CN111399490A - Automatic driving method and device - Google Patents
Automatic driving method and device Download PDFInfo
- Publication number
- CN111399490A CN111399490A CN201811614039.7A CN201811614039A CN111399490A CN 111399490 A CN111399490 A CN 111399490A CN 201811614039 A CN201811614039 A CN 201811614039A CN 111399490 A CN111399490 A CN 111399490A
- Authority
- CN
- China
- Prior art keywords
- data
- driving behavior
- driving
- behavior data
- target area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 230000007613 environmental effect Effects 0.000 claims abstract description 23
- 230000006399 behavior Effects 0.000 claims description 207
- 238000012545 processing Methods 0.000 claims description 31
- 230000015654 memory Effects 0.000 claims description 29
- 238000011156 evaluation Methods 0.000 claims description 22
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 4
- 238000012502 risk assessment Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 description 19
- 238000000605 extraction Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 230000003068 static effect Effects 0.000 description 6
- 239000000284 extract Substances 0.000 description 5
- 230000001133 acceleration Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 4
- 238000010168 coupling process Methods 0.000 description 4
- 238000005859 coupling reaction Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 235000019800 disodium phosphate Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 238000011022 operating instruction Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000005022 packaging material Substances 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- HOWHQWFXSLOJEF-MGZLOUMQSA-N systemin Chemical compound NCCCC[C@H](N)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(O)=O)C(=O)OC(=O)[C@@H]1CCCN1C(=O)[C@H]1N(C(=O)[C@H](CC(O)=O)NC(=O)[C@H](CCCN=C(N)N)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CO)NC(=O)[C@H]2N(CCC2)C(=O)[C@H]2N(CCC2)C(=O)[C@H](CCCCN)NC(=O)[C@H](CO)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](C)N)C(C)C)CCC1 HOWHQWFXSLOJEF-MGZLOUMQSA-N 0.000 description 1
- 108010050014 systemin Proteins 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/028—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0285—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using signals transmitted via a public communication network, e.g. GSM network
Abstract
The embodiment of the application discloses an automatic driving method and an automatic driving system, which are used for determining an automatic driving strategy according to historical driving behavior data and current environment data of a user so as to realize safe driving. The method in the embodiment of the application comprises the following steps: the method comprises the steps of obtaining pre-stored driving behavior data, wherein the pre-stored driving behavior data are used for representing driving behavior data when a vehicle passes through a target area under the condition of manual driving; acquiring first environment data of the target area, wherein the first environment data is currently acquired in real time; determining a target driving strategy according to the first environmental data and the prestored driving behavior data; and finishing automatic driving to pass through the target area according to the target driving strategy.
Description
Technical Field
The present application relates to the field of automatic driving, and in particular, to an automatic driving method and apparatus.
Background
With the development of automated driving and driver assistance technologies, more and more companies are developing and even mass-producing driving systems that can travel by themselves in a lane, so that vehicles equipped with the self-traveling driving systems are provided with automated driving functions without human intervention.
The system composition for autonomous driving or advanced assisted driving is generally shown in fig. 1. In the system shown in fig. 1, the automatic driving scheme is performed as follows: acquiring sensing data of sensors (such as external sensors, internal sensors, satellite positioning and data word patterns); the perception data acquired by the sensor is input into an automatic driving or auxiliary driving system to be used as an information input source for planning driving behaviors, and meanwhile, a user can input partial data according to a digital map or own experience; then planning out the expected target speed and the steering path according to a computer processing algorithm; and finally, the control and execution system completes the execution of the driving behavior of the automatic driving vehicle.
Therefore, under the scheme of the system, the vehicle usually adopts fixed parameters to automatically drive, and due to the openness of the actual traffic environment, the behavior of the vehicle when driving through a curve and the finally expressed vehicle driving data are influenced by factors such as environment, surrounding vehicles, other obstacles or pedestrians, and safety problems may occur.
Disclosure of Invention
The embodiment of the application provides an automatic driving method and device, which determine an automatic driving strategy according to historical driving behavior data and current environment data of a user, so that safe driving is realized, automatic driving experience better conforming to the past driving experience of a driver is provided, and the expected comfort and safety of the driver are better met.
In a first aspect, an embodiment of the present application provides an automatic driving method, including: the automatic driving system acquires pre-stored driving behavior data, wherein the pre-stored driving behavior data is driving behavior data when the vehicle passes through a target area under the condition of manual driving; then the automatic driving system acquires current first environment data of the target area; the automatic driving system adjusts the prestored driving behavior data according to the first environment data to obtain a final target driving strategy; and finally, the automatic driving system drives the vehicle to pass through the target area according to the target driving strategy.
The pre-stored driving behavior data can be pre-stored in the local vehicle, or pre-stored in a remote server or a cloud server. Therefore, the pre-stored driving behavior data can be acquired from the memory of the vehicle, and can also be acquired from a remote server. In the historical driving process of the vehicle where the automatic driving system is located, the driving behavior data is recorded to form the driving behavior data of the vehicle, and the driving behavior data can also be sent to a server to form a driving behavior database on the server. If the driving behavior data are recorded on the server, the driving behavior data can be recorded on other vehicles on the corresponding road section besides the data of the own vehicle, and the data are processed on the server to form more optimized driving behavior data. If the driving behavior data is pre-stored in a remote server or a cloud server, the self vehicle can be obtained in real time, and can also be obtained in advance according to a driving target place and the like.
In the present embodiment, the driving behavior data includes information such as a vehicle speed of the vehicle, an acceleration of the vehicle, a braking operation of the vehicle, and a raw signal of a steering system of the vehicle. The target area includes road areas such as curves, intersections, and intersections.
The environment data comprises position information of all vehicles in the target area, the number of all vehicles, position information and the number of obstacles in the target area, current weather conditions and road conditions. For example, in the curve, a car is arranged beside the vehicle, a large truck is arranged twenty meters ahead of the vehicle, the current weather is foggy, and the road is wet and slippery.
In the embodiment of the application, the automatic driving system determines the automatic driving strategy according to the historical driving behavior data of the user (namely, the driving behavior data equivalent to the driving behavior data when the vehicle passes through the target area under the condition of manual driving) and the current environment data, so that safe driving is realized, automatic driving experience more conforming to the past driving experience of the driver is provided, and the expected comfort and safety of the driver are more conformed.
Optionally, before the automatic driving system obtains the pre-stored driving behavior data, the automatic driving system needs to obtain a real-time position of the vehicle; and when the distance between the real-time position and the target area reaches a preset threshold value, triggering the automatic driving system to acquire the pre-stored driving behavior data. Therefore, the automatic driving system can acquire the prestored driving behavior data corresponding to the target area in advance, and is beneficial to planning the driving strategy in time. It will be appreciated that in this embodiment, the real-time position of the vehicle may be determined from a digital map or satellite positioning. The digital map is a high-precision digital map which can achieve the positioning precision of less than 1 meter and provide accurate lane information and traffic data information; the satellite positioning generally refers to the existing civil Global satellite positioning technology, is widely applied to various electronic products, is an essential function for positioning the geographic Position of equipment, and specifically comprises a Global Positioning System (GPS) in the united states, a galileo satellite positioning System in europe, a glonass satellite positioning System in russia and a beidou satellite positioning System in china.
Optionally, before the automatic driving system obtains the pre-stored driving behavior data, the pre-stored driving behavior data needs to be stored, and the specific manner includes several possible implementation manners as follows:
in one possible implementation manner, when the automatic driving system passes through the target area under the condition of manual driving, the automatic driving system acquires driving behavior data of the vehicle and second environment data, wherein the second environment data is environment data of the target area of the manual driving at that time, and the driving behavior data is driving behavior data of the vehicle at the time of the manual driving at that time; then, evaluating the driving behavior data and the second environment data to obtain a first evaluation value; and when the first evaluation value indicates that the driving behavior data meets the preset condition, the automatic driving system saves the driving behavior data as the prestored driving behavior data. The driving behavior data includes information such as a vehicle speed of the vehicle, an acceleration of the vehicle, a braking operation of the vehicle, and a raw signal of a steering system of the vehicle. The target area includes road areas such as curves, intersections, or other structured roads.
In another possible implementation manner, when the automatic driving system passes through the target area under the condition of manual driving, the automatic driving system acquires driving behavior data of the vehicle and second environment data, wherein the second environment data is environment data of the target area of the manual driving at that time, and the driving behavior data is driving behavior data of the vehicle at the time of the manual driving at that time; then the automatic driving system obtains first characteristic data of the driving behavior data and second characteristic data of the second environment data by using a supervised regression algorithm; the automatic driving system evaluates the first characteristic data and the second characteristic data to obtain a second evaluation value; and when the second evaluation value indicates that the driving behavior data meet the preset conditions, the automatic driving system saves the first characteristic data as the pre-stored driving behavior data. The driving behavior data includes information such as a vehicle speed of the vehicle, an acceleration of the vehicle, a braking operation of the vehicle, and a raw signal of a steering system of the vehicle. The target area includes road areas such as curves, intersections, or other structured roads.
Optionally, the first evaluation value includes an effective value of the driving behavior data and a risk value of the second environmental data; the effective value of the driving behavior data is obtained according to a first formula, and the risk value of the second environmental data is obtained according to a second formula; wherein the first formula is:
the second formula is:
wherein, the
Representing risk field data available within a sensing range during recording of the data; the
Risk assessment data representing user event behavior in the driving behavior data; the n and the k are constants equal to the number of recorded data; the r is a risk value of the second environment data; s isiPosition information of an obstacle in the vicinity of the vehicle; i is a constant. Similarly, driving in the target area by the automatic driving systemIn the solution that the driving behavior data is subjected to feature extraction to obtain the first feature data, and the second environment data is subjected to feature extraction to obtain the second feature data, the automatic driving system may obtain the second evaluation value in the same manner as described below, and the specific operation manner is not described again.
Optionally, the specific operation of storing the pre-stored driving behavior data in the automatic driving behavior data is as follows:
in one possible implementation manner, the automatic driving system stores the target area and the driving behavior data according to a position index, that is, the automatic driving system establishes a mapping relationship between the target area and the driving behavior data, and in the case of automatic driving, if the automatic driving system determines that the vehicle is located in the target area, the driving behavior data corresponding to the target area is extracted as prestored driving behavior data. For example, the target area is a curve 1, and the corresponding pre-stored driving behavior data is data a, then when the automatic driving system determines that the vehicle is located in the curve 1 through positioning, the automatic driving system extracts the data a.
In another possible implementation, the automatic driving system stores the driving behavior data with the target area and an area within a preset range of the target area according to a position index. The automatic driving system establishes a mapping relation between the driving behavior data and a target position, wherein the target position comprises the target area and an area located within a preset range of the target area, and then under the condition of automatic driving, if the automatic driving system determines that the vehicle is located at the target position, the driving behavior data corresponding to the target position is extracted to serve as prestored driving behavior data. For example, if the target area is a curve 1, the target position may be an area with the curve 1 as a center and a radius as a preset distance, and then when the distance between the vehicle and the curve 1 is determined to be the preset distance by the automatic driving system through positioning, the automatic driving system extracts pre-stored driving behavior data corresponding to the curve 1. Therefore, the automatic driving system can acquire the driving behavior data corresponding to the target area in advance, so that the driving behavior data can be more effectively adjusted according to the actual environment data, a more correct driving strategy is obtained, and the driving safety is ensured.
In a second aspect, the present application provides an automatic driving system, which has corresponding functions to implement the method of the first aspect. The functions can be realized by hardware, and the functions can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the above-described functions.
In one possible implementation, the autopilot system comprises means or modules for performing the steps of the first aspect above. For example, the automatic driving system includes: the acquisition module is used for acquiring prestored driving behavior data, and the prestored driving behavior data is used for representing the driving behavior data when the vehicle passes through the target area under the condition of manual driving; acquiring first environment data of the target area, wherein the first environment data is currently acquired in real time; the processing module is used for determining a target driving strategy according to the first environment data and the prestored driving behavior data; and the execution module is used for finishing automatic driving to pass through the target area according to the target driving strategy.
Optionally, the system further comprises a storage module for storing necessary program instructions and data of the automatic driving system, such as the pre-stored driving behavior data.
In one possible implementation, the autopilot system includes: a processor and a memory, the processor being configured to support an autonomous driving system to perform the respective functions of the method provided by the first aspect described above. The transceiver is used for acquiring various data of the external environment, such as receiving positioning information sent by a satellite positioning system, so as to determine the real-time position of the vehicle. The memory is for coupling to a processor that stores program instructions and data necessary for the autonomous driving system, such as the data being the pre-stored driving behavior data.
In one possible implementation, when the automatic driving system is a chip, the chip includes: a processing module and a transceiver module, wherein the processing module may be, for example, a processor, and the processor is configured to determine a target driving strategy according to the first environment data and the pre-stored driving behavior data; the transceiver module may be, for example, an input/output interface, pin or circuit on the chip, and transmits the target driving strategy generated by the processor to other chips or modules coupled to the chip. The processing module may execute computer-executable instructions stored by the storage unit to support the automatic driving system to perform the method provided by the first aspect. Alternatively, the storage unit may be a storage unit in the chip, such as a register, a cache, and the like, and the storage unit may also be a storage unit located outside the chip, such as a read-only memory (ROM) or another type of static storage device that can store static information and instructions, a Random Access Memory (RAM), and the like.
In one possible implementation, the apparatus includes: a processor, baseband circuitry, radio frequency circuitry, and an antenna. The processor is used for realizing control of functions of each circuit part, the baseband circuit can generate information carrying positioning requests, and the information is subjected to analog conversion, filtering, amplification, up-conversion and the like through the radio frequency circuit and then is sent to corresponding equipment in the satellite positioning system through the antenna. Optionally, the apparatus further comprises a memory that stores program instructions and data necessary for the autopilot system.
The processor mentioned in any of the above paragraphs may be a general purpose Central Processing Unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of programs for controlling the method of coordinated allocation of channel resources in the above paragraphs.
In a third aspect, an embodiment of the present application provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to execute the method described in any possible implementation manner in the first aspect.
In a fourth aspect, embodiments of the present application provide a computer program product containing instructions, which when executed on a computer, cause the computer to perform the method according to any possible implementation manner of the first aspect.
In a fifth aspect, the present application provides a chip system comprising a processor for enabling an autopilot system to carry out the functions referred to in the above aspects, such as generating or processing data and/or information referred to in the above methods. In one possible design, the system-on-chip further includes a memory for storing program instructions and data necessary for the autopilot system to function in any of the above aspects. The chip system may be formed by a chip, and may also include a chip and other discrete devices.
In a sixth aspect, embodiments of the present application provide a vehicle including the automatic driving system of the above aspect.
Drawings
FIG. 1 is a schematic diagram of a system configuration for autonomous driving or advanced driver assistance;
FIG. 2 is a schematic diagram of an embodiment of an automatic driving method in an embodiment of the present application;
FIG. 3 is a schematic view of an application scenario of an automatic driving method in an embodiment of the present application;
fig. 4 is a schematic diagram of a storage process of pre-stored driving behavior data in the embodiment of the present application;
FIG. 5 is a schematic view of an application scenario of an automatic driving method in an embodiment of the present application;
FIG. 6 is a schematic diagram of an embodiment of an autopilot system in an embodiment of the present application;
FIG. 7 is a schematic diagram of another embodiment of an autopilot system according to an embodiment of the present application;
fig. 8 is a schematic diagram of another embodiment of the automatic driving system in the embodiment of the present application.
Detailed Description
The embodiment of the application provides an automatic driving method and device, which are used for determining an automatic driving strategy according to historical driving behavior data and current environment data of a user so as to realize safe driving.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The system composition for autonomous driving or advanced assisted driving is generally shown in fig. 1. In the system shown in fig. 1, the automatic driving scheme is performed as follows: acquiring sensing data of a sensor; processing data acquired by the sensor to serve as an information input source for planning the driving behavior; then planning out the expected target speed and the steering path according to a computer processing algorithm; and finally, the control and execution system completes the execution of the driving behavior of the automatic driving vehicle. Therefore, under the scheme of the above system, the vehicle usually adopts fixed parameters for automatic driving, and due to the openness of the actual traffic environment, the behavior of the vehicle when driving through a curve and the finally expressed vehicle driving data are influenced by factors such as the environment, surrounding vehicles, other obstacles or pedestrians, so that safety problems or discomfort caused by driving behavior not meeting the expectation of the driver can occur.
The embodiment of the application provides the following technical scheme: the automatic driving system acquires pre-stored driving behavior data, wherein the pre-stored driving behavior data is driving behavior data when the vehicle passes through a target area under the condition of manual driving; then the automatic driving system acquires current first environment data of the target area; the automatic driving system adjusts the prestored driving behavior data according to the first environment data to obtain a final target driving strategy; and finally, the automatic driving system drives the vehicle to pass through the target area according to the target driving strategy.
For convenience of understanding, some terms referred to in the embodiments of the present application are explained below:
structuring the road: the structured road is a public road which is defined and constructed by clear construction standard laws and regulations (such as the industry standard CJJ 37-2012 & ltCity road engineering design Specification & gt) of the people's republic of China, such as an expressway and an urban arterial road. The structured road has clear construction requirements, clear road sign lines and auxiliary facilities.
Automatic driving: the automatic driving is an intelligent automobile which realizes unmanned driving through a computer system. The motor vehicle can be automatically and safely operated by a computer without any active operation of human by means of the cooperative cooperation of artificial intelligence, visual calculation, radar, a monitoring device and a global positioning system.
Auxiliary driving: the advanced driving auxiliary system senses the surrounding environment at any time in the driving process of the automobile by using a sensor on the automobile and combines relevant data such as a navigation map and the like, so that a driver can detect the possible danger in advance, and the comfort and the safety of automobile driving are effectively improved. The driving auxiliary system comprises a lane keeping auxiliary system, an automatic parking auxiliary system, a brake auxiliary system, a backing auxiliary system and a driving auxiliary system.
A digital map: digital map systems widely used in smart phones, vehicle navigation and automatic driving systems, wherein maps for commercial use, smart phones and vehicle navigation are non-high-precision maps, and are used for providing people with geographic information such as navigation. The digital map for full-automatic driving is a high-precision digital map which has positioning precision of less than 1 meter and provides accurate lane information and traffic data information.
A vehicle-mounted sensor: the sensor device is used in an automatic driving or advanced assistant driving system and is a general name of a sensor used for helping a vehicle to sense the external environment and the internal environment of the vehicle and objects, and common sensor devices comprise sensors such as a camera, a millimeter wave radar, an ultrasonic radar and a laser radar.
Satellite positioning: the positioning System generally refers to the existing civil Global satellite positioning technology, is widely used for various electronic products, is an essential function for positioning the geographic Position of equipment, and specifically comprises a Global Positioning System (GPS) in the united states, a galileo satellite positioning System in europe, a glonass satellite positioning System in russia and a beidou satellite positioning System in china.
Specifically, referring to fig. 2, an embodiment of an automatic driving method in the embodiment of the present application:
201. the automatic driving system acquires pre-stored driving behavior data, which is driving behavior data of a vehicle passing through a target area under a manual driving condition.
In this embodiment, when the automatic driving system travels within a preset range from the target area, the automatic driving system triggers and acquires pre-stored driving behavior data. The automatic driving system extracts pre-stored driving behavior data corresponding to the target area from a database, wherein the pre-stored driving behavior data are driving behavior data of a driver when the vehicle passes through the target area under the condition of manual driving. The pre-stored driving behavior data can be pre-stored in the local vehicle, or pre-stored in a remote server or a cloud server.
In the present embodiment, the driving behavior data includes information such as a vehicle speed of the vehicle, an acceleration of the vehicle, a braking operation of the vehicle, and a raw signal of a steering system of the vehicle. The target area includes road areas such as curves, intersections, or other structured roads.
202. The automatic driving system acquires current first environment data of the target area.
When the automatic driving system drives to the target area, the automatic driving system acquires current first environment data of the target area through an external sensor and navigation.
The environment data comprises position information of all vehicles in the target area, the number of all vehicles, position information and the number of obstacles in the target area, current weather conditions and road conditions. For example, in the curve, a car is arranged beside the vehicle, a large truck is arranged twenty meters ahead of the vehicle, the current weather is foggy, and the road is wet and slippery. The external sensors and navigation systems in the autopilot system include, but are not limited to, cameras, millimeter wave radar, lidar, ultrasonic radar, inertial sensors, satellite positioning, and digital maps.
203. The automatic driving system determines a target driving strategy according to the first environmental data and the pre-stored driving data.
The automatic driving system determines a final target driving strategy according to the first environmental data and the pre-stored driving data.
In this embodiment, the automatic driving system adjusts the pre-stored driving behavior data according to the first environment data and the automatic driving rule, and finally determines the target driving strategy.
For example, the pre-stored driving behavior data indicates that the driving speed of the own vehicle should be the a speed, the first environment data indicates that a large truck is present twenty meters ahead of the own vehicle, it is determined according to the automatic driving rule that the driving speed needs to be reduced to the B speed, and then the driving speed of the own vehicle in the final target driving strategy is the B speed.
204. The autonomous driving system drives the vehicle through the target area according to the target driving strategy.
The autonomous driving system drives the vehicle through the target area according to the target driving strategy.
A specific application scenario is described below, specifically referring to the curve 300 shown in fig. 3:
after the vehicle 301 is in the autonomous driving state, the feature extraction triggering event mechanism of the autonomous driving system is turned on. When the user enters the recognition area 302 (i.e., the area corresponding to the target area or the area within the preset range of the target area in this embodiment), the autopilot system triggers to extract the pre-stored driving behavior data corresponding to the target area, and uses the pre-stored driving behavior data to participate in autopilot. Carrying out environment matching on the prestored driving behavior data according to the planned path 303 to obtain data of traffic rules and environment states in the target area, such as speed limit, whether whistling is prohibited, the distance between vehicles, whether barriers exist on a lane, weather conditions and the like; then the automatic driving system adjusts the pre-stored driving behavior data according to the traffic rules and the environmental state in the target area to obtain a new driving strategy; finally the autonomous driving system controls the vehicle through the curve 300 using the driving strategy.
In this embodiment, the automatic driving system obtains historical driving behavior data of the driver in advance, duplicates the operation of the driver when the driver passes through the target area, adjusts the operation according to the first environment data of the target area to obtain a final target driving strategy, and passes through the target area according to the target driving strategy. Therefore, the driving safety can be effectively ensured.
In this embodiment, the automatic driving system further needs to acquire and store the pre-stored driving behavior data, and the specific situation may be as shown in fig. 4:
401. the automatic driving system acquires driving behavior data of the vehicle passing through the target area under the condition of manual driving and second environment data of the target area.
The automatic driving system stores data recorded by a sensor of the vehicle during manual driving of the driver through the target area, wherein the data comprises driving behavior data of the driver and second environment data of the target area.
402. The autonomous driving system determines an effective value of the driving behavior data and determines a risk value of the second environmental data.
After the automatic driving system acquires the driving behavior data and the second environment data, the effective value of the driving behavior data and the risk value of the second environment data are respectively calculated.
In this embodiment, the automatic driving system calculates the effective value by using a first formula, and calculates the risk value by using a second formula; wherein the first formula is:
the second formula is:
wherein, the
Representing risk field data available to the autopilot system within a range perceived during recording of the data; the
Risk assessment data representing a user event behavior of the autonomous driving system with respect to the driving behavior; the n and the k are constants and equal to the number of data recorded by the automatic driving system; the r is a risk value of the second environment data; s isiObtaining position information of an obstacle near the self-vehicle for the automatic driving system; i is a constant. Meanwhile, the user event behavior includes operations of a curve, passing, sudden stop, and the like. When the first formula is used to calculate the effective value, the automatic driving system may also perform an evaluation in combination with actual constraints of the current road, for example, the actual constraints include that the current road speed cannot exceed 60, a turn is not allowed, or a whistle is not allowed.
403. The autopilot system determines whether the effective value and the first threshold satisfy a first condition, if so, go to step 404, and if not, go to step 406.
After determining the effective value and the risk value, the automatic driving system determines whether the effective value and the first threshold satisfy a first condition according to a third formula; if so, the step 404 is performed, otherwise, the step 406 is performed. Wherein the third formula is:
wherein the threshold is1And the thrshold2Is a first threshold.
404. The autopilot system determines whether the risk value satisfies a second condition, if so, step 405 is performed, and if not, step 406 is performed.
After the automatic driving system confirms that the effective value of the driving behavior data and the first threshold satisfy the first condition, the risk value of the second environment data of the driving behavior at this time is judged, if the risk value satisfies the second condition, the automatic driving system executes step 405, and if not, the automatic driving system executes step 406.
405. The automatic driving system saves the driving behavior data as the pre-stored driving behavior data.
The automatic driving system stores the driving behavior data as the pre-stored driving behavior data, wherein the target area is used as an index and is in one-to-one correspondence with the pre-stored driving behavior data. Meanwhile, the automatic driving system can also take the preset range of the target area as an index of the pre-stored driving behavior data. For example, the target area and the preset range of the target area are used as the identification area, and the vehicle can directly acquire the pre-stored driving behavior data when passing through the identification area.
406. The autopilot system discards the driving behavior data.
It is understood that the automatic driving system may also perform feature extraction on the driving behavior data, and perform the operations 401 to 406 described above using the feature data.
When the automatic driving system performs feature extraction, feature data of the driving behavior data can be obtained by adopting a supervision regression mode. Specifically, the description will be given with reference to the velocity.
Assume that the driving behavior data recorded by the autopilot system is as follows:
position information:
time:
speed:
wherein, the speed is subjected to characteristic extraction, and a polynomial is taken
As a kernel function, where θ ═ a, b, c, d]Is a target characteristic parameter. According to the supervised regression mode, the characteristic data of the speed is obtained as
A specific application scenario will be described below, where a shown in fig. 5 and B shown in fig. 5 are the same curve point 500, where a shown in fig. 5 is a scenario when the vehicle passes through in a manual driving manner, and B shown in fig. 5 is a scenario when the vehicle passes through in an automatic driving manner. In a shown in fig. 5, when a driver drives a vehicle 501 through the curve 500, the driving behavior 502 of the vehicle is recorded by an automatic driving system of the vehicle, and after the behavior evaluation is completed according to the current site environment, whether the operation behavior 502 corresponds to data that can be referred to by the automatic driving system or the driving-assisted automatic cruise function is determined, and if so, the driving behavior 502 is saved as the driving behavior data 503. Then, when the user passes through the same curve location 500 through the automatic driving function or the assisted driving auto cruise function, the automatic driving system extracts the driving behavior data 503, and adjusts the driving behavior data 503 according to the current environmental data to obtain a final driving strategy 504, and finally the vehicle passes through the curve 500 according to the driving strategy 504.
The automatic driving method in the embodiment of the present application is described above, and the automatic driving system in the embodiment of the present application is explained below:
specifically, referring to fig. 6, the automatic driving system device 600 in the embodiment of the present application includes: an acquisition module 601, a processing module 602 and an execution module 603. The apparatus 600 may be the autopilot system in the above-described method embodiment, or may be one or more chips in the autopilot system. The apparatus 600 may be used to perform some or all of the functions of the autopilot system in the above-described method embodiments.
For example, the obtaining module 601 may be configured to perform steps 201 to 202 or perform step 401 in the foregoing method embodiment. For example, the obtaining module 601 obtains pre-stored driving behavior data, which is used for representing driving behavior data when the vehicle passes through a target area under the condition of manual driving; acquiring first environment data of the target area, wherein the first environment data is currently acquired in real time;
the processing module 602 may be configured to perform step 203 in the above method embodiment, or to perform steps 402 to 404. For example, the processing module 602 determines a target driving strategy based on the first environmental data and the pre-stored driving behavior data.
The executing module 603 may be configured to execute step 204 in the foregoing method embodiment. For example, the executive module 603 performs autonomous driving through the target area in accordance with the target driving strategy.
Optionally, the apparatus 600 further includes a storage module coupled to the processing module, so that the processing module can execute the computer-executable instructions stored in the storage module to implement the functions of the station in the above-described method embodiments. In an example, the storage module optionally included in the apparatus 600 may be a storage unit within a chip, such as a register, a cache, or the like, and the storage module may also be a storage unit located outside the chip, such as a read-only memory (ROM) or another type of static storage device that can store static information and instructions, a Random Access Memory (RAM), or the like.
It should be understood that the flow executed between the modules of the automatic driving system in the embodiment corresponding to fig. 6 is similar to the flow executed by the automatic driving system in the corresponding method embodiment in fig. 2 to fig. 4, and detailed description thereof is omitted here.
Fig. 7 shows a possible structure diagram of an automatic driving system device 700 in the above embodiment, and the device 700 may be configured as the automatic driving system. The apparatus 700 may include: a processor 702, a computer-readable storage medium/memory 703, a transceiver 704, an input device 705 and an output device 706, and a bus 701. Wherein the processor, transceiver, computer readable storage medium, etc. are connected by a bus. The embodiments of the present application do not limit the specific connection medium between the above components.
In one example, the processor 702 obtains pre-stored driving behavior data representing driving behavior data of the vehicle as it passes through the target area under manual driving; the transceiver 704 acquires first environment data of the target area, wherein the first environment data is currently acquired in real time; the processor 702 determines a target driving strategy based on the first environmental data and the pre-stored driving behavior data; and finishing automatic driving to pass through the target area according to the target driving strategy.
In one example, the processor 702 may include baseband circuitry, e.g., may generate location request information. The transceiver 704 may include radio frequency circuitry to modulate, amplify, etc., the location request information for transmission to the location device.
In yet another example, the processor 702 may run an operating system that controls functions between various devices and appliances. The transceiver 704 may include baseband circuitry and radio frequency circuitry, for example, where the location request information may be processed by the baseband circuitry and the radio frequency circuitry for transmission to the location device.
The transceiver 704 and the processor 702 may implement corresponding steps in any one of the embodiments of fig. 2 to fig. 4, which are not described herein in detail.
It is understood that fig. 7 merely illustrates a simplified design of an autopilot system, and that in practical applications an autopilot system may include any number of transceivers, processors, memories, etc., and that all autopilot systems that may implement the present application are within the scope of the present application.
The processor 702 involved in the apparatus 700 may be a general-purpose processor, such as a general-purpose Central Processing Unit (CPU), a Network Processor (NP), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program according to the present application. But also a Digital Signal Processor (DSP), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The controller/processor can also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors, among others. Processors typically perform logical and arithmetic operations based on program instructions stored within memory.
The bus 701 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.
The computer-readable storage medium/memory 703 referred to above may also hold an operating system and other application programs. In particular, the program may include program code including computer operating instructions. More specifically, the memory may be a read-only memory (ROM), other types of static storage devices that may store static information and instructions, a Random Access Memory (RAM), other types of dynamic storage devices that may store information and instructions, a disk memory, and so forth. The memory 703 may be a combination of the above memory types. And the computer-readable storage medium/memory described above may be in the processor, may be external to the processor, or distributed across multiple entities including the processor or processing circuitry. The computer-readable storage medium/memory described above may be embodied in a computer program product. By way of example, a computer program product may include a computer-readable medium in packaging material.
Alternatively, embodiments of the present application also provide a general-purpose processing system, such as that commonly referred to as a chip, including one or more microprocessors that provide processor functionality; and an external memory providing at least a portion of the storage medium, all connected together with other supporting circuitry through an external bus architecture. The memory stored instructions, when executed by the processor, cause the processor to perform some or all of the steps of the autopilot system in the autopilot method of the embodiments described in fig. 2-4 and/or other processes for the techniques described herein.
The steps of a method or algorithm described in connection with the disclosure herein may be embodied in hardware or in software instructions executed by a processor. The software instructions may consist of corresponding software modules that may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. Additionally, the ASIC may reside in user equipment. Of course, the processor and the storage medium may reside as discrete components in user equipment.
A possible schematic block diagram of the autopilot system is described below, with particular reference to fig. 8:
the automatic driving system comprises a characteristic processing system 801, a sensor and navigation system 802 and a driving control system 803;
the sensor and navigation system 802 includes, but is not limited to, cameras, millimeter wave radar, lidar, ultrasonic radar, inertial sensors, satellite positioning, and digital maps. During the manual driving phase, the driving behavior data and the environmental data collected by the sensor and navigation system 802 are submitted to the sensor data processing 8031 or the driving behavior planning 8032 in the driving control system 803. The sensor data processing 8031 would then submit the driving behavior data and environmental data to the feature processing system 801.
The feature processing system 801 includes a behavior feature extraction module 8011, a behavior evaluation module 8012, a matching plan module 8013, and a feature database 8014. In the manual driving stage, the behavior feature extraction module 8011 is configured to perform behavior data recording M8011a (i.e., record driving behavior data of the vehicle passing through the target area) when the vehicle passes through the target area in a manual driving manner; then, performing data processing (such as optimized regression) on the driving behavior data M8011 b; and finally, performing behavior feature extraction on the driving behavior data M8011 c. After the behavior feature extraction is completed, the feature processing system 801 submits feature data to the behavior evaluation module 8012. The behavior evaluation module 8012 is responsible for evaluating the characteristic data of the driving behavior data. In practical situations, the driving behavior of the driver may produce different operation results due to the influence of the environmental data. For example, the external vehicles are more, the road is narrow, traffic jam is caused, or the external vehicles suddenly turn to cause the self vehicle to suddenly turn, and the like. At the moment, the automatic driving system needs to perform rationality evaluation on the behavior feature M8012a, the environmental risk M8012b, the driver perception M8012c and the abnormal event M8012d, and then gives a behavior result M8012e, so that the manual driving behavior data are effective and reasonable. For example, it is not a reasonable situation if traffic jam or sudden lane change occurs. The details are not described here. When the behavior result M8012e shows that the driving behavior data is reasonable data, the driving behavior data and the target area are stored in the feature database 8014 in a one-to-one correspondence relationship.
During the autopilot phase, the sensor and navigation system 802 sends the collected data to the driving control system 803; then the sensor data processing 8031 in the driving control system 803 submits the processed data to the driving behavior plan 8032, and the driving behavior plan 8032 extracts the driving behavior data from the feature database 8014; finally, the driving behavior plan 8032 sends the data obtained by the driving data and sensor data processing 8031 to the vehicle control calculation 8033; finally the vehicle control calculation 8033 obtains the final driving strategy and submits the final driving strategy to the vehicle control execution 8034; the vehicle control executive 8034 controls the vehicle according to driving strategies to ultimately achieve a safer autopilot effect experience based on driver behavior characteristics.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (15)
1. An automatic driving method, characterized by comprising:
the method comprises the steps of obtaining pre-stored driving behavior data, wherein the pre-stored driving behavior data are used for representing driving behavior data when a vehicle passes through a target area under the condition of manual driving;
acquiring first environment data of the target area, wherein the first environment data is currently acquired in real time;
determining a target driving strategy according to the first environmental data and the prestored driving behavior data;
and finishing automatic driving to pass through the target area according to the target driving strategy.
2. The method of claim 1, further comprising:
acquiring a real-time position of the vehicle;
and triggering and acquiring the pre-stored driving behavior data if the distance between the real-time position and the target area reaches a preset threshold value.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
acquiring driving behavior data and second environment data of the vehicle, wherein the second environment data is data when the vehicle passes through the target area under the condition of manual driving, and the driving behavior data is data when the vehicle passes through the target area under the condition of manual driving;
evaluating the second environmental data and the driving behavior data to obtain a first evaluation value;
and when the first evaluation value indicates that the driving behavior data meet a preset condition, storing the driving behavior data as the pre-stored driving behavior data.
4. The method according to claim 1 or 2, characterized in that the method further comprises:
acquiring driving behavior data and second environment data of the vehicle, wherein the second environment data is data when the vehicle passes through the target area under the condition of manual driving, and the driving behavior data is data when the vehicle passes through the target area under the condition of manual driving;
obtaining first characteristic data of the driving behavior data and second characteristic data of the second environment data by using a supervised regression algorithm;
evaluating the first characteristic data and the second characteristic data to obtain a second evaluation value;
and when the second evaluation value indicates that the driving behavior data meet preset conditions, storing the first characteristic data as the pre-stored driving behavior data.
5. The method according to claim 3, characterized in that the first evaluation value includes an effective value of the driving behavior data and a risk value of the second environmental data;
the effective value is obtained according to a first formula, and the risk value is obtained according to a second formula;
wherein the first formula is:
the second formula is:
wherein, the
Representing risk field data available within a sensing range during recording of the data; the above-mentioned
Risk assessment data representing user event behavior in the driving behavior data; n and k are constants equal to the number of recorded data; the r is a risk value of the second environment data; s isiPosition information of obstacles near the vehicle; the i is a constant.
6. The method of claim 3, wherein storing the driving behavior data as the pre-stored driving behavior data comprises:
storing the driving behavior data and the target area according to a position index, wherein the driving behavior data is used as the pre-stored driving behavior data;
or the like, or, alternatively,
and storing the driving behavior data, the target area and a preset range of the target area according to a position index, wherein the driving behavior data is used as the pre-stored driving behavior data.
7. An autopilot system, comprising:
the acquisition module is used for acquiring prestored driving behavior data, and the prestored driving behavior data is used for representing the driving behavior data when the vehicle passes through the target area under the condition of manual driving; acquiring first environment data of the target area, wherein the first environment data is currently acquired in real time;
the processing module is used for determining a target driving strategy according to the first environment data and the prestored driving behavior data;
and the execution module is used for finishing automatic driving to pass through the target area according to the target driving strategy.
8. The autopilot system of claim 7 wherein the acquisition module is further configured to acquire a real-time location of the vehicle;
the processing module is further configured to trigger and acquire the pre-stored driving behavior data if the distance between the real-time position and the target area reaches a preset threshold.
9. The autopilot system of claim 7 or 8 wherein the acquisition module is further configured to acquire driving behavior data of the vehicle and second environmental data, the second environmental data being data of the vehicle as it passes through the target area under manual driving, the driving behavior data being data of the vehicle as it passes through the target area under manual driving;
the processing module is further configured to evaluate the second environmental data and the driving behavior data to obtain a first evaluation value;
and the storage module is used for storing the driving behavior data as the pre-stored driving behavior data when the first evaluation value indicates that the driving behavior data accords with a preset condition.
10. The autopilot system of claim 7 or 8 wherein the acquisition module is further configured to acquire driving behavior data of the vehicle and second environmental data, the second environmental data being data of the vehicle as it passes through the target area under manual driving, the driving behavior data being data of the vehicle as it passes through the target area under manual driving;
the processing module is further used for obtaining first characteristic data of the driving behavior data and second characteristic data of the second environment data by using a supervised regression algorithm; evaluating the first characteristic data and the second characteristic data to obtain a second evaluation value;
and the storage module is used for storing the first characteristic data as the pre-stored driving behavior data when the second evaluation value indicates that the driving behavior data meet the preset conditions.
11. The automatic driving system according to claim 9, wherein the first evaluation value includes an effective value of the driving behavior data and a risk value of the second environmental data;
the effective value is obtained according to a first formula, and the risk value is obtained according to a second formula;
wherein the first formula is:
the second formula is:
wherein, the
Representing risk field data available within a sensing range during recording of the data; the above-mentioned
Risk assessment data representing user event behavior in the driving behavior data; n and k are constants equal to the number of recorded data; the r is a risk value of the second environment data; s isiPosition information of obstacles near the vehicle; the i is a constant.
12. The autopilot system of claim 9, wherein the storage module is configured to store the driving behavior data with the target area according to a location index, the driving behavior data serving as the pre-stored driving behavior data;
or the like, or, alternatively,
and storing the driving behavior data, the target area and a preset range of the target area according to a position index, wherein the driving behavior data is used as the pre-stored driving behavior data.
13. An autopilot system, comprising: a processor and a memory, wherein the memory has a computer readable program stored therein, and the processor is configured to execute the program in the memory to perform the method of any of claims 1 to 6.
14. A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method of claims 1 to 6 above.
15. A computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811614039.7A CN111399490B (en) | 2018-12-27 | 2018-12-27 | Automatic driving method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811614039.7A CN111399490B (en) | 2018-12-27 | 2018-12-27 | Automatic driving method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111399490A true CN111399490A (en) | 2020-07-10 |
| CN111399490B CN111399490B (en) | 2021-11-19 |
Family
ID=71431984
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811614039.7A Active CN111399490B (en) | 2018-12-27 | 2018-12-27 | Automatic driving method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111399490B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113344345A (en) * | 2021-05-21 | 2021-09-03 | 深圳裹动智驾科技有限公司 | Data processing method and device for automatic driving vehicle and computer equipment |
| CN113954858A (en) * | 2020-07-20 | 2022-01-21 | 华为技术有限公司 | Method for planning vehicle driving route and intelligent automobile |
| WO2022179531A1 (en) * | 2021-02-23 | 2022-09-01 | 北京三快在线科技有限公司 | Self-driving control, and unmanned device |
| CN115221151A (en) * | 2022-07-13 | 2022-10-21 | 小米汽车科技有限公司 | Vehicle data transmission method and device, vehicle, storage medium and chip |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102800136A (en) * | 2011-05-25 | 2012-11-28 | 株式会社审调社 | Drive evaluation system, drive evaluation program, and drive evaluation method |
| WO2017018842A1 (en) * | 2015-07-30 | 2017-02-02 | 삼성전자 주식회사 | Apparatus and method for controlling an autonomous driving vehicle |
| CN107531252A (en) * | 2015-04-21 | 2018-01-02 | 松下知识产权经营株式会社 | Driving assistance method and make use of the drive assistance device of the driving assistance method, automatic Pilot control device, vehicle, drive auxiliary program |
| CN108139756A (en) * | 2016-08-29 | 2018-06-08 | 百度（美国）有限责任公司 | Ambient enviroment is built for automatic driving vehicle to formulate the method and system of Driving Decision-making |
| CN108357496A (en) * | 2018-02-12 | 2018-08-03 | 北京小马智行科技有限公司 | Automatic Pilot control method and device |
| CN108734303A (en) * | 2018-05-29 | 2018-11-02 | 深圳市易成自动驾驶技术有限公司 | Vehicle drive data predication method, equipment and computer readable storage medium |
| CN108974009A (en) * | 2017-06-02 | 2018-12-11 | 百度（美国）有限责任公司 | Method, medium and system for automatic Pilot control |
-
2018
- 2018-12-27 CN CN201811614039.7A patent/CN111399490B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102800136A (en) * | 2011-05-25 | 2012-11-28 | 株式会社审调社 | Drive evaluation system, drive evaluation program, and drive evaluation method |
| CN107531252A (en) * | 2015-04-21 | 2018-01-02 | 松下知识产权经营株式会社 | Driving assistance method and make use of the drive assistance device of the driving assistance method, automatic Pilot control device, vehicle, drive auxiliary program |
| WO2017018842A1 (en) * | 2015-07-30 | 2017-02-02 | 삼성전자 주식회사 | Apparatus and method for controlling an autonomous driving vehicle |
| CN108139756A (en) * | 2016-08-29 | 2018-06-08 | 百度（美国）有限责任公司 | Ambient enviroment is built for automatic driving vehicle to formulate the method and system of Driving Decision-making |
| CN108974009A (en) * | 2017-06-02 | 2018-12-11 | 百度（美国）有限责任公司 | Method, medium and system for automatic Pilot control |
| CN108357496A (en) * | 2018-02-12 | 2018-08-03 | 北京小马智行科技有限公司 | Automatic Pilot control method and device |
| CN108734303A (en) * | 2018-05-29 | 2018-11-02 | 深圳市易成自动驾驶技术有限公司 | Vehicle drive data predication method, equipment and computer readable storage medium |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113954858A (en) * | 2020-07-20 | 2022-01-21 | 华为技术有限公司 | Method for planning vehicle driving route and intelligent automobile |
| WO2022179531A1 (en) * | 2021-02-23 | 2022-09-01 | 北京三快在线科技有限公司 | Self-driving control, and unmanned device |
| CN113344345A (en) * | 2021-05-21 | 2021-09-03 | 深圳裹动智驾科技有限公司 | Data processing method and device for automatic driving vehicle and computer equipment |
| CN115221151A (en) * | 2022-07-13 | 2022-10-21 | 小米汽车科技有限公司 | Vehicle data transmission method and device, vehicle, storage medium and chip |
| CN115221151B (en) * | 2022-07-13 | 2024-02-02 | 小米汽车科技有限公司 | Vehicle data transmission method and device, vehicle, storage medium and chip |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111399490B (en) | 2021-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111399490B (en) | Automatic driving method and device | |
| CN110248861B (en) | Guiding a vehicle using a machine learning model during vehicle maneuvers | |
| CN111123933B (en) | Vehicle track planning method and device, intelligent driving area controller and intelligent vehicle | |
| US10558217B2 (en) | Method and apparatus for monitoring of an autonomous vehicle | |
| US10703363B2 (en) | In-vehicle traffic assist | |
| JP2018534692A (en) | Method for determining driving intention for a vehicle and vehicle communication system | |
| US20140257686A1 (en) | Vehicle lane determination | |
| EP3745376B1 (en) | Method and system for determining driving assisting data | |
| CN110562222B (en) | Emergency braking control method for curve scene, vehicle-mounted device and storage medium | |
| EP3489926A1 (en) | Lane change assistance method and lane change assistance device | |
| JP7466396B2 (en) | Vehicle control device | |
| JP6638172B2 (en) | Vehicle control device, vehicle control method, and program | |
| CN114373295B (en) | Driving safety early warning method, driving safety early warning system, storage medium and driving safety early warning equipment | |
| CN110936960A (en) | Driving assisting method and system | |
| WO2022151369A1 (en) | Information processing method and related apparatus | |
| JP2023085371A (en) | Travel storage system and travel storage method | |
| CN110375786B (en) | Calibration method of sensor external parameter, vehicle-mounted equipment and storage medium | |
| WO2020049685A1 (en) | Vehicle control device, automatic-drive vehicle development system, vehicle control method, and program | |
| US20230294731A1 (en) | Traveling control apparatus for vehicle | |
| JP7147448B2 (en) | map information system | |
| US20220204040A1 (en) | Vehicle driving control system and vehicle traffic control apparatus | |
| JP2020053069A (en) | On-vehicle electronic control device | |
| JP7387068B2 (en) | Driving area determination device and driving area determination method | |
| US20220203980A1 (en) | Vehicle driving control system | |
| US20230202497A1 (en) | Hypothesis inference for vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |