CN113428168A - Control method, system and medium for automatically driving vehicle and automobile - Google Patents
Control method, system and medium for automatically driving vehicle and automobile Download PDFInfo
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- CN113428168A CN113428168A CN202010196850.9A CN202010196850A CN113428168A CN 113428168 A CN113428168 A CN 113428168A CN 202010196850 A CN202010196850 A CN 202010196850A CN 113428168 A CN113428168 A CN 113428168A
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/082—Selecting or switching between different modes of propelling
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
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Abstract
The invention provides a method, a system and a medium for controlling an automatic driving vehicle and an automobile. The autonomous vehicle control method includes: acquiring fault related parameters of a vehicle; acquiring the automatic driving reliability probability according to the fault related parameters; and determining the current driving mode according to the automatic driving reliability probability. The automatic driving vehicle control method does not need a driver to subjectively judge the current driving mode, and improves the driving experience.
Description
Technical Field
The invention belongs to the field of automotive electronics, relates to an automatic driving vehicle control method, and particularly relates to an automatic driving vehicle control method, an automatic driving vehicle control system, a medium and an automobile.
Background
The automatic driving automobile is also called unmanned automobile and computer driving automobile, and is one intelligent automobile with unmanned driving realized via computer system. Referring to fig. 1, a diagram of an autopilot system for an autopilot vehicle is shown. The automatic driving system integrally consists of a sensing module, a decision-making module and a control module; the sensing module is used for collecting information of an automobile, environment and a driver, the decision module is used for processing the collected information and making a decision, and the control module is used for controlling the running state of the automobile according to the decision. In addition, the decision module can realize the communication between the automobile and external equipment through the T-BOX. However, the automatic driving system cannot guarantee 100% safe operation of the automobile, and in the practical application process, automatic driving or manual driving needs to be selected according to the specific driving situation of the automobile, and the selection is often determined by subjective judgment of a driver, which requires the driver to pay attention to the driving situation of the automobile at any time, and reduces the driving experience of the automobile.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, an object of the present invention is to provide an autonomous vehicle control method, system, medium, and vehicle, which are used to solve the problem of determining a driving mode depending on subjective judgment of a driver in the prior art.
To achieve the above and other related objects, the present invention provides a control method of an autonomous vehicle; the autonomous vehicle control method includes: acquiring fault related parameters of a vehicle; acquiring the automatic driving reliability probability according to the fault related parameters; and determining the current driving mode according to the automatic driving reliability probability.
In an embodiment of the present invention, the method for controlling an autonomous vehicle further includes: acquiring fault conditions of a vehicle in multiple trips and vehicle operation parameters corresponding to the trips; obtaining a correlation between each of the vehicle operating parameters and the fault condition; and selecting corresponding vehicle operation parameters as fault related parameters according to the correlation.
In an embodiment of the invention, the vehicle operation parameters include a vehicle historical operation parameter and a current trip parameter.
In one embodiment of the present invention, the historical operating parameters of the vehicle include an average fault-free operating distance, an average fault-free time, a unit distance fault frequency and/or an average first fault distance; and/or the current trip parameter comprises weather, road conditions, time of travel, fuel consumption, and/or mileage.
In an embodiment of the present invention, the method for calculating the automatic driving reliability probability according to the fault-related parameter includes: taking the fault-related parameters as the input of a first neural network model, wherein the output of the first neural network model is the automatic driving reliability probability; the training method of the first neural network model comprises the following steps: acquiring the reliability probability of the vehicle in multiple trips and fault related parameters corresponding to the trips; and training a neural network model by using the fault related parameters and the reliability probability to obtain the first neural network model.
In an embodiment of the present invention, the method for controlling an autonomous vehicle further includes: and evaluating the current driving safety according to the reliability probability and displaying the evaluation result to the user.
In an embodiment of the present invention, the driving mode includes: an automatic driving mode, an assisted driving mode, and a manual driving mode.
In an embodiment of the present invention, the program is executed by a processor to perform the method for controlling an autonomous vehicle according to the present invention.
In one embodiment of the present invention, the automatic driving vehicle control system includes: the parameter acquisition module is used for acquiring fault related parameters of the vehicle; the probability calculation module is connected with the parameter acquisition module and used for acquiring the automatic driving reliability probability according to the fault related parameters; and the mode selection module is connected with the probability calculation module and used for determining the current driving mode according to the automatic driving reliability probability.
In an embodiment of the present invention, the vehicle includes: the automatic driving system is used for realizing automatic driving of the automobile; the automatic driving vehicle control system is connected with the automatic driving system and used for determining the current driving mode according to the automatic driving reliability probability.
As described above, the method, system, medium, and automobile for controlling an autonomous vehicle according to the present invention have the following advantageous effects: the automatic driving vehicle control method can acquire the reliability probability of automatic driving according to the fault related parameters of the vehicle, determine the current driving mode according to the reliability probability, and improve the driving experience of the driver without paying attention to the current running condition of the vehicle and subjectively selecting the driving mode.
Drawings
FIG. 1 is a block diagram of an embodiment of an autopilot system.
FIG. 2 is a flowchart illustrating an exemplary embodiment of an automatic vehicle control method according to the present invention.
FIG. 3 is a flowchart illustrating an exemplary method of controlling an autonomous vehicle according to the present invention.
FIG. 4 is a flowchart illustrating a neural network training process of the method for controlling an autonomous vehicle according to an embodiment of the present invention.
FIG. 5 is a block diagram of an embodiment of an autonomous vehicle control system according to the present invention.
Description of the element reference numerals
5 automatic driving vehicle control system
51 parameter acquisition module
52 probability calculation module
53 mode selection module
S21-S23
S31-S33
S41-S42
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. 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 is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
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 components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Referring to fig. 1, in some embodiments, an autopilot system is generally comprised of a sensing module, a decision module, and a control module; wherein: the sensing module is used for collecting information of an automobile, environment and a driver, the decision module is used for processing the collected information and making a decision, and the control module is used for controlling the running state of the automobile according to the decision. In addition, the decision module can realize the communication between the automobile and external equipment through the T-BOX. However, the automatic driving system cannot guarantee 100% safe operation of the automobile, and in the practical application process, automatic driving or manual driving needs to be selected according to the specific driving situation of the automobile, and the selection is often determined by subjective judgment of a driver, and the driver is required to observe the current driving situation of the automobile and make a judgment, so that the driving experience of the automobile is reduced. To address this problem, the present invention provides an autonomous vehicle control method including: acquiring fault related parameters of a vehicle; acquiring the automatic driving reliability probability according to the fault related parameters; and determining the current driving mode according to the automatic driving reliability probability. The automatic driving vehicle control method can acquire the reliability probability of automatic driving according to the fault related parameters of the vehicle, and determine the proper driving mode according to the reliability probability, and the whole process basically does not need the participation of a driver, thereby reducing the driving burden of the driver and improving the driving experience.
Referring to fig. 2, in an embodiment of the present invention, the method for controlling an autonomous vehicle includes:
and S21, acquiring the fault related parameters of the vehicle. The vehicle is a vehicle provided with an automatic driving system, and the fault-related parameter indicates a parameter related to whether the vehicle is faulty or not.
And S22, acquiring the automatic driving reliability probability according to the fault related parameters. The reliability probability refers to the probability that the autonomous vehicle will not fail during the trip.
And S23, determining the current driving mode according to the automatic driving reliability probability.
In this embodiment, the driving mode is obtained by the vehicle control system according to the fault-related parameter of the vehicle, and ideally, the driver does not need to participate in the selection of the driving mode. Therefore, the automatic driving vehicle control method does not need the driver to pay attention to the driving condition of the current vehicle all the time, reduces the driving burden of the driver, and improves the driving experience.
In an embodiment of the present invention, the method for controlling an autonomous vehicle further includes: notifying the driver of the driving mode determined at step S23 in the form of voice; the driver can select automatic driving or manual driving with reference to the driving mode determined at step S23.
Referring to fig. 3, in an embodiment of the present invention, the method for controlling an autonomous vehicle further includes:
and S31, acquiring fault conditions of the vehicle in multiple trips and vehicle operation parameters corresponding to the trips. Specifically, for any trip, whether an automatic driving fault occurs in the trip and vehicle operation parameters in the trip need to be acquired; the automatic driving failure is, for example: traffic accidents, speeding, running red light, driving without road signs and the like occur.
And S32, acquiring the correlation degree between each vehicle running parameter and the fault condition. In this embodiment, the correlation is used to reflect the correlation between the vehicle operating parameter and the condition. For example: the variable y is used to indicate whether the vehicle has failed in a certain trip: when y is 1, the vehicle is indicated to be in fault in the journey; when y is 0, the vehicle is not in fault in the journey. The degree of correlation between the vehicle operation parameter and whether the vehicle is in failure can be obtained by calculating the degree of correlation between the variable y and the vehicle operation parameter. The calculation method of the correlation degree can be realized by a correlation coefficient calculation formula, a machine learning model and the like, and the formula for calculating the correlation degree by using the correlation coefficient calculation formula is as follows:
wherein x represents any vehicle operating parameter, and y represents whether the vehicle has a accident or notAnd (4) a barrier. Cov (x, y) denotes the covariance of the random variable x and the random variable y, and is E (x × y) -E (x) × E (y); e (x) represents the mean of the random variable x, e (y) represents the mean of the random variable y; var (x) denotes the variance of the random variable x, and Var (y) denotes the variance of the random variable y.
And S33, selecting corresponding vehicle operation parameters as fault related parameters according to the correlation. Specifically, the higher the correlation corresponding to a certain vehicle operation parameter is, the higher the correlation between the vehicle operation parameter and whether the vehicle is in failure is indicated; conversely, a lower degree of correlation for a certain vehicle operation parameter indicates a lower relevance between the vehicle operation parameter and whether the vehicle is malfunctioning. In order to reduce the amount of data processed in the automatic driving vehicle control method, in the present embodiment, some vehicle operation parameters having a high degree of correlation may be selected as the failure-related parameters. For example, a vehicle operation parameter having a correlation coefficient greater than 0.9 may be selected as the fault-related parameter.
In this embodiment, by analyzing the correlation between the vehicle operation parameters and the fault condition and selecting a part of the vehicle operation parameters as the fault related parameters based on the correlation, the processing amount of data can be effectively reduced, and the execution time of the algorithm is shortened, so that the automatic driving vehicle control method can be ensured to determine the current driving mode in time, and safety accidents and illegal driving behaviors caused by delay are avoided.
In an embodiment of the invention, the vehicle operation parameters include a vehicle historical operation parameter and a current trip parameter. In particular, the probability of reliability of the vehicle on a certain trip is related to historical operating parameters of the vehicle, such as: for a vehicle that frequently fails in a historical trip, the probability of reliability is lower than for a vehicle that never failed in a historical trip. Furthermore, the reliability probability of the vehicle in a certain trip is also related to the relevant parameters formed at that time, such as: road conditions, vehicle density, visibility, etc.
In an embodiment of the present invention, the historical operating parameters of the vehicle include an average fault-free operating distance, an average fault-free time, a unit distance fault frequency and/or an average first fault distance. Wherein the mean operating range is defined as the average operating range of the vehicle between two driving behavior failures; the mean time to failure is defined as the average run time of the vehicle between two behavioral failures; the unit-mileage fault frequency is defined as the number of times of unreliable driving behaviors of the vehicle within a unit kilometer; the average first fault mileage is defined as the average running mileage from the vehicle after leaving the factory to the time when the first behavior fault occurs.
In this embodiment, the average fault-free operation distance, the average fault-free time, the unit distance fault frequency and/or the average first fault distance can sufficiently reflect the fault state of the vehicle in the historical trip, and incorporating the historical operation parameters into the vehicle operation parameters can effectively improve the accuracy of the reliable probability calculation in step S22.
In an embodiment of the present invention, the current journey parameter includes weather, road condition, driving time, fuel consumption, mileage, fuel amount, and the like. Parameters such as weather, road conditions, driving time, fuel consumption, mileage, and fuel consumption all affect the reliability of automatic driving, for example: the probability of the automatic driving failure in sunny days is generally smaller than that in rainy days; the probability of failure of autonomous driving on a straight smooth road is generally less than the probability of failure of autonomous driving on a curved rough road. In this embodiment, the accuracy of the reliability probability calculation in step S22 can be effectively improved by incorporating the current trip parameter into the vehicle operation parameter.
In one embodiment of the present invention, the historical operating parameters of the vehicle include an average fault-free operating distance, an average fault-free time, a unit distance fault frequency and/or an average first fault distance; and the current travel parameters comprise weather, road conditions, driving time, oil consumption and driving mileage.
In one embodiment of the present invention, a neural network model is used to calculate the autopilot reliability probability. Specifically, the fault-related parameter is used as an input of a neural network model, and an output of the first neural network model is the automatic driving reliability probability.
Referring to fig. 4, in the present embodiment, the training process of the neural network model includes the following steps:
and S41, acquiring the reliability probability of the vehicle in multiple trips and fault related parameters corresponding to the trips. The reliability probability of a trip can be evaluated by the fault-free running distance, the fault-free running time and the unit mileage fault frequency in the trip. In particular, when the automatic driving system in the trip fails, the automatic driving reliability probability in the trip can be considered to be 0; the probability of reliability of the automated driving for the trip may be considered to be 1 when the automated driving system does not malfunction during the trip.
S42, training a neural network model by using the fault related parameters and the reliability probability to obtain the first neural network model. Specifically, in the training process, the fault-related parameters and the reliability probabilities are used as training data and grouped to obtain a plurality of groups of training data, and each group of training data includes one reliability probability and fault-related parameters included in a trip corresponding to the reliability probability. The training of the neural network model by using the plurality of sets of training data can be realized by using the existing training scheme, and details are not repeated here.
In an embodiment of the present invention, the method for controlling an autonomous vehicle further includes: and evaluating the current driving safety according to the automatic driving reliability probability and displaying an evaluation result to a user.
Specifically, in the automatic driving process, the higher the automatic driving reliability probability obtained in step S22, the higher the safety when the automatic driving is adopted in the present trip is; the smaller the automated driving reliability probability obtained in step S22, the lower the safety when automated driving is employed for the present trip. One implementation method for evaluating the current driving safety comprises the following steps: dividing the interval of 0-1 into a plurality of sections of intervals, wherein each section of interval corresponds to one security level; and determining the safety level of the current driving according to the interval where the reliability probability is located.
The mode of showing the evaluation result to the user can be realized by voice broadcast, vehicle-mounted display screen display, warning sound and the like, and the technical scheme of the embodiment can be realized by the reminding mode of enabling the user to receive the evaluation result. The reminding mode of the evaluation result is not limited in the invention.
In this embodiment, the safety of the current driving is evaluated according to the reliability probability, and the evaluation result is displayed to the user, so that the user can timely know the safety of the current automatic driving, and the user can timely prepare for taking over the driving system when the safety is low, thereby avoiding safety accidents and illegal driving behaviors caused by faults of the automatic driving.
In an embodiment of the present invention, the value range 0-1 of the reliability probability is divided into 4 sections, and the probability ranges corresponding to each section are: 0-0.5, which means that automatic driving is basically unreliable and manual driving is required; 0.5-0.8, which means that the reliability of automatic driving is low, and manual driving is recommended; 0.8-0.9, which indicates that the automatic driving reliability is high, and advises the driver to improve the attention and prepare for taking over the driving system; 0.9-1, indicating that the automatic driving is reliable.
In an embodiment of the present invention, the driving mode includes: an automatic driving mode, an assisted driving mode, and a manual driving mode.
The automatic driving mode corresponds to fully automated driving, in which all driving operations can be performed by the automatic driving system and driving is performed on all roads and in all environments.
The manual driving mode corresponds to full manual driving, and the driver can operate the automobile in the mode completely; alternatively, the autopilot system may provide warning information to the driver and assistance from the protection system.
The assistant driving mode may be classified into a deep assistant driving mode and a shallow assistant driving mode. In the deep auxiliary driving mode, all driving operations are completed by the automatic driving system, and a driver can provide appropriate response according to the request of the automatic driving system; in the shallow assisted driving mode, the driving operation is mainly completed by the driver, and the automatic driving system can provide driving support for the relevant operation in the steering wheel and/or acceleration and deceleration according to the current environment.
In this embodiment, the appropriate driving mode is selected by the reliability probability: selecting an automatic driving mode when the automatic driving reliability probability is higher; selecting an automatic driving mode when the automatic driving reliability probability is lower; and selecting the auxiliary driving mode when the automatic driving reliability probability is low. The above-mentioned process can realize driving safety and the balance between the driver's operation complexity to the realization promotes driver's driving experience under the prerequisite of guaranteeing driving safety.
In an embodiment of the present invention, the process of the driving mode being changed from the manual driving mode to the auxiliary driving mode, from the auxiliary driving mode to the automatic driving mode, or from the manual driving mode to the automatic driving mode may be automatically completed by the automatic driving system, or the user may be prompted to manually complete the conversion in a voice manner. The process that the driving mode is converted from the automatic driving mode into the auxiliary driving mode, the auxiliary driving mode into the manual driving mode and the automatic driving mode into the manual driving mode needs to remind a user of manually completing conversion in the modes of voice and the like, so that the safety of the mode conversion process is improved, and traffic accidents and illegal driving behaviors caused by mode conversion are avoided.
In practical applications, most people have distrust on the automatic driving mode or the auxiliary driving technology, and most people do not wish to use automatic driving or auxiliary driving even under the condition of technical license. To address this problem, in an embodiment of the present invention, the method for controlling an autonomous vehicle further includes:
according to the reliability probability, parameters such as weather and road conditions are combined, and the safe driving behavior of the user is rewarded; and/or
And according to the reliability probability, scoring the automatic driving behaviors of the user, wherein the automatic driving behaviors comprise the driving track, the driving time, the oil consumption, the driving distance and the like of the driver, and corresponding rewards are given to the owner through the scoring result.
In this embodiment, the enthusiasm of the owner in using the automatic driving mode can be improved by rewarding the owner, and the popularization and the use of automatic driving and assistant driving are facilitated.
The present invention also provides a computer-readable storage medium having stored thereon a computer program; the computer program, when executed by a processor, implements the autonomous vehicle control method of the invention.
Referring to fig. 5, the present invention also provides an automatic driving vehicle control system 5. The autonomous vehicle control system 5 includes:
the parameter obtaining module 51 is configured to execute step S21, that is: acquiring fault related parameters of a vehicle;
the probability calculation module 52 is connected to the parameter obtaining module 51, and configured to execute step S22, that is: acquiring the automatic driving reliability probability according to the fault related parameters;
a mode selection module 53, connected to the probability calculation module 52, for implementing step S23, that is: and determining the current driving mode according to the automatic driving reliability probability.
In an embodiment of the present invention, the automatic driving vehicle control system 5 further includes:
an operation parameter obtaining module, configured to perform step S31, that is: acquiring fault conditions of a vehicle in multiple trips and vehicle operation parameters corresponding to the trips;
a correlation calculation module, connected to the operation parameter obtaining module, configured to execute step S32, that is: obtaining a correlation between each of the vehicle operating parameters and the fault condition;
a fault-related parameter selection module, connected to the correlation calculation module and the operation parameter acquisition module, respectively, for executing step S33, that is: and selecting corresponding vehicle operation parameters as fault related parameters according to the correlation.
In an embodiment of the invention, the vehicle operation parameters acquired by the operation parameter acquiring module include vehicle historical operation parameters and current trip parameters.
In an embodiment of the present invention, the historical operating parameters of the vehicle obtained by the operating parameter obtaining module include average fault-free operating mileage, average fault-free time, unit mileage fault frequency and/or average first fault mileage; and/or the current travel parameters acquired by the operation parameter acquisition module comprise weather, road conditions, driving time, oil consumption and driving mileage.
In an embodiment of the present invention, the method for calculating the probability of obtaining the automatic driving reliability according to the fault-related parameter by the probability calculation module includes: taking the fault-related parameters as the input of a first neural network model, wherein the output of the first neural network model is the automatic driving reliability probability; the training method of the first neural network model comprises the following steps: acquiring the reliability probability of the vehicle in multiple trips and fault related parameters corresponding to the trips; and training a neural network model by using the fault related parameters and the reliability probability to obtain the first neural network model.
In an embodiment of the present invention, the automatic driving vehicle control system further includes a safety evaluation module, connected to the probability calculation module, for evaluating the current driving safety according to the reliability probability and displaying the evaluation result to the user.
In an embodiment of the present invention, the mode selection module selects an automatic driving mode, an auxiliary driving mode or a manual driving mode according to the automatic driving reliability probability.
The present invention also provides an automobile comprising: the automatic driving system is used for realizing automatic driving of the automobile; and the automatic driving vehicle control system is connected with the automatic driving system and used for determining the current driving mode according to the automatic driving reliability probability.
The protection scope of the control method for the autonomous driving vehicle according to the present invention is not limited to the execution sequence of the steps illustrated in the embodiment, and all the solutions of the prior art including the steps addition, subtraction and step replacement according to the principle of the present invention are included in the protection scope of the present invention.
The present invention also provides an automatic driving vehicle control system, which can implement the automatic driving vehicle control method of the present invention, but the implementation device of the automatic driving vehicle control method of the present invention includes but is not limited to the structure of the automatic driving vehicle control system recited in the present embodiment, and all the structural modifications and substitutions of the prior art made according to the principle of the present invention are included in the protection scope of the present invention.
The automatic driving vehicle control method can acquire the reliability probability of automatic driving according to the fault related parameters of the vehicle, and determine the proper driving mode according to the reliability probability, the whole process basically does not need the participation of a driver, the driving burden of the driver is reduced, and the driving experience is improved;
the automatic driving vehicle control method can effectively reduce the data processing amount and shorten the execution time of the algorithm by analyzing the correlation degree between the vehicle operation parameters and the fault condition and selecting part of the vehicle operation parameters as the fault related parameters according to the correlation degree, thereby ensuring that the automatic driving vehicle control method can determine the current driving mode in time and avoiding safety accidents caused by delay;
the automatic driving vehicle control method determines the reliability probability of automatic driving according to the historical condition and the current condition of the vehicle, and the accuracy is higher.
In conclusion, the present invention effectively overcomes various disadvantages of the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned 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. An autonomous-vehicle control method characterized by comprising:
acquiring fault related parameters of a vehicle;
acquiring the automatic driving reliability probability according to the fault related parameters;
and determining the current driving mode according to the automatic driving reliability probability.
2. The autonomous-vehicle control method according to claim 1, characterized by further comprising:
acquiring fault conditions of a vehicle in multiple travels and vehicle operation parameters corresponding to each travel;
obtaining a correlation between each of the vehicle operating parameters and the fault condition;
and selecting corresponding vehicle operation parameters as fault related parameters according to the correlation.
3. The autonomous-vehicle control method according to claim 2, characterized in that: the vehicle operation parameters include vehicle historical operation parameters and current trip parameters.
4. The autonomous-vehicle control method according to claim 3, characterized in that:
the historical operating parameters of the vehicle comprise average fault-free operating mileage, average fault-free time, unit mileage fault frequency and/or average first fault mileage; and/or
The current trip parameters include weather, road conditions, travel time, fuel consumption, and/or mileage.
5. The automated driving vehicle control method according to claim 1, wherein the calculation method of obtaining the automated driving reliability probability based on the failure-related parameter includes:
taking the fault-related parameters as the input of a first neural network model, wherein the output of the first neural network model is the automatic driving reliability probability;
the training method of the first neural network model comprises the following steps:
acquiring the reliability probability of the vehicle in multiple trips and fault related parameters corresponding to the trips;
and training a neural network model by using the fault related parameters and the reliability probability to obtain the first neural network model.
6. The autonomous-vehicle control method according to claim 1, characterized by further comprising: and evaluating the current driving safety according to the reliability probability and displaying the evaluation result to the user.
7. The autonomous-vehicle control method of claim 1, wherein the driving mode includes: an automatic driving mode, an assisted driving mode, and a manual driving mode.
8. A computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the autonomous vehicle control method of any of claims 1 to 7.
9. An autonomous vehicle control system, comprising:
the parameter acquisition module is used for acquiring fault related parameters of the vehicle;
the probability calculation module is connected with the parameter acquisition module and used for acquiring the automatic driving reliability probability according to the fault related parameters;
and the mode selection module is connected with the probability calculation module and used for determining the current driving mode according to the automatic driving reliability probability.
10. An automobile, characterized in that the automobile comprises:
the automatic driving system is used for realizing automatic driving of the automobile;
the autonomous vehicle control system of claim 9, coupled to the autonomous system, for determining the current driving mode based on the autonomous reliability probability.
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