeover events occur during the driving of the test vehicle within that geographical area. For example, the plurality of geographic areas may include 3 geographic areas, and the correspondence data may include: the connection type of the area 1 is a steering wheel connection; the connection type of the area 2 is a brake connection pipe; the types of connections for zone 3 are both steering wheel connections and throttle connections, etc. At least one of the determined correspondence data take over types is an important reference indicator for determining characteristics of the geographic area, which may subsequently be used to generate a driving strategy.

Fig. 3 shows a flow diagram of a data generation method 300 for autonomous driving, the method 300 for using correspondence data to provide a driving strategy for vehicles other than the test vehicle after determining the correspondence data, according to an embodiment of the disclosure. As shown in fig. 3, the method 300 includes:

step 301, determining a target geographic area where a target vehicle is located, wherein the target geographic area is one of a plurality of geographic areas;

step 302, determining at least one takeover type corresponding to the target geographic area according to the corresponding relation data; and

step 303, generating an autonomous driving maneuver for the target vehicle based at least on the at least one takeover type corresponding to the target geographic area.

In step 301, the target vehicle may also be the motor vehicle 110 shown in fig. 1, which may be capable of receiving satellite positioning signals from the satellites 141 via the communication means 112 and determining a target geographical area in which the target vehicle is located based on these signals.

In step 302, the correspondence data may be the correspondence data determined according to the method 200 shown in fig. 2. These correspondence data may be stored in a relational database 150. After the relevant server 120 for generating the driving strategy receives the request for generating the driving strategy of the target vehicle and determines the target geographic area, the server 120 queries the corresponding relationship data in the database to obtain at least one takeover type corresponding to the target geographic area.

As described above, since the type of takeover is an important reference indicator for determining the characteristics of the geographical area, the autonomous driving maneuver may be generated for the target vehicle according to at least one type of takeover corresponding to the target geographical area in step 303. The generated autonomous driving strategy will be adapted to the road conditions and the environment of the target geographical area. For example, when at least one of the takeover types includes a brake takeover, which indicates that the road condition of the target geographic area is poor (e.g., includes more roadblocks or the road is narrower), a strategy for reducing the driving speed of the autonomous driving may be generated to avoid a traffic accident of the target vehicle. For another example, when at least one of the takeover types includes steering wheel takeover, which indicates that there are more turning roads in the target geographic area, the speed limit may be reduced, and the overall control of the vehicle may be adjusted to a more conservative level to ensure safety. The subsequent vehicle may be automatically driven according to the generated driving strategy.

Fig. 4 illustrates a flow chart of a method 400 of determining at least one takeover type according to an embodiment of the present disclosure, as illustrated in fig. 4, the method 400 including:

step 401, selecting first driving data at a first time before a driving takeover event occurs and second driving data at a second time after the driving takeover event occurs from historical driving data, wherein the first driving data and the second driving data are data of the same parameter type;

step 402, determining a difference between the first driving data and the second driving data;

and step 403, at least in response to the difference value being larger than the preset difference value threshold value, judging that the driving takeover event belongs to a pending takeover type related to the parameter type.

Step 404, determining the occurrence frequency of the driving takeover event belonging to the type to be taken over;

and 405, in response to the occurrence times being greater than the preset threshold times, determining that at least one takeover type comprises a to-be-determined takeover type.

As described above, the autonomous driving system of the vehicle may identify a driving takeover event, the time of occurrence of which will be recorded in the historical driving data. Before step 401, the test examples uploaded by the test vehicle may be first screened, so that only test examples containing recorded driving takeover events are selected. The test examples comprise a group of time series data, namely relevant parameters corresponding to a plurality of time points, wherein the plurality of time points comprise the occurrence time of the driving takeover event. In step 401, the first time point is a time point before the occurrence time point, and the second time point is a time point after the occurrence time point, that is, the historical driving data corresponding to the first time point and the second time point represents the relevant parameters of the vehicle after the occurrence of the driving takeover event. The type of take-over may then be determined based on the change in the above parameters.

In some embodiments, the time difference between the first time and the time at which the driving takeover event occurs is less than a preset time threshold, and the time difference between the second time and the time at which the driving takeover event occurs is less than the preset time threshold. The preset time length threshold value can be set to be 2s, for example, that is, the time difference between the first moment and the occurrence moment is less than 2s, so that the first driving data and the second driving data can accurately reflect the change of the vehicle parameters before and after the driving takeover event.

In step 403, if it is determined that the difference is greater than the preset difference threshold, it indicates that the relevant parameters of the vehicle have changed significantly, so as to determine that the to-be-determined connection pipe type related to the parameter type has occurred.

The method of the embodiment judges the takeover type through two driving data before and after the occurrence moment of the driving takeover event, and the judging process is simple and accurate.

In some embodiments, the first driving data includes a first driving speed, and the second driving data includes a second driving speed, and when the first driving speed is greater than the second driving speed and the difference value is greater than a preset speed difference threshold value, it may be determined that the vehicle speed significantly decreases after the driving takeover event occurs, so that it may be determined that the driving takeover event belongs to brake takeover. In other embodiments, the first driving data and the second driving data may also be braking percentages, and subsequently, whether the takeover type is braking takeover is determined by determining a difference value between the two braking percentages.

In some embodiments, the first driving data includes a first travel acceleration and the second driving data includes a second travel acceleration, and then the second travel acceleration is greater than a preset addition when the second travel acceleration is greater than the preset additionA speed threshold value and the difference value is larger than a preset acceleration difference threshold value (2 m/s)²) In the process, the acceleration of the vehicle can be obviously increased after the driving takeover event occurs, and the driving takeover event can be judged to belong to the accelerator takeover. In other embodiments, the first driving data and the second driving data may also be percentage of throttle, and then whether the take-over type is the throttle take-over is determined by determining the difference between the two percentage of throttle.

In some embodiments, the first driving data includes a first steering wheel angle, and the second driving data includes a second steering wheel angle, then when the difference is greater than a preset angle difference threshold (e.g., 10 °), it may be determined that the steering wheel is significantly rotated after the driving takeover event occurs, and thus it may be determined that the driving takeover event belongs to steering wheel takeover.

It should be added that the various threshold values listed in the above embodiments are only exemplary, and specific values thereof can be determined according to relevant experiments.

In the case of a large geographical area, there may be a plurality of take-over types of driving take-over events in each geographical area, in which case determining the pending take-over type for all driving take-over events occurring within a geographical area as the take-over type corresponding to that geographical area is insufficient to distinguish each geographical area from other geographical areas. Therefore, in step 404, after determining the pending takeover type of the driving takeover event, the number of occurrences of the driving takeover event that is the same as the pending takeover type may be further determined.

In step 405, if the number of occurrences in step 404 exceeds the threshold number of times, determining that at least one takeover type corresponding to the geographic area includes the above-mentioned to-be-determined takeover type; and if the occurrence times are lower than the threshold times, determining that at least one takeover type corresponding to the geographic area does not comprise the to-be-determined takeover type. The threshold number of times may be set to 10 times, 20 times, 50 times, and the like, and may be specifically determined according to the size of the geographic area and the number of uploaded test examples.

In some embodiments, after determining at least one takeover type corresponding to the target geographic area, further comprising: and generating takeover prompt information according to at least one takeover type corresponding to the target geographic area. For example, in the case that it is determined that at least one takeover type includes brake takeover, it indicates that the road conditions in the target geographic area are complex and there are likely to be many road blocks, and then a prompt message prompting the driver to take care of the road blocks may be generated at this time.

In some embodiments, determining from the historical driving data at least one type of takeover corresponding to the geographic area further comprises: and inputting historical driving data into the trained prediction classification model to obtain at least one takeover type. It will be appreciated that whilst in the above embodiments the type of take-over of the driving take-over event is determined by the difference between the first and second driving data, in other embodiments the type of take-over may be determined from historical driving data by means of, for example, machine learning. Specifically, the predictive classification model may be trained using sample data that includes historical driving data and its corresponding determined takeover types. Subsequently, historical driving data of the taking-over type to be determined can be input into the trained prediction classification model, and at least one taking-over type is output. In this way, at least one takeover type is determined, and the result is quicker and more accurate.

According to another aspect of the present disclosure, there is also provided a data generating apparatus for automatic driving, and fig. 5 shows a block diagram of a data generating apparatus 500 for automatic driving according to an embodiment of the present disclosure. As shown in fig. 5, the apparatus 500 includes: an obtaining unit 510 configured to obtain historical driving data of at least one test vehicle while traveling in a plurality of geographic areas; a first determining unit 520 configured to determine, for each of a plurality of geographic areas, at least one takeover type corresponding to the geographic area from historical driving data, wherein the corresponding at least one takeover type is at least one takeover type to which a driving takeover event occurring on at least one test vehicle within the geographic area belongs; a first generating unit 530 configured to generate correspondence data for subsequently generating an automatic driving strategy, wherein the correspondence data comprises each of the plurality of geographical areas and its corresponding at least one takeover type.

Fig. 6 shows a block diagram of a data generation apparatus 600 for automatic driving according to an embodiment of the present disclosure. As shown in fig. 6, the apparatus 600 additionally includes, compared to the apparatus 500: a second determining unit 640, configured to determine a target geographic area where the target vehicle is located, where the target geographic area is one geographic area of a plurality of geographic areas; a third determining unit 650 configured to determine at least one takeover type corresponding to the target geographical area according to the correspondence data; and a second generating unit configured to generate an autonomous driving strategy for the target vehicle at least according to the at least one takeover type corresponding to the target geographic area.

In some embodiments, as shown in fig. 6, the first determining unit 620 includes: the selection module 621 is configured to select, from the historical driving data, first driving data at a first time before a time when a driving takeover event occurs and second driving data at a second time after the time when the driving takeover event occurs, where the first driving data and the second driving data are data of the same parameter type; a first determination module 622 configured to determine a difference between the first driving data and the second driving data; and a second determination module 623 configured to determine that the driving takeover event belongs to a pending takeover type associated with the parameter type in response to at least the difference being greater than a preset difference threshold.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.

According to an embodiment of the present disclosure, there is also provided an electronic device, a readable storage medium, and a computer program product.

Referring to fig. 7, a block diagram of a structure of an electronic device 700, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)702 or a computer program loaded from a storage unit 707 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

A number of components in the electronic device 700 are connected to the I/O interface 705, including: an input unit 706, an output unit 707, a storage unit 707, and a communication unit 709. The input unit 706 may be any type of device capable of inputting information to the electronic device 700, and the input unit 706 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote controller. Output unit 707 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 707 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 709 allows the electronic device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.

Computing unit 701 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 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, and so forth. The calculation unit 701 executes the respective methods and processes described above, such as the data generation method for automatic driving. For example, in some embodiments, the data generation method for autonomous driving may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 707. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 700 via the ROM 702 and/or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the data generation method for autonomous driving described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured by any other suitable means (e.g., by means of firmware) to perform the data generation method for autonomous driving.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

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, special purpose computer, or other programmable data processing apparatus, 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 performed. 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.

In the context of this 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. The 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, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, 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.

To provide for interaction with a user, the systems and techniques described here can 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 a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (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 here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.

Claims

1. A data generation method for autonomous driving, comprising:

acquiring historical driving data of at least one test vehicle when the test vehicle runs in a plurality of geographic areas;

for each of the plurality of geographic areas, determining at least one takeover type corresponding to the geographic area from the historical driving data, wherein the corresponding at least one takeover type is the at least one takeover type to which a driving takeover event occurring on the at least one test vehicle within the geographic area belongs;

generating correspondence data for subsequent generation of an autonomous driving maneuver, wherein the correspondence data includes each of the plurality of geographic areas and its corresponding at least one takeover type.

2. The method of claim 1, further comprising:

determining a target geographic area where a target vehicle is located, wherein the target geographic area is one of the plurality of geographic areas;

determining at least one takeover type corresponding to the target geographic area according to the corresponding relation data; and

generating an autonomous driving maneuver for the target vehicle based at least on at least one takeover type corresponding to the target geographic area.

3. The method of claim 2, wherein the determining, for each of the plurality of geographic areas, at least one takeover type corresponding to the geographic area from the historical driving data comprises:

selecting first driving data at a first moment before a moment when a driving takeover event occurs and second driving data at a second moment after the moment when the driving takeover event occurs from the historical driving data, wherein the first driving data and the second driving data are data of the same parameter type;

determining a difference between the first driving data and the second driving data; and

and at least responding to the difference value being larger than a preset difference value threshold value, determining that the driving takeover event belongs to a pending takeover type related to the parameter type.

4. The method of claim 3, further comprising:

determining the occurrence frequency of the driving takeover event belonging to the type to be taken over;

and in response to the occurrence number being greater than a preset threshold number, determining that the at least one takeover type comprises the to-be-determined takeover type.

5. The method of claim 3, wherein a time difference between the first time and the time at which the driving takeover event occurred is less than a preset time threshold, and a time difference between the second time and the time at which the driving takeover event occurred is less than the preset time threshold.

6. The method of claim 3, wherein the first driving data comprises a first travel speed, the second driving data comprises a second travel speed, and the determining that the driving takeover event is of a pending takeover type that is related to the parameter type, at least in response to the difference being greater than a preset difference threshold, comprises:

in response to the first travel speed being greater than the second travel speed and the difference being greater than a preset speed difference threshold, determining that the driving takeover event belongs to a brake takeover.

7. The method of claim 3, wherein the first driving data includes a first travel acceleration, the second driving data includes a second travel acceleration, and the determining that the driving takeover event is of a pending takeover type that is associated with the parameter type, at least in response to the difference being greater than a preset difference threshold, comprises:

in response to the second travel acceleration being greater than a preset acceleration threshold and the difference being greater than a preset acceleration difference threshold, determining that the drive takeover event belongs to a throttle takeover.

8. The method of claim 3, wherein the first driving data includes a first steering wheel angle, the second driving data includes a second steering wheel angle, and the determining that the driving takeover event is of a pending takeover type that is related to the parameter type, at least in response to the difference being greater than a preset difference threshold, comprises:

and in response to the difference value being greater than a preset angle difference threshold value, determining that the driving takeover event belongs to steering wheel takeover.

9. The method of claim 8, wherein the generating an autonomous driving maneuver for the target vehicle based at least on the at least one takeover type corresponding to the target geographic area further comprises:

in response to the at least one takeover type comprising a steering wheel takeover, an autonomous driving maneuver is generated that sets a maximum speed limit for the target vehicle.

10. The method of any of claims 2 to 9, wherein said determining at least one type of takeover corresponding to the target geographic area in accordance with the correspondence data further comprises:

and generating takeover prompt information according to at least one takeover type corresponding to the target geographic area.

11. The method of any of claims 1-9, wherein the determining, for each of the plurality of geographic areas, at least one takeover type corresponding to the geographic area from the historical driving data further comprises:

and inputting the historical driving data into a trained prediction classification model to obtain the at least one takeover type.

12. A data generating apparatus for autonomous driving, comprising:

an acquisition unit configured to acquire historical driving data of at least one test vehicle while traveling in a plurality of geographical areas;

a first determining unit configured to determine, for each of the plurality of geographic areas, at least one takeover type corresponding to the geographic area from the historical driving data, wherein the corresponding at least one takeover type is at least one takeover type to which a driving takeover event occurring on the at least one test vehicle within the geographic area belongs;

a first generating unit configured to generate correspondence data for subsequently generating an automatic driving strategy, wherein the correspondence data comprises each of the plurality of geographic areas and its corresponding at least one takeover type.

13. The apparatus of claim 12, further comprising:

a second determining unit configured to determine a target geographic area where a target vehicle is located, wherein the target geographic area is one of the plurality of geographic areas;

a third determining unit configured to determine at least one takeover type corresponding to the target geographic area according to the correspondence data; and

a second generating unit configured to generate an autonomous driving maneuver for the target vehicle based at least on at least one takeover type corresponding to the target geographic area.

14. The apparatus of claim 13, wherein the first determining unit comprises:

the selection module is configured to select first driving data at a first moment before a moment when a driving takeover event occurs and second driving data at a second moment after the moment when the driving takeover event occurs from the historical driving data, wherein the first driving data and the second driving data are data of the same parameter type;

a first determination module configured to determine a difference between the first driving data and the second driving data; and

a second determination module configured to determine that the driving takeover event belongs to a pending takeover type related to the parameter type in response to at least the difference being greater than a preset difference threshold.

15. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-11.

17. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-11 when executed by a processor.