CN112389445A - Vehicle driving regulation and control method and device, vehicle-mounted equipment and readable storage medium - Google Patents
Vehicle driving regulation and control method and device, vehicle-mounted equipment and readable storage medium Download PDFInfo
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- CN112389445A CN112389445A CN202011223627.5A CN202011223627A CN112389445A CN 112389445 A CN112389445 A CN 112389445A CN 202011223627 A CN202011223627 A CN 202011223627A CN 112389445 A CN112389445 A CN 112389445A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
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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
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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
- B60W2050/0001—Details of the control system
- B60W2050/0002—Automatic control, details of type of controller or control system architecture
- B60W2050/0004—In digital systems, e.g. discrete-time systems involving sampling
- B60W2050/0005—Processor details or data handling, e.g. memory registers or chip architecture
Abstract
The embodiment of the application provides a vehicle driving regulation and control method, a vehicle driving regulation and control device, vehicle-mounted equipment and a readable storage medium, the method relates to the technical field of maps and artificial intelligence, and the method can comprise the following steps: for any time period, acquiring historical traffic accident probability corresponding to the road section where the vehicle is located in the time period; acquiring driving state information of each candidate driver corresponding to the time interval; and determining the target driver in the time period from the candidate drivers based on the driving state information of the candidate drivers corresponding to the time period and the historical traffic accident probability. In the embodiment of the application, the number of drivers required in each time interval can be dynamically and reasonably adjusted through the actual historical traffic accident probability of the road section where the vehicle is located and the driving state information of each candidate driver, and at the moment, the drivers do not need to be frequently replaced, so that the driver can be effectively prevented from fatigue driving, and the driving efficiency of the vehicle is ensured.
Description
Technical Field
The application relates to the technical field of maps and artificial intelligence, in particular to a vehicle driving regulation and control method, a vehicle driving regulation and control device, vehicle-mounted equipment and a readable storage medium.
Background
In actual driving, most freight drivers finish loading and start driving in the morning and start driving for a long time, the drivers are easily tired, and when too many vehicles are on the road, traffic accidents are easily caused. At present, in order to avoid traffic accidents easily caused by long-time driving of a driver, the fatigue of the driver is reduced by frequently replacing the driver of a truck, but the number of the drivers on the truck is limited, and the frequent replacement of the driver inevitably reduces the driving efficiency of the driver of the truck.
Disclosure of Invention
The application provides a vehicle driving regulation and control method, a vehicle-mounted device, and a readable storage medium, which can effectively ensure that a driver does not fatigue driving any more and ensure the driving efficiency of a vehicle.
In one aspect, an embodiment of the present application provides a vehicle driving regulation method, where the method includes:
for any time period, acquiring historical traffic accident probability corresponding to the road section where the vehicle is located in the time period;
acquiring driving state information of each candidate driver corresponding to the time interval;
and determining the target driver in the time period from the candidate drivers based on the driving state information of the candidate drivers corresponding to the time period and the historical traffic accident probability.
In another aspect, an embodiment of the present application provides a vehicle driving regulation device, including:
the traffic accident probability acquisition module is used for acquiring historical traffic accident probability corresponding to a road section where the vehicle is located in any time period;
the driving state information acquisition module is used for acquiring the driving state information of each candidate driver corresponding to the time interval;
and the target driver determining module is used for determining the target driver in the time period from the candidate drivers based on the driving state information and the historical traffic accident probability of the candidate drivers corresponding to the time period.
In another aspect, an embodiment of the present application provides an in-vehicle device, including a processor and a memory: the memory is configured to store a computer program that, when executed by the processor, causes the processor to perform any one of the vehicle driving regulation methods.
In one aspect, an embodiment of the present application provides an electronic device, including a processor and a memory: the memory is configured to store a computer program that, when executed by the processor, causes the processor to perform any one of the vehicle driving regulation methods.
In still another aspect, embodiments of the present application provide a computer-readable storage medium for storing a computer program, which, when run on a computer, makes the computer execute any one of the vehicle driving regulation methods.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
in the embodiment of the application, when determining the driver driving the vehicle, the driver in each time period can be determined according to the historical traffic accident probability corresponding to the road section where the vehicle is located in each time period and the driving state information of each candidate driver corresponding to each time period. That is to say, in the embodiment of the application, the number of drivers required in each time interval can be dynamically and reasonably adjusted through the actual historical traffic accident probability of the road section where the vehicle is located and the driving state information of each candidate driver, and at this time, the drivers do not need to be frequently replaced, so that the driver can be effectively ensured not to be tired and the driving efficiency of the vehicle is also ensured.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
FIG. 1a is a schematic diagram of a million kilometers accident distribution provided by an embodiment of the present application;
fig. 1b is a schematic flowchart of a vehicle driving regulation method according to an embodiment of the present application;
fig. 2 is a schematic view of an application scenario provided in an embodiment of the present application;
fig. 3 is a schematic architecture diagram of a cloud traffic management platform according to an embodiment of the present disclosure;
FIG. 4 is a schematic flow chart illustrating another vehicle driving regulation method according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a vehicle driving regulation device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
In actual driving, most freight drivers finish loading and start driving in the morning, and continuously work for about 8 hours to 7-9 am, and at the moment, the freight drivers are in a tired state, and based on statistical data shown in fig. 1a, the time period of 7-9 am is usually the morning peak of commuting, the road vehicle density is increased rapidly, the number of millions of kilometers of accidents is distributed as high as 4.7, accidents are easy to occur, the number of drivers on one truck is limited, and the driving efficiency of the truck drivers is greatly reduced by frequently changing the truck drivers to reduce the fatigue of the drivers. Therefore, how to balance the driving efficiency and driving fatigue of the truck driver is one of the key problems facing the safe assistant driving.
Based on this, the embodiment of the application provides a vehicle driving regulation method, a vehicle driving regulation device, an electronic device and a readable storage medium, and aims to balance the driving efficiency and the driving fatigue of a truck driver.
In the embodiment of the application, the processing of the related data can be realized by cloud computing, for example, statistical computation can be performed on traffic accident data based on a cloud computing mode to obtain historical traffic accident probability corresponding to a road section where a vehicle is located in each time period, and driving state information of each candidate driver corresponding to each time period is obtained based on an image processing technology in an artificial intelligence technology; correspondingly, when the driving efficiency and the driving fatigue of the truck driver are balanced, for any time period, the historical traffic accident probability corresponding to the road section where the vehicle is located in the time period and the driving state information of each candidate driver corresponding to the time period can be acquired, then the target driver in the time period is determined from the candidate drivers based on the driving state information and the historical traffic accident probability of each candidate driver corresponding to the time period, namely, the number of drivers required in each time period is dynamically and reasonably adjusted through the actual historical traffic accident probability and the driving state information of each candidate driver, so that the driver is not fatigued any more, the drivers are not required to be frequently replaced, and the driving efficiency of the vehicle is ensured.
Wherein, cloud computing (cloud computing) refers to a delivery and use mode of an IT infrastructure, and refers to acquiring required resources in an on-demand and easily-extensible manner through a network; the generalized cloud computing refers to a delivery and use mode of a service, and refers to obtaining a required service in an on-demand and easily-extensible manner through a network. Such services may be IT and software, internet related, or other services. Cloud Computing is a product of development and fusion of traditional computers and Network Technologies, such as Grid Computing (Grid Computing), distributed Computing (distributed Computing), Parallel Computing (Parallel Computing), Utility Computing (Utility Computing), Network Storage (Network Storage Technologies), Virtualization (Virtualization), Load balancing (Load Balance), and the like.
With the development of diversification of internet, real-time data stream and connecting equipment and the promotion of demands of search service, social network, mobile commerce, open collaboration and the like, cloud computing is rapidly developed. Different from the prior parallel distributed computing, the generation of cloud computing can promote the revolutionary change of the whole internet mode and the enterprise management mode in concept.
Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.
The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1b shows a flowchart of a vehicle driving regulation method provided in an embodiment of the present application, which may be executed by a server or a terminal device. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud computing services. The terminal may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a vehicle-mounted terminal device (including a vehicle-mounted computer), and the like, but is not limited thereto. The terminal device and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein. Optionally, when the method provided in the embodiment of the present application is executed by a server, when the server obtains the target driver corresponding to each time period based on the method provided in the embodiment of the present application, the obtained result may be sent to an in-vehicle computer, and the in-vehicle computer displays the result to a user.
As shown in fig. 1b, the method may include:
step S101, for any time interval, obtaining historical traffic accident probability corresponding to the road section where the vehicle is located in the time interval.
The duration of each time interval can be preset according to actual requirements, and the duration of each time interval can be the same or different, for example, 24 hours a day can be equally divided into n time intervals, and the duration of each time interval obtained at the moment is the same and is 24/n; of course, the 24 hours a day may be divided into n periods with different durations, and the duration of each period is not limited in the embodiment of the present application. Alternatively, in order to improve safety, the duration of each period is not set to be too long, since the time of attention of the driver does not usually exceed 4 hours, and therefore the duration of each period may be set to be less than 4 hours.
For each time interval, the historical traffic accident probability corresponding to the road segment where the vehicle is located in the time interval refers to the probability of a traffic accident possibly occurring when the vehicle runs in the road segment where the vehicle is located in the time interval, wherein the historical traffic accident probability can be obtained based on a first traffic accident probability corresponding to the type of the vehicle to which the vehicle belongs in all the road segments and a second traffic accident probability corresponding to the type of the vehicle to which the vehicle belongs in the road segment where the vehicle is located in the time interval, and the first traffic accident probability and the second traffic accident probability can be obtained according to statistical traffic accident data.
Optionally, when the road segment where the vehicle is located in a certain time period includes at least two road segments, the historical traffic accident probability corresponding to the road segment where the vehicle is located in the time period may be obtained according to the historical traffic accident probability corresponding to the time period of each included road segment. For example, the average historical traffic accident probability may be obtained according to the historical traffic accident probability of each road segment corresponding to the time period, and the obtained average historical traffic accident probability is used as the historical traffic accident probability corresponding to the road segment where the vehicle is located in the time period; or the maximum historical traffic accident probability in each road section can be directly used as the historical traffic accident probability corresponding to the road section where the vehicle is located in the time period, and the like; of course, in practical applications, the historical traffic accident probability corresponding to the time period of each included road segment may also be taken as the historical traffic accident probability corresponding to the road segment where the vehicle is located in the time period, and accordingly, when other processing is performed subsequently based on the historical traffic accident probability corresponding to the road segment where the vehicle is located in the time period, the historical traffic accident probability corresponding to the time period of each road segment may be processed respectively.
And step S102, acquiring the driving state information of each candidate driver corresponding to the time interval.
The driving state information of the driver represents the driving capability of the driver for safe driving, and can be represented by the time length that the driver can drive continuously at one time, for example, the driving state information can include the maximum time length, the minimum time length or the average time length that the driver can drive continuously at one time. Alternatively, the driver driving state information may be characterized by an average length of time that the driver may be continuously driving once.
Optionally, at least one (usually multiple) candidate driver may be configured in advance in different time periods, and the candidate driver corresponding to each time period is the respective driver configured in the time period. The manner of obtaining the driving state information of each candidate driver corresponding to any time period may be configured in advance, and the embodiment of the present application is not limited. For example, the driving state information of each candidate driver corresponding to each time period may be stored in the database in advance, and when the driving state information of each candidate driver corresponding to any time period needs to be acquired, the driving state information may be directly acquired from the database; the parameter information for determining the driving state information of each candidate driver corresponding to each segment may also be configured in advance, and when the driving state information of each candidate driver corresponding to any time period needs to be obtained, the parameter information for determining the driving state information of each candidate driver corresponding to the segment may be obtained from the database, and then the driving state information of each candidate driver is calculated based on the obtained parameter information.
And step S103, determining a target driver in the time period from the candidate drivers based on the driving state information and the historical traffic accident probability of the candidate drivers corresponding to the time period.
The target driver refers to a driver determined from the candidate drivers and actually performing vehicle driving, and the target driver in any time period refers to a driver needing to actually perform vehicle driving in the time period.
Optionally, when there are multiple target drivers in a certain period, it is described that multiple drivers are required to drive the vehicle in the period, and how the multiple drivers allocate driving time lengths in the period may be configured in advance according to actual needs, which is not limited in the embodiment of the present application. For example, the driving time period of each target driver may be allocated according to the driving state information of each target driver during the period, such as allocating a target driver with better driving state information to a longer driving time period, allocating a target driver with worse driving state information to a shorter driving time period, and the like.
In the embodiment of the application, when determining the driver driving the vehicle, the driver in each time period can be determined according to the historical traffic accident probability corresponding to the road section where the vehicle is located in each time period and the driving state information of each candidate driver corresponding to each time period. That is to say, in the embodiment of the application, the number of drivers required in each time interval can be dynamically and reasonably adjusted through the actual historical traffic accident probability of the road section where the vehicle is located and the driving state information of each candidate driver, and at this time, the drivers do not need to be frequently replaced, so that the driver can be effectively ensured not to be tired and the driving efficiency of the vehicle is also ensured.
In an optional embodiment of the present application, obtaining a historical traffic accident probability corresponding to a road segment where a vehicle is located in the time period includes:
acquiring first traffic accident probabilities corresponding to all road sections of the types of the vehicles at the time period;
acquiring a second traffic accident probability corresponding to the type of the vehicle in the road section where the vehicle is located in the time period;
and determining historical traffic accident probability corresponding to the road section where the vehicle is located in the time period based on the first traffic accident probability and the second traffic accident probability corresponding to the vehicle type of the vehicle in the road section where the vehicle is located in the time period.
The first traffic accident probability refers to the ratio of the number of traffic accidents occurring in all road sections of the vehicle type of the vehicle in the period to the total number of traffic accidents occurring in all road sections of the vehicle type of the vehicle in the period, and represents the relative frequent degree of the traffic accidents of the vehicle type of the vehicle in different periods; the second traffic accident probability is a ratio of the number of traffic accidents of the vehicle type to which the vehicle belongs occurring in the road section to which the vehicle belongs in the time period to the total number of traffic accidents of the vehicle type to which the vehicle belongs occurring in all the road sections in the time period, and represents a probability that the road section to which the vehicle belongs is involved in the traffic accidents of the vehicle type to which the vehicle belongs.
Optionally, for the first traffic accident probability, the total number of traffic accidents occurring in all road segments by different vehicle types within a set time (for example, every day) and the total number of traffic accidents occurring in all road segments by different vehicle types within each time period may be counted, so as to obtain the first traffic accident probability corresponding to each vehicle type in all road segments within each time period. When the total number of the traffic accidents occurring in all the road sections by different vehicle types within the set time is counted, the total number of the traffic accidents occurring in different vehicle types each day within the set area of the road section where the vehicle is located can be counted, and if the road section where the vehicle is located belongs to city A, the total number of the traffic accidents occurring in different vehicle types each day within the city A area can be only counted; of course, in practical applications, the total number of traffic accidents occurring in different vehicle types every day in the national area may be counted, and the embodiment of the present application is not limited.
In one example, assuming that the time interval 1 and the time interval 2 are included, the vehicle types comprise a truck and a car, and in the time interval 1, the number of traffic accidents of the truck is 50, and the number of traffic accidents of the car is 20; in the time period 2, the number of traffic accidents of the trucks is 30, and the number of traffic accidents of the cars is 10; at this time, the total traffic accident probability of the truck corresponding to the time interval 1 is 50/(50+30) ═ 62.5%, and the total traffic accident probability corresponding to the time interval 2 is 30/(50+30) ═ 37.5%; the total traffic accident probability of the car corresponding to the time period 1 is 20/(10+20) ═ 66.7%, and the total traffic accident probability corresponding to the time period 2 is 10/(10+20) ═ 33.3%.
Optionally, for the first traffic accident probability, the number of traffic accidents occurring in different vehicle types in each time period and the number of traffic accidents occurring in different vehicle types in each road section in each time period may be counted, and then, based on the number of traffic accidents occurring in different vehicle types in each time period and the number of traffic accidents occurring in different vehicle types in each road section in each time period, the second traffic accident probability corresponding to different vehicle types when the vehicle in each time period is in different road sections is obtained. For example, the type of the vehicle is a truck, and for the time slot 1, 20 total traffic accidents occur in the truck, where 5 of the 20 traffic accidents occur in the road segment 1, and 15 of the 20 traffic accidents occur in the road segment 2, at this time, the probability of the second traffic accident corresponding to the truck in the road segment 1 in the time slot 1 is 5/20-25%, and the probability of the second traffic accident corresponding to the truck in the road segment 1 in the time slot 1 is 15/20-75%.
It can be understood that, in the embodiment of the present application, the first traffic accident probability that each vehicle type corresponds to each time interval and the second traffic accident probability that each vehicle type occurs in each road segment in each time interval may be predetermined and stored in a database (for example, a local database of a vehicle-mounted device (such as a vehicle-mounted computer) or a designated cloud database), and when it is required to obtain the first traffic accident probability that a certain vehicle type corresponds to a certain time interval and the second traffic accident probability that a certain vehicle type corresponds to a road segment in which a certain time interval is located, the first traffic accident probability may be directly obtained from the database. The first traffic accident probability of each vehicle type corresponding to each time interval and the second traffic accident probability of each vehicle type occurring at each road segment in each time interval may refer to data corresponding to a day before the current time, or may be average data corresponding to a set time length before the current time, and the like, which is not limited in the embodiment of the present application.
Correspondingly, for any time period, the historical traffic accident probability corresponding to the road section where the vehicle is located in the time period can be obtained based on the obtained first traffic accident probability and the obtained second traffic accident probability. The specific implementation manner of obtaining the historical traffic accident probability corresponding to the road section where the vehicle is located in the time period based on the obtained first traffic accident probability and the second traffic accident probability may be configured in advance, and the embodiment of the application is not limited. For example, the product of the first traffic accident probability and the second traffic accident probability may be directly used as the historical traffic accident probability corresponding to the road segment where the vehicle is located in the time period, or different weights may be given to the first traffic accident probability and the first traffic accident probability, and the historical traffic accident probability corresponding to the road segment where the vehicle is located in the time period may be obtained based on the respective weights.
In an example, assuming that the type of the vehicle is a truck, the vehicle is located on a road section 1 in a time period 1, and the probability of acquiring a first traffic accident corresponding to the truck in the time period 1 is W1And the probability of the second traffic accident corresponding to the freight car in the road section 1 where the vehicle is located in the time period 1 is PhistoryAt this time, the historical traffic accident probability P ═ W corresponding to the road segment 1 where the vehicle is located in the time period 1 can be obtained1*Phistory。
In yet another example, assume that the truck is first on road segment 1 and then on time segment 1At the road section 2, the second traffic accident probability P corresponding to the trucks in the road section 1 in the time interval 1 can be respectively obtained at the moment1 historyAnd a second traffic accident probability P corresponding to the truck in the section 2 in the time interval 12 historyThen according to P1 historyAnd P2 historyAnd calculating an average value, and determining the historical traffic accident probability corresponding to the road section where the vehicle is located in the time period 1 according to the obtained average value and the total traffic accident probability corresponding to the trucks in the time period 1. Of course, P can also be calculated separately1 historyAnd P2 historyAnd when the calculation processing is carried out based on the historical traffic accident probability corresponding to the road section of the vehicle in the time period 1, the corresponding two historical traffic accident probabilities can be adopted for carrying out processing respectively.
In an alternative embodiment of the present application, for any candidate driver, the driving state information of the candidate driver includes an average duration of one continuous driving of the candidate driver;
determining a target driver in the time period from the candidate drivers based on the driving state information and the historical traffic accident probability of the candidate drivers corresponding to the time period, wherein the method comprises the following steps:
determining the safe driving probability of each candidate driver in the time period according to the average time length of one-time continuous driving of each candidate driver and the time length of the time period;
and determining a target driver from the candidate drivers based on the safe driving probability and the historical traffic accident probability of each candidate driver corresponding to the time period in the time period.
Alternatively, the driving state information of the candidate driver may be characterized by an average length of time that the driver may be continuously driving at once. Alternatively, the average duration of one continuous drive for each candidate driver may be determined based on the historical driving conditions of the candidate driver. For example, during the driving of a vehicle by a driver, an in-vehicle camera or other face recognition devices may capture a candidate driver in real time to obtain a video frame image including a face image of the candidate driver, and then extract facial features of the driver based on the video frame image, and further analyze whether the driving state of the driver has changed and the continuous stay time of the driver in a certain state according to the facial features, so as to determine the average duration of one-time continuous driving of the driver.
Optionally, in order to ensure that the driver can drive safely and reduce traffic accidents, in the embodiment of the present application, the safe driving probability of each candidate driver in each time period may be determined, and when determining the target driver in a certain time period, the target driver may be determined from the candidate drivers based on the safe driving probability of each candidate driver in the time period corresponding to the time period and the historical traffic accident probability corresponding to the road segment where the vehicle is located in the time period.
The safe driving probability of each candidate driver in any period can refer to the probability that the average duration of one-time continuous driving of the candidate driver exceeds the duration of the period, and optionally, the probability that the average duration of one-time continuous driving of the candidate driver exceeds the duration of the period is subject to an index
Where at represents the period duration,
represents an average duration of one continuous driving of the candidate driver; accordingly, in determining the safe driving probability of each candidate driver for the period, the average duration of one continuous driving based on the candidate driver and the duration of the period may be passed
Thus obtaining the product. For example, for any candidate driver, the average duration of one continuous driving of the candidate driver is 3h, the duration of the period 1 is 2 hours, and the safe driving probability of the candidate driver in the period 1 can be e-2/3。
In an optional embodiment of the present application, determining a target driver from candidate drivers based on a safe driving probability and a historical traffic accident probability of each candidate driver corresponding to the time period in the time period includes:
determining the probability of non-traffic accidents corresponding to the time period according to the historical traffic accident probability;
and determining the target driver from the candidate drivers based on the non-historical traffic accident probability and the safe driving probability of each candidate driver in the period.
The non-traffic accident probability corresponding to a certain time period refers to the probability that a traffic accident will not occur in a road segment where a vehicle is located in the time period, and may be determined according to the historical traffic accident probability corresponding to the road segment where the vehicle is located in the time period, and the specific determination mode may be preconfigured, which is not limited in the embodiment of the present application. For example, the non-traffic accident probability may be determined based on the following formula.
Pk＝1-Pn
Wherein, PkRepresenting the probability of a non-traffic accident, P, corresponding to a period of timenAnd the historical traffic accident probability corresponding to the road section where the vehicle is located in the period is represented.
Alternatively, when based on formula Pk＝1-PnWhen the non-traffic accident probability is determined, the coefficients of the parameters in the formula can be changed according to actual requirements to obtain different non-traffic accident probabilities, for example, the non-traffic accident probability can be based on the formula Pk＝1-2PnAnd determining the probability of the non-traffic accident.
Optionally, for any time period, if the safe driving probability of the candidate driver in the time period is greater than the non-traffic accident probability corresponding to the time period, it may be stated that the candidate driver is not likely to experience fatigue driving when driving the vehicle in the time period, and safe driving may be effectively ensured.
In an alternative embodiment of the present application, the determining the target driver from the candidate drivers based on the non-historical traffic accident probability and the safe driving probability of each candidate driver in the time period comprises:
and determining at least one target driver from the candidate drivers according to the safe driving probability of each candidate driver in the time period, wherein the sum of the safe driving probability of each target driver in the time period is not less than the non-historical traffic accident probability.
Optionally, for any time period, at least one target driver may be determined from the candidate drivers based on a relationship between the probability of the non-historical traffic accident corresponding to the time period and the safe driving probability of each candidate driver in the time period, where the sum of the safe driving probabilities of the determined at least one target driver in the time period is not less than the probability of the non-historical traffic accident. In the embodiment of the application, because the sum of the safe driving probabilities of at least one target driver in the time period is not less than the non-historical traffic accident probability, the at least one target driver is in attention concentration when driving a vehicle in the time period, and the probability of occurrence of a traffic accident can be effectively reduced.
In an alternative embodiment of the present application, the determining the target driver from the candidate drivers based on the non-historical traffic accident probability and the safe driving probability of each candidate driver in the time period comprises:
and selecting at least one target driver from the candidate drivers according to the sequence that the safe driving probability of each candidate driver in the time interval is from large to small until the sum of the safe driving probabilities of the selected target drivers in the time interval is not less than the non-historical traffic accident probability.
Optionally, for any time period, the safe driving probabilities of each candidate driver corresponding to the time period may be arranged in order from large to small, and then the target drivers are sequentially selected from the candidate drivers according to the order from large to small until the sum of the safe driving probabilities of the selected target drivers in the time period is not less than the non-historical traffic accident probability.
In one example, assume for timeA segment k, the probability of the non-historical traffic accident corresponding to the segment k is PkM candidate drivers are corresponding to the time interval, and the safe driving probability corresponding to the first candidate driver in the time interval is E1The safe driving probability corresponding to the second candidate driver in the time interval is E2… …, the safe driving probability of the mth candidate driver is Em(ii) a Accordingly, when the target driver corresponding to the time interval is determined, the maximum safe driving probability E may be selected from the safe driving probabilities of the candidate drivers in the order of the safe driving probability of the candidate drivers in the time interval from the maximum to the minimumk1If E isk1Not less than PkThen the target driver in the period may be determined to be Ek1A corresponding candidate driver; if Ek1Less than PkSelecting the next highest safe driving probability E from the safe driving probabilities of the candidate drivers according to the sequence that the safe driving probability of the candidate drivers in the time interval is from high to lowk2And determining Ek1+Ek2Whether or not less than PkIf E isk1+Ek2Not less than PkThen E can bek1Corresponding candidate drivers and Ek2The corresponding candidate driver is taken as a target driver corresponding to the time interval; if Ek1+Ek2Less than PkThen, the third highest safe driving probability E is selected from the safe driving probabilities of the candidate drivers according to the safe driving probability of the candidate drivers in the time interval from the highest to the lowestk3And determining Ek1+Ek2+Ek3Whether or not less than PkAnd the like until the sum of the safe driving probabilities of the selected target drivers in the time period is not less than Pk。
In an embodiment of the present application, the method further includes:
and if the target driver corresponding to the adjacent time interval has the repeated driver, adjusting the target driver corresponding to the adjacent time interval according to a preset driver adjustment strategy.
Optionally, if there is a repeated driver in the target driver corresponding to the adjacent time interval, it is indicated that the repeated driver will drive the vehicle in two consecutive time intervals, at this time, the driver is easy to have driving fatigue, and the probability of occurrence of a traffic accident will be increased. Based on this, in the embodiment of the application, when the target driver corresponding to the adjacent time interval has the repeat driver, the target driver corresponding to the adjacent time interval can be adjusted according to the preset driver adjustment strategy, so that the target driver corresponding to the adjacent time interval is ensured not to have the repeat driver, the driving fatigue of the driver is effectively avoided, and the probability of traffic accidents is reduced.
The specific adjustment mode of the driver adjustment strategy can be configured in advance according to the actual situation, and the embodiment of the application is not limited. For example, when there is a repeat driver corresponding to the target driver in the adjacent time period, for any one time period in the adjacent time period, the candidate driver with the highest safe driving probability may be selected from the unselected candidate drivers corresponding to the time period to replace the repeat driver, and it may be understood that the candidate driver replacing the repeat driver needs to be not the target driver corresponding to the adjacent time period in the time period.
Optionally, the method provided by the embodiment of the application can be applied to a plurality of application scenarios, such as the fields of intelligent networked automobiles and intelligent travel. For example, the method provided by the embodiment of the application can be integrated into an application applet and a map application, and when a vehicle needs to be driven for a long time, drivers corresponding to different periods of the vehicle during driving can be determined by the application applet or the map application based on the method, so that the driving risk can be reduced, and the fine operation level of a freight enterprise can be improved. Optionally, when determining drivers corresponding to different time periods based on the method provided in the embodiment of the present application, the determination may be performed in real time during driving of the vehicle, for example, based on determining a target driver required in a next time period in a current time period; it is also possible to predict the road section where the vehicle is likely to be located in each section after the vehicle starts driving, while knowing the route on which the vehicle is driven and the time of starting driving, and then preselect the target driver required to determine each section, and drive the vehicle directly according to the target driver required in each predetermined section after the vehicle starts driving.
In order to better understand the method provided by the embodiment of the present application, the method is described in detail below with reference to the application scenario of truck driving as shown in fig. 2. In this example, as shown in fig. 3, each truck (truck 1 to truck 5) has an onboard computer installed therein, when driving the truck, the onboard computer installed in each truck may obtain parameter data for determining a target driver required for each time period from the traffic management cloud platform, and then determine the target driver required for each time period according to the obtained parameter data. Alternatively, the following describes a method for determining the target driver required for each time interval by taking one of the trucks as an example, and may be specifically as shown in fig. 4.
Step S401, the vehicle-mounted computer obtains the ratio of the million kilometer accidents of the truck to the million kilometer accidents of the truck in the whole day in different time periods in one day (namely the probability of the first traffic accident in the foregoing);
specifically, the vehicle-mounted computer obtains the ratio of the million kilometer accidents of the truck to the million kilometer accidents of the truck in the whole day in different time periods in one day from the traffic management cloud platform. For example, assume that there are n periods, which are referred to as single periods 1,2, n, respectively (i.e., each period has a duration of 24/n), and take a, respectively1,a2,...,anThe method is characterized in that the million kilometer accidents of trucks in each time interval of n time intervals are represented, the proportion of the million kilometer accidents of trucks in each time interval to the million kilometer accidents of trucks in all days is further adopted, and w is adopted1＝a1/(a1+a2+...+an),w2＝a2/(a1+a2+...+an),...,wn＝an/(a1+a2+...+an) And (4) showing.
Step S402, the vehicle-mounted computer determines the driving state characteristic distribution of each candidate driver;
the vehicle-mounted computer is pre-configured with which drivers of the truck can be used in each time interval 1,2For any time interval, m candidate drivers exist in the time interval, the m candidate drivers are respectively called as drivers 1, 2.. and m, and then the average duration time of the candidate drivers staying in the driving state at one time is obtained from the traffic management cloud platform
And based on the exponential distribution and the time length delta T of the time interval, determining the probability that the duration of one-time stay in the driving state of each candidate driver 1,2
Step S403, the vehicle-mounted computer determines the traffic accident rate (i.e. the historical traffic accident probability) of the truck traffic accidents occurring at each road segment in different time periods of the day:
specifically, in step S401, a ratio of the million kilometer accidents of the truck in different time periods of a day to the million kilometer accidents of the truck in the whole day is determined, and the ratio can describe a relative frequency degree of traffic accidents of the truck in different time periods of a day. Therefore, the vehicle-mounted computer acquires the historical traffic accident rate (namely the second traffic accident probability in the foregoing) of the current road section where the truck is involved from the traffic management department and records the historical traffic accident rate as phistoryThen, based on the ratio of the million kilometer accidents of the truck in each time interval to the million kilometer accidents of the truck in the whole day determined in the step S401, the obtained traffic accident rates of the truck traffic accidents in each road segment in different time intervals in one day are respectively marked as p1＝phistoryw1,p2＝phistoryw2,...,pn＝phistorywn；
Step S404, the vehicle-mounted computer determines how many truck drivers are needed in different time periods.
Specifically, the vehicle-mounted computer knows the corresponding candidate drivers in each time interval in advance. At the moment, each corresponding candidate driver stays in the driving state for one time in each time intervalThe set of probabilities of duration of (d) exceeding Δ T is S1,S2,...,Sn，S1,S2,...,SnCan be respectively expressed as
Correspondingly, for period k, from the probability set SkThe maximum probability is selected and recorded as Ek1Judgment Ek1≥1-pnIf it does, then during time period k only the use of the reference E is requiredk1The other drivers can have a rest; if Ek1＜1-pn) Then, the second highest probability is recorded as Ek2Judgment Ek1+Ek2≥1-pnIf it does, then during time period k only the use of the reference E is requiredk1And Ek2The other drivers can rest, and so on, and all drivers required to be used in each time period can be obtained at the time.
Optionally, in the embodiment of the present application, based on a scheme in the prior art (for example, a scheme in which a truck driver is frequently replaced) and a scheme provided in the embodiment of the present application, ten comparison experiments are performed on the fatigue driving time of the truck driver passing through the same traffic intersection, and specific implementation results are shown in table 1:
TABLE 1 results of the experiment
| Order of experiment | Ratio of time period of driver fatigue driving in the prior art and the present application |
| First experiment | 1.74 |
| Second experiment | 1.72 |
| Third experiment | 1.75 |
| Fourth experiment | 1.74 |
| Fifth experiment | 1.76 |
| The sixth experiment | 1.72 |
| The seventh experiment | 1.71 |
| The eighth experiment | 1.76 |
| The ninth experiment | 1.73 |
| The tenth experiment | 1.76 |
Wherein, the first column (i.e. the experimental sequence) in table 1 represents the experiment of the second time, the second column (i.e. the ratio of the time length of fatigue driving of the driver in the prior art and the present application) represents the experimental result of each experiment, i.e. the ratio of the time length of fatigue driving of the truck driver passing through the same traffic intersection based on the scheme of the prior art and the scheme provided in the present application, and the second column and the second row represent the ratio of the time length of fatigue driving of the truck driver based on the prior art scheme and the time length of fatigue driving of the truck driver based on the scheme provided in the present application is 1.74 when the first experiment is performed. Accordingly, it can be seen from the ten experimental results shown in table 1 that the ratio of the fatigue driving time of the truck driver based on the prior art scheme to the fatigue driving time of the truck driver based on the scheme provided in the embodiment of the present application is greater than 1, that is, the fatigue driving time of the truck driver based on the prior art scheme is greater than the fatigue driving time of the truck driver based on the scheme provided in the embodiment of the present application. Therefore, the performance of the scheme provided in the embodiment of the application is superior to that of the prior art, and compared with the scheme in the prior art, the fatigue driving state of a driver can be relieved more effectively.
The embodiment of the present application provides a vehicle driving control device, as shown in fig. 5, the vehicle driving control device 60 may include: a traffic accident probability acquisition module 601, a driving state information acquisition module 602, and a target driver determination module 603, wherein,
a traffic accident probability obtaining module 601, configured to obtain, for any time period, a historical traffic accident probability corresponding to a road segment where a vehicle is located in the time period;
a driving state information obtaining module 602, configured to obtain driving state information of each candidate driver corresponding to the time period;
and a target driver determining module 603, configured to determine a target driver in the time period from the candidate drivers based on the driving state information and the historical traffic accident probability of the candidate drivers corresponding to the time period.
Optionally, when the traffic accident probability obtaining module obtains the historical traffic accident probability corresponding to the road segment where the vehicle is located at the time period, the traffic accident probability obtaining module is specifically configured to:
acquiring first traffic accident probabilities corresponding to all road sections of the types of the vehicles at the time period;
acquiring a second traffic accident probability corresponding to the type of the vehicle in the road section where the vehicle is located in the time period;
and determining historical traffic accident probability corresponding to the road section where the vehicle is located in the time period based on the first traffic accident probability and the second traffic accident probability corresponding to the vehicle type of the vehicle in the road section where the vehicle is located in the time period.
Optionally, for any candidate driver, the driving state information of the candidate driver includes an average duration of one continuous driving of the candidate driver;
the target driver determination module is specifically configured to, when determining the target driver in the time period from the candidate drivers based on the driving state information and the historical traffic accident probability of the candidate drivers corresponding to the time period, determine the target driver in the time period from among the candidate drivers:
determining the safe driving probability of each candidate driver in the time period according to the average time length of one-time continuous driving of each candidate driver and the time length of the time period;
and determining a target driver from the candidate drivers based on the safe driving probability and the historical traffic accident probability of each candidate driver corresponding to the time period in the time period.
Optionally, when the target driver determining module determines the target driver from the candidate drivers based on the safe driving probability and the historical traffic accident probability of each candidate driver corresponding to the time period in the time period, the target driver determining module is specifically configured to:
determining the probability of non-traffic accidents corresponding to the time period according to the historical traffic accident probability;
and determining the target driver from the candidate drivers based on the non-historical traffic accident probability and the safe driving probability of each candidate driver in the period.
Optionally, when the target driver determination module determines the target driver from the candidate drivers based on the non-historical traffic accident probability and the safe driving probability of each candidate driver in the time period, the target driver determination module is specifically configured to:
and determining at least one target driver from the candidate drivers according to the safe driving probability of each candidate driver in the time period, wherein the sum of the safe driving probability of each target driver in the time period is not less than the non-historical traffic accident probability.
Optionally, when the target driver determination module determines the target driver from the candidate drivers based on the non-historical traffic accident probability and the safe driving probability of each candidate driver in the time period, the target driver determination module is specifically configured to:
and selecting at least one target driver from the candidate drivers according to the sequence that the safe driving probability of each candidate driver in the time interval is from large to small until the sum of the safe driving probabilities of the selected target drivers in the time interval is not less than the non-historical traffic accident probability.
Optionally, the apparatus further includes an adjusting module, configured to:
and when repeated drivers exist in the target drivers corresponding to the adjacent time intervals, adjusting the target drivers corresponding to the adjacent time intervals according to a preset driver adjustment strategy.
The vehicle driving regulation and control device of the embodiment of the application can execute the vehicle driving regulation and control method provided by the embodiment of the application, the implementation principles are similar, and the detailed description is omitted here.
In the embodiment of the application, when determining the driver driving the vehicle, the driver in each time period can be determined according to the historical traffic accident probability corresponding to the road section where the vehicle is located in each time period and the driving state information of each candidate driver corresponding to each time period. That is to say, in the embodiment of the application, the number of drivers required in each time interval can be dynamically and reasonably adjusted through the actual historical traffic accident probability of the road section where the vehicle is located and the driving state information of each candidate driver, and at this time, the drivers do not need to be frequently replaced, so that the driver can be effectively ensured not to be tired and the driving efficiency of the vehicle is also ensured.
An embodiment of the present application provides an electronic device, as shown in fig. 6, an electronic device 2000 shown in fig. 6 includes: a processor 2001 and a memory 2003. Wherein the processor 2001 is coupled to a memory 2003, such as via a bus 2002. Optionally, the electronic device 2000 may also include a transceiver 2004. It should be noted that the transceiver 2004 is not limited to one in practical applications, and the structure of the electronic device 2000 is not limited to the embodiment of the present application.
The processor 2001 is applied in the embodiment of the present application to implement the functions of the modules shown in fig. 5.
The processor 2001 may be a CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 2001 may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs and microprocessors, and the like.
The memory 2003 may be, but is not limited to, ROM or other types of static storage devices that can store static information and computer programs, RAM or other types of dynamic storage devices that can store information and computer programs, EEPROM, CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store a desired computer program or in the form of a data structure and that can be accessed by a computer.
The memory 2003 is used for storing computer programs for executing the application programs of the present scheme and is controlled in execution by the processor 2001. The processor 2001 is used to execute a computer program of an application program stored in the memory 2003 to realize the actions of the vehicle driving regulation apparatus provided in the embodiment shown in fig. 5.
An embodiment of the present application provides an electronic device, including a processor and a memory: the memory is configured to store a computer program which, when executed by the processor, causes the processor to perform any of the methods of the above embodiments.
The present application provides a computer-readable storage medium for storing a computer program, which, when run on a computer, enables the computer to execute any one of the above-mentioned methods.
The embodiment of the application provides a vehicle-mounted device, and the vehicle-mounted device comprises: comprising a processor and a memory: the memory is configured to store a computer program which, when executed by the processor, causes the processor to perform any of the methods of the above embodiments.
According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the vehicle driving regulation method provided in the above-mentioned various optional implementation modes.
The terms and implementation principles related to a computer-readable storage medium in the present application may specifically refer to a vehicle driving regulation method in the embodiments of the present application, and are not described herein again.
In the embodiment of the application, when determining the driver driving the vehicle, the driver in each time period can be determined according to the historical traffic accident probability corresponding to the road section where the vehicle is located in each time period and the driving state information of each candidate driver corresponding to each time period. That is to say, in the embodiment of the application, the number of drivers required in each time interval can be dynamically and reasonably adjusted through the actual historical traffic accident probability of the road section where the vehicle is located and the driving state information of each candidate driver, and at this time, the drivers do not need to be frequently replaced, so that the driver can be effectively ensured not to be tired and the driving efficiency of the vehicle is also ensured.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present application, and it should be noted that, for those skilled in the art, several modifications and decorations can be made without departing from the principle of the present application, and these modifications and decorations should also be regarded as the protection scope of the present application.
Claims (10)
1. A vehicle driving regulation method characterized by comprising:
for any time period, acquiring historical traffic accident probability corresponding to the road section where the vehicle is located in the time period;
acquiring driving state information of each candidate driver corresponding to the time interval;
and determining the target driver in the time period from the candidate drivers based on the driving state information of the candidate drivers corresponding to the time period and the historical traffic accident probability.
2. The method according to claim 1, wherein the obtaining of the historical traffic accident probability corresponding to the road segment where the vehicle is located in the time period comprises:
acquiring first traffic accident probabilities corresponding to all road sections of the types of the vehicles in the time period;
acquiring a second traffic accident probability corresponding to the type of the vehicle in the road section where the vehicle is located in the time period;
and determining the historical traffic accident probability corresponding to the road section where the vehicle is located in the time period based on the first traffic accident probability and the second traffic accident probability corresponding to the vehicle type of the vehicle in the road section where the vehicle is located in the time period.
3. The method according to claim 1, wherein for any candidate driver, the driving state information of the candidate driver comprises an average length of time the candidate driver has been driving continuously once;
the determining the target driver in the time period from the candidate drivers based on the driving state information of the candidate drivers corresponding to the time period and the historical traffic accident probability includes:
determining the safe driving probability of each candidate driver in the time period according to the average time length of one-time continuous driving of each candidate driver and the time length of the time period;
and determining a target driver from the candidate drivers based on the safe driving probability and the historical traffic accident probability of each candidate driver corresponding to the time period in the time period.
4. The method according to claim 3, wherein the determining a target driver from the candidate drivers based on the safe driving probability and the historical traffic accident probability of each candidate driver corresponding to the time period in the time period comprises:
determining the non-traffic accident probability corresponding to the time period according to the historical traffic accident probability;
and determining a target driver from the candidate drivers based on the non-historical traffic accident probability and the safe driving probability of each candidate driver in the period.
5. The method of claim 4, wherein determining a target driver from the candidate drivers based on the non-historical probability of the traffic accident and a safe driving probability for each candidate driver over the time period comprises:
and determining at least one target driver from the candidate drivers according to the safe driving probability of each candidate driver in the time period, wherein the sum of the safe driving probability of the at least one target driver in the time period is not less than the non-historical traffic accident probability.
6. The method of claim 5, wherein determining a target driver from the candidate drivers based on the non-historical probability of the traffic accident and a safe driving probability for each candidate driver over the time period comprises:
and selecting at least one target driver from the candidate drivers according to the sequence that the safe driving probability of each candidate driver in the time interval is from large to small until the sum of the safe driving probabilities of the selected target drivers in the time interval is not less than the non-historical traffic accident probability.
7. The method of claim 1, wherein for any of said time periods, said method further comprises:
and if the target driver corresponding to the adjacent time interval has the repeated driver, adjusting the target driver corresponding to the adjacent time interval according to a preset driver adjustment strategy.
8. A vehicle driving regulation device characterized by comprising:
the traffic accident probability acquisition module is used for acquiring historical traffic accident probability corresponding to a road section where the vehicle is located in any time period;
the driving state information acquisition module is used for acquiring the driving state information of each candidate driver corresponding to the time interval;
and the target driver determining module is used for determining the target driver in the time period from the candidate drivers based on the driving state information of the candidate drivers corresponding to the time period and the historical traffic accident probability.
9. An in-vehicle apparatus, comprising a processor and a memory:
the memory is configured to store a computer program which, when executed by the processor, causes the processor to perform the method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium is used for storing a computer program which, when run on a computer, makes the computer perform the method of any of the preceding claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990758A (en) * | 2021-04-14 | 2021-06-18 | 北京三快在线科技有限公司 | Method and device for remotely controlling unmanned equipment |
| CN113903105A (en) * | 2021-09-30 | 2022-01-07 | 杭州海康汽车软件有限公司 | Video circulating storage method and device, electronic equipment and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990758A (en) * | 2021-04-14 | 2021-06-18 | 北京三快在线科技有限公司 | Method and device for remotely controlling unmanned equipment |
| CN112990758B (en) * | 2021-04-14 | 2021-09-07 | 北京三快在线科技有限公司 | Method and device for remotely controlling unmanned equipment |
| CN113903105A (en) * | 2021-09-30 | 2022-01-07 | 杭州海康汽车软件有限公司 | Video circulating storage method and device, electronic equipment and storage medium |
| CN113903105B (en) * | 2021-09-30 | 2023-11-03 | 杭州海康汽车软件有限公司 | Video cyclic storage method and device, electronic equipment and storage medium |
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