CN115476884A - Transverse deviation method and device in automatic driving, electronic equipment and storage medium - Google Patents
Transverse deviation method and device in automatic driving, electronic equipment and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0015—Planning or execution of driving tasks specially adapted for safety
- B60W60/0016—Planning or execution of driving tasks specially adapted for safety of the vehicle or its occupants
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
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- B60—VEHICLES IN GENERAL
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- B60W2554/00—Input parameters relating to objects
- B60W2554/40—Dynamic objects, e.g. animals, windblown objects
- B60W2554/402—Type
- B60W2554/4029—Pedestrians
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/40—Dynamic objects, e.g. animals, windblown objects
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Abstract
The invention provides a lateral deviation method and a device in automatic driving, electronic equipment and a storage medium, wherein the lateral deviation method in automatic driving comprises the following steps: acquiring data of a manual lane changing process when a vehicle is driven by a driver and data of an automatic lane changing process in an automatic driving process; inputting the data of the automatic lane changing process into a feedback neural network for training to obtain an initial offset decision neural network; inputting the manual lane changing process data into an initial offset decision neural network for training to obtain a target offset decision neural network; and if the starting instruction of the automatic driving is detected, executing the transverse deviation of the vehicle through a target deviation decision neural network. The target offset decision neural network is formed through manual lane changing process data training, is formed based on the driving habits of the driver, has the personalized style of the driver when the transverse offset is executed, reserves the driving habits of the driver, and enables the driver to be better adapted when starting the automatic driving function of the vehicle.
Description
Technical Field
The present application relates to the field of automatic driving technologies, and in particular, to a lateral shift method and apparatus in automatic driving, an electronic device, and a computer-readable storage medium.
Background
With the rapid development of science and technology, artificial intelligence is applied to a plurality of fields and has a good application effect, and intelligent driving is particularly concerned in the hot field. In recent years, the intelligent driving technology greatly facilitates the life of people from an emergency auxiliary function to a partial automatic driving function, and can release a driver from long-time repeated simple actions.
When the vehicle is in an automatic driving process, the vehicle is obstructed by a target vehicle, an obstacle and the like, and transverse deviation avoidance is required. Different transverse deviation avoidance behaviors can occur under different working conditions, and transverse deviation can be triggered at different times or the deviation amount of the transverse deviation can be triggered differently under different working conditions due to subjective factors of different drivers. In the prior art, a great deal of research is carried out on the aspects of improving effectiveness and safety and the like of lateral deviation in an automatic driving plate, and little research is carried out on the aspect of learning automatic lateral deviation to match driving habits of a driver, so that the lateral deviation cannot match the driving habits in the current automatic driving, and the driver cannot adapt to starting an automatic driving vehicle.
Disclosure of Invention
In view of the above drawbacks of the prior art, the present invention provides a method and an apparatus for lateral shift in automatic driving, an electronic device, and a storage medium, so as to solve the above technical problem that lateral shift does not match the habit of a driver in automatic driving of a vehicle.
In a first aspect, the present invention provides a lateral shift method in automatic driving, including:
acquiring data of a manual lane changing process when a vehicle is driven by a driver and data of an automatic lane changing process in an automatic driving process;
inputting the automatic lane changing process data into a feedback neural network for training to obtain an initial offset decision neural network;
inputting the manual lane changing process data into the initial offset decision neural network for training to obtain a target offset decision neural network;
and if a starting instruction of automatic driving is detected, executing the transverse deviation of the vehicle through the target deviation decision neural network.
Optionally, the acquiring data of the manual lane changing process when the vehicle is driven by the driver includes:
continuously and repeatedly acquiring manual lane changing process data when a driver drives, and inputting the acquired manual lane changing process data into the initial offset decision neural network for training to obtain the target offset decision neural network;
stopping when the decision of the target offset decision neural network on the lateral offset is consistent with the decision of the lateral offset when the driver drives.
Optionally, acquiring data of a manual lane change process when the vehicle is driven by the driver and data of an automatic lane change process in an automatic driving process, includes:
the automatic lane changing process data and the manual lane changing process data comprise vehicle information, lane information and dynamic information;
the vehicle information comprises vehicle speed, vehicle course angle and vehicle acceleration;
the lane information comprises lane line types, lane widths and lane types;
the dynamic information comprises the speed, the acceleration, the course angle and the relative position of the week vehicle.
Optionally, the dynamic information includes a speed of the vehicle, an acceleration of the vehicle, a heading angle of the vehicle, a relative position of the pedestrian, a speed of the pedestrian, a direction of the pedestrian, and a position of the obstacle.
Optionally, acquiring data of a manual lane change process when the vehicle is driven by the driver and data of an automatic lane change process in an automatic driving process, includes:
the automatic lane changing process data and the manual lane changing process data are collected 10s-20s before the vehicle performs the transverse deviation, and are sampled and obtained through a sampling period of 0.1s-1 s.
Optionally, inputting the automatic lane changing process data into a feedback neural network for training to obtain an initial offset decision neural network, including:
and selecting a forward feedback neural network to train the input automatic lane changing process data to obtain the initial offset decision neural network.
Optionally, before the data of the manual lane changing process is input to the initial offset decision neural network for training to obtain a target offset decision neural network, the method includes:
if the manual lane changing process data do not meet the requirement of lateral deviation in traffic regulations, deleting the group of manual lane changing process data; and if the manual lane changing process data meet the requirement of transverse deviation in traffic regulations, inputting the manual lane changing process data into the initial deviation decision neural network for training, replacing the automatic lane changing process data with the manual lane changing process data in the training process, and deleting the replaced automatic lane changing process data after the training is finished.
In a second aspect, the present invention provides an automatic lateral deviation device in driving, comprising:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring data of a manual lane changing process when a vehicle is driven by a driver and data of an automatic lane changing process in an automatic driving process;
the processing module is used for inputting the automatic lane changing process data into a feedback neural network for training to obtain an initial offset decision neural network;
the training module is used for inputting the manual lane changing process data into the initial offset decision neural network for training to obtain a target offset decision neural network;
and the execution module is used for executing the transverse deviation of the vehicle through the target deviation decision neural network if the starting instruction of the automatic driving is detected.
In a third aspect, the present invention provides an electronic device, comprising:
one or more processors;
a storage device to store one or more programs that, when executed by the one or more processors, cause the electronic equipment to implement an autonomous in-driving lateral offset method as recited in any of the above.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor of a computer, causes the computer to execute any of the above-described lateral offset in autonomous driving methods.
In the scheme realized by the method and the device for transverse deviation in automatic driving, the electronic equipment and the storage medium, the initial deviation decision neural network is formed through automatic lane changing process data training and is carried out based on the transverse deviation data of automatic driving in the prior art, so that the initial deviation decision neural network ensures enough safety when the transverse deviation is executed; the target offset decision neural network is formed through manual lane changing process data training, and the target offset decision neural network is formed on the basis of the driving habits of the driver, has the personalized style of the driver when the transverse offset is executed, reserves the driving habits of the driver, and enables the driver to be better adapted when starting the automatic driving function of the vehicle.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 is a schematic diagram illustrating an environment for implementing a lateral shift method in autonomous driving according to an exemplary embodiment of the present application;
FIG. 2 is a flow chart illustrating a lateral shift in autonomous driving method according to an exemplary embodiment of the present application;
FIG. 3 is a block diagram of an autonomous in-driving lateral offset apparatus shown in an exemplary embodiment of the present application;
FIG. 4 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the disclosure herein, wherein the embodiments of the present invention are described in detail with reference to the accompanying drawings and preferred embodiments. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than being drawn according to the number, shape and size of the components in actual implementation, and the type, amount and proportion of each component in actual implementation can be changed freely, and the layout of the components can be more complicated.
In the following description, numerous details are set forth to provide a more thorough explanation of embodiments of the present invention, however, it will be apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details, and in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring embodiments of the present invention.
It should be noted that automatic driving, also called unmanned driving, is a complete, safe and effective driving of a vehicle by means of a computer and an artificial intelligence technology without artificial control. The automatic driving technology can coordinate the travel route and the planning time under the support of the car networking technology and the artificial intelligence technology, so that the travel efficiency is greatly improved, the energy consumption is reduced to a certain degree, meanwhile, the automatic driving technology can help avoid drunk driving, potential safety hazards such as fatigue driving and the like, the error of a driver is reduced, and the safety is improved. The automatic driving is a driving state without driver control on the vehicle through an automatic driving system, and the automatic driving system adopts advanced communication, computer, network and control technology to realize real-time and continuous control on the vehicle.
Here, the lateral deviation refers to control for changing lanes or changing lanes of the vehicle during automatic driving. The technology developed based on a vision system is used for implementing transverse offset on the vehicle in automatic driving, road geometric characteristics and dynamic parameters of the vehicle are sensed through a camera, a sensor, a radar and the like, then an on-board intelligent module is used for calculating and feeding back to a control system, and the vehicle is controlled to implement lane changing operation.
Fig. 1 is a schematic diagram of an implementation environment of a lateral shift method in automatic driving according to an exemplary embodiment of the present application. The method comprises the steps that a vehicle is arranged in front of the vehicle in the driving process and needs to be laterally deviated to change lanes, information such as the position, the speed and the acceleration of a front vehicle is obtained through a vehicle-mounted camera, a radar and the like in the lane changing process of the vehicle, the lane information of a front vehicle is obtained through a high-precision map, the obtained information is comprehensively processed, and the lateral deviation can be executed in automatic driving to change lanes.
The high-precision map can be installed on an intelligent terminal, and the intelligent terminal can be a terminal device supporting installation of navigation map software at will, such as a smart phone, a vehicle-mounted computer, a tablet computer, a notebook computer or a wearable device, but is not limited thereto. The intelligent terminal may communicate with the navigation server 220 through a wireless network such as 3G (third generation mobile information technology), 4G (fourth generation mobile information technology), 5G (fifth generation mobile information technology), and the like, which is not limited herein. The server device shown in fig. 1 is a server, and may be, for example, an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a CDN (Content Delivery Network), and a big data and artificial intelligence platform, which is not limited herein.
Referring to fig. 2, fig. 2 is a flowchart illustrating a lateral shift method in automatic driving according to an exemplary embodiment of the present application. The method may be applied to the implementation environment shown in FIG. 1 and specifically performed by a vehicle in the implementation environment. It should be understood that the method may be applied to other exemplary implementation environments and is specifically executed by devices in other implementation environments, and the embodiment does not limit the implementation environment to which the method is applied.
As shown in fig. 2, in an exemplary embodiment, the lateral shift method in automatic driving at least includes steps S210 to S240, which are described in detail as follows:
step S210, acquiring data of a manual lane change process when the vehicle is driven by the driver, and data of an automatic lane change process during automatic driving.
It should be noted that, during the driving process of the vehicle, the manual lane changing process data may be continuously acquired through a millimeter wave radar, a laser radar, an intelligent camera, and the like, and the data is temporarily stored. For the acquisition of the manual lane changing process data, the stored manual lane changing process data can be extracted after the vehicle performs lateral deviation to change lanes. The data format and the data information of the data of the moving track changing process and the data of the automatic track changing process are kept consistent.
The collected data are uniformly stored, the data can be stored in a database, the data are convenient to increase, delete, check and change, and the data after being collected and trained each time are deleted in time.
The automatic driving setting carried out by combining the high-precision map and the driving safety can be directly extracted for the automatic lane changing process data, and the automatic lane changing process data can also be obtained by the acquisition mode of the manual lane changing process data.
In some embodiments, the automatic lane-changing process data and the manual lane-changing process data each include own-vehicle information, lane information, dynamic information, and the like;
the vehicle information comprises vehicle speed, vehicle course angle and vehicle acceleration;
the lane information comprises lane line types, lane widths and lane types;
the dynamic information comprises the speed, the acceleration, the course angle and the relative position of the week vehicle, the relative position of the pedestrian, the speed, the direction and the position of the obstacle.
The information is used for judging whether the lane change is carried out by the lateral deviation, whether the lane change condition is met, how the vehicle needs to be controlled when the lane change is carried out specifically, and the like.
In some embodiments, the automatic lane-changing process data and the manual lane-changing process data are both collected 10s-20s before the vehicle performs the lateral offset and are sampled and acquired with a sampling period of 0.1s-1 s.
In the specific implementation process, in the automatic driving process of the vehicle or the manual driving process of a driver, after the lane change is performed by the transverse deviation, data information in 15 seconds before the lane change of the vehicle is performed can be extracted, the data information is sampled and acquired every 0.1s, namely, each data in each deviation has 150 sampling points, a group of completed data is formed and analyzed, and the situation of the transverse lane change performed by the vehicle can be comprehensively judged. In other implementation processes, after the lane change is performed by the transverse offset, data information within 20 seconds before the lane change of the vehicle is performed can be extracted, the data information is sampled and acquired every 0.2s, namely each data in each offset has 100 sampling points, a group of completed data is formed and analyzed, and the situation of the lane change performed by the vehicle can be comprehensively judged. And extracting the acquisition duration and the sampling period of the data information according to the actual analysis requirement of the data information.
Step S220, inputting the automatic lane changing process data into a feedback neural network for training to obtain an initial offset decision neural network.
In some embodiments, a feedforward neural network is selected to train the input automatic lane change process data to obtain the initial offset decision neural network.
The forward feedback neural network trains the automatic lane changing process data, the existing forward feedback neural network and a training module can be adopted, the obtained initial offset decision neural network can execute the transverse offset of the vehicle, and the module is not repeated.
Step S230, inputting the manual lane changing process data to the initial offset decision neural network for training, so as to obtain a target offset decision neural network.
In some embodiments, if the manual lane change process data does not meet the requirement of lateral deviation in traffic regulations, deleting the set of manual lane change process data; and if the manual lane changing process data meet the requirement of transverse deviation in traffic regulations, inputting the manual lane changing process data into the initial deviation decision neural network for training, replacing the automatic lane changing process data with the manual lane changing process data in the training process, and deleting the replaced automatic lane changing process data after the training is finished.
For example, if the lane lines in the lane information of the manual lane changing process data are solid lines, the group of manual lane changing process data is used as the lane information, so that the condition that the acquired manual lane changing process data violates rules and causes potential safety hazards in subsequent automatic driving is avoided. After the manual lane changing process data is substituted into the training, the automatic lane changing process data can be replaced.
In some embodiments, manual lane changing process data of a driver during driving is continuously acquired for multiple times, and the acquired manual lane changing process data of each time is input into the initial offset decision neural network for training to obtain the target offset decision neural network; stopping when the decision of the target offset decision neural network on the lateral offset is consistent with the decision of the lateral offset when the driver drives.
And after data are collected, performing offset decision neural network learning training until the target offset decision neural network is stable, namely well learning the offset style of the driver, and stopping training, wherein the target offset decision neural network has the personal habit of the driver and is personalized according to the driving habit of the driver.
Step S240, if a start instruction of automatic driving is detected, executing lateral deviation of the vehicle through the target deviation decision neural network.
The vehicle automatically drives according to the target deviation decision neural network, the habit of the driver is matched, the driver can feel safe when sitting on the vehicle, and the driver can adapt to the target deviation decision neural network.
In an embodiment, an automatic driving lateral shifting apparatus is provided, where the automatic driving lateral shifting apparatus corresponds to the automatic driving lateral shifting method in the foregoing embodiments one to one, as shown in fig. 3, fig. 3 is a schematic structural diagram of an automatic driving lateral shifting apparatus shown in an exemplary embodiment of the present application, and includes an obtaining module 301, a processing module 302, a training module 303, and an executing module 304, where each functional module is described in detail as follows:
an acquisition module 301 for acquiring data of a manual lane change process when a vehicle is driven by a driver and data of an automatic lane change process in an automatic driving process;
the processing module 302 is configured to input the automatic lane changing process data into a feedback neural network for training, so as to obtain an initial offset decision neural network;
a training module 303, configured to input the manual lane changing process data to the initial offset decision neural network for training, to obtain a target offset decision neural network;
an executing module 304, configured to execute, by the target offset decision neural network, the lateral offset of the vehicle if a starting instruction of the automatic driving is detected.
It should be noted that the lateral shifting apparatus in automatic driving provided by the foregoing embodiment and the lateral shifting method in automatic driving provided by the foregoing embodiment belong to the same concept, and specific ways for the modules and units to perform operations have been described in detail in the method embodiment, and are not described herein again. In practical applications, the lateral shifting apparatus in automatic driving provided by the above embodiment may distribute the above functions by different functional modules according to needs, that is, divide the internal structure of the apparatus into different functional modules to complete all or part of the above described functions, which is not limited herein.
An embodiment of the present application further provides an electronic device, including: one or more processors; a storage device for storing one or more programs that, when executed by the one or more processors, cause the electronic apparatus to implement the lateral offset in autonomous driving method provided in the above-described embodiments.
FIG. 4 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application. It should be noted that the computer system 400 of the electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the application scope of the embodiments of the present application.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU) 401, which can execute various appropriate actions and processes, such as executing the method described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 402 or a program loaded from a storage portion 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An Input/Output (I/O) interface 405 is also connected to the bus 404.
The following components are connected to the I/O interface 405: an input portion 406 including a keyboard, a mouse, and the like; an output section 407 including a Display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A drive 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 401.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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), a 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. In the present application, a computer-readable signal medium may comprise a propagated data signal with a computer-readable computer program embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
Another aspect of the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor of a computer, causes the computer to execute the lateral offset in autonomous driving method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist separately without being incorporated in the electronic device.
Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the lateral offset in autonomous driving method provided in the above-described embodiments.
The foregoing embodiments are merely illustrative of the principles of the present invention and its efficacy, and are not to be construed as limiting the invention. Those skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A method of lateral shifting in autonomous driving, the method comprising:
acquiring data of a manual lane changing process when a vehicle is driven by a driver and data of an automatic lane changing process in an automatic driving process;
inputting the automatic lane changing process data into a feedback neural network for training to obtain an initial offset decision neural network;
inputting the manual lane changing process data into the initial offset decision neural network for training to obtain a target offset decision neural network;
and if the starting instruction of automatic driving is detected, executing the transverse deviation of the vehicle through the target deviation decision neural network.
2. The method of lateral shifting in autonomous driving of claim 1, wherein obtaining data of a manual lane change procedure when the vehicle is driven by the driver comprises:
continuously and repeatedly acquiring manual lane changing process data when a driver drives, and inputting the acquired manual lane changing process data into the initial offset decision neural network for training to obtain the target offset decision neural network;
stopping when the decision of the target offset decision neural network on the lateral offset is consistent with the decision of the lateral offset when the driver drives.
3. The automatic driving lateral shift method according to claim 2, wherein acquiring data of a manual lane change process when the vehicle is driven by the driver and data of an automatic lane change process during automatic driving includes:
the automatic lane changing process data and the manual lane changing process data comprise vehicle information, lane information and dynamic information;
the vehicle information comprises vehicle speed, vehicle course angle and vehicle acceleration;
the lane information comprises lane line types, lane widths and lane types;
the dynamic information comprises the speed, the acceleration, the course angle and the relative position of the week vehicle.
4. The method of claim 3, wherein the dynamic information comprises a cycle speed, a cycle acceleration, a cycle heading angle, a cycle relative position, and further comprises a pedestrian relative position, a pedestrian speed, a pedestrian direction, an obstacle position.
5. The lateral shift method in automatic driving according to claim 4, wherein acquiring data of a manual lane change process when the vehicle is driven by the driver and data of an automatic lane change process during automatic driving includes:
the automatic lane changing process data and the manual lane changing process data are collected 10s-20s before the vehicle performs the transverse deviation, and are sampled and obtained through a sampling period of 0.1s-1 s.
6. The method of claim 5, wherein the automatic lane change process data is input to a feedback neural network for training to obtain an initial deviation decision neural network, and the method comprises:
and selecting a forward feedback neural network to train the input automatic lane changing process data to obtain the initial offset decision neural network.
7. The method of claim 6, wherein before inputting the manual lane-changing process data into the initial deviation decision neural network for training and obtaining a target deviation decision neural network, the method comprises:
if the manual lane changing process data do not meet the requirement of lateral deviation in traffic regulations, deleting the group of manual lane changing process data; and if the manual lane changing process data meet the requirement of transverse deviation in traffic regulations, inputting the manual lane changing process data into the initial deviation decision neural network for training, replacing the automatic lane changing process data with the manual lane changing process data in the training process, and deleting the replaced automatic lane changing process data after the training is finished.
8. An automatic lateral shift device in driving, characterized in that the device comprises:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring data of a manual lane changing process when a vehicle is driven by a driver and data of an automatic lane changing process in an automatic driving process;
the processing module is used for inputting the automatic lane changing process data into a feedback neural network for training to obtain an initial offset decision neural network;
the training module is used for inputting the manual lane changing process data into the initial offset decision neural network for training to obtain a target offset decision neural network;
and the execution module is used for executing the transverse deviation of the vehicle through the target deviation decision neural network if a starting instruction of automatic driving is detected.
9. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a storage device to store one or more programs that, when executed by the one or more processors, cause the electronic device to implement the autopilot lateral shift method of any of claims 1-7.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor of a computer, causes the computer to execute the lateral offset in autonomous driving method of any one of claims 1 to 7.
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