Vehicle driving control method, device, vehicle-mounted equipment and readable storage medium

Abstract

The embodiments of the present application provide a vehicle driving control method, apparatus, on-board device, and readable storage medium. The method relates to the fields of map and artificial intelligence technology. The method may include: for any time period, obtaining the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period; obtaining the driving status information of each candidate driver corresponding to the time period; and determining the target driver for the time period from each candidate driver based on the driving status information and the historical traffic accident probability of each candidate driver corresponding to the time period. In the embodiments of the present application, the number of drivers required for each time period can be dynamically and reasonably adjusted based on the actual historical traffic accident probability of the road section where the vehicle is located and the driving status information of each candidate driver. In this case, there is no need to frequently change drivers, which effectively ensures that the driver no longer drives fatigued and the driving efficiency of the vehicle is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of maps and artificial intelligence technology. Specifically, the present application relates to a vehicle driving control method, device, vehicle-mounted equipment and readable storage medium. Background Art

[0002] In practice, most truck drivers typically finish loading their trucks in the early morning hours and begin driving for long periods of time. This can easily lead to fatigue, and when the road is overcrowded, accidents are more likely to occur. Currently, to prevent accidents caused by a single driver driving for extended periods, a common approach is to frequently rotate truck drivers to reduce driver fatigue. However, the number of drivers on a truck is limited, and frequent driver changes inevitably reduce driver efficiency. Summary of the Invention

[0003] The present application provides a vehicle driving control method, device, vehicle-mounted equipment and readable storage medium, which can effectively ensure that the driver no longer drives fatigued and ensure the driving efficiency of the vehicle.

[0004] In one aspect, an embodiment of the present application provides a vehicle driving control method, the method comprising:

[0005] For any period of time, obtain the historical traffic accident probability corresponding to the road section where the vehicle is located during that period;

[0006] Obtaining driving status information of each candidate driver corresponding to the time period;

[0007] Based on the driving status information and historical traffic accident probability of each candidate driver corresponding to the time period, a target driver for the time period is determined from the candidate drivers.

[0008] On the other hand, an embodiment of the present application provides a vehicle driving control device, which includes:

[0009] The traffic accident probability acquisition module is used to obtain the historical traffic accident probability corresponding to the road section where the vehicle is located in any time period;

[0010] A driving status information acquisition module is used to obtain the driving status information of each candidate driver corresponding to the time period;

[0011] The target driver determination module is used to determine the target driver for the time period from the candidate drivers based on the driving status information and historical traffic accident probability of each candidate driver corresponding to the time period.

[0012] On the other hand, an embodiment of the present application provides a vehicle-mounted device, including a processor and a memory: the memory is configured to store a computer program, and when the computer program is executed by the processor, the processor executes any one of the vehicle driving control methods.

[0013] In one aspect, an embodiment of the present application provides an electronic device comprising a processor and a memory: the memory is configured to store a computer program, which, when executed by the processor, enables the processor to perform any one of the vehicle driving control methods.

[0014] On the other hand, an embodiment of the present application provides a computer-readable storage medium, which is used to store a computer program. When the computer program runs on a computer, the computer can execute any one of the vehicle driving control methods.

[0015] The beneficial effects of the technical solution provided by the embodiments of the present application are:

[0016] In this embodiment of the present application, when determining the driver for a vehicle, the driver for each time period can be determined based on the historical traffic accident probability corresponding to the road section the vehicle is on during each time period, as well as the driving status information of each candidate driver corresponding to each time period. In other words, in this embodiment of the present application, the number of drivers required for each time period can be dynamically and reasonably adjusted based on the actual historical traffic accident probability of the road section the vehicle is on and the driving status information of each candidate driver. This eliminates the need for frequent driver changes, effectively preventing driver fatigue while maintaining vehicle driving efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments of the present application.

[0018] Figure 1a A schematic diagram of the distribution of accidents per million kilometers provided in an embodiment of the present application;

[0019] Figure 1b A flow chart of a vehicle driving control method provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of an application scenario provided in an embodiment of the present application;

[0021] Figure 3 A schematic diagram of the architecture of a cloud traffic management platform provided in an embodiment of the present application;

[0022] Figure 4 A flow chart of another vehicle driving control method provided in an embodiment of the present application;

[0023] Figure 5 A schematic structural diagram of a vehicle driving control device provided in an embodiment of the present application;

[0024] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limiting the present application.

[0026] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0027] In actual driving, most freight drivers usually finish loading and start driving in the early morning. By 7-9 am, they have worked continuously for about 8 hours and will be tired at this time. Figure 1a As shown in the statistics, the morning rush hour between 7 and 9 a.m. is typically characterized by a surge in vehicle density and a high accident rate of 4.7 accidents per million kilometers, making accidents highly likely. However, a truck has a limited number of drivers, and frequently changing drivers to reduce driver fatigue significantly reduces driving efficiency. Therefore, balancing driver efficiency and fatigue is a key issue facing safe assisted driving.

[0028] Based on this, the embodiments of the present application provide a vehicle driving control method, device, electronic device and readable storage medium, which aim to balance the driving efficiency and driving fatigue of truck drivers.

[0029] Among them, in the embodiment of the present application, the processing of the data involved can be achieved by cloud computing. For example, the traffic accident data can be statistically calculated based on cloud computing to obtain the historical traffic accident probability corresponding to the road section where the vehicle is located in each time period, and the driving status information of each candidate driver corresponding to each time period can be obtained based on the image processing technology in artificial intelligence technology; accordingly, when balancing the driving efficiency and driving fatigue of the truck driver, 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 status information of each candidate driver corresponding to the time period can be obtained, and then based on the driving status information and historical traffic accident probability of each candidate driver corresponding to the time period, the target driver for the time period is determined from the candidate drivers, that is, the number of drivers required for each time period is dynamically and reasonably adjusted through the actual historical traffic accident probability and the driving status information of each candidate driver, that is, it can be ensured that the driver no longer drives fatigued, and there is no need to frequently change drivers, thereby ensuring the driving efficiency of the vehicle.

[0030] Cloud computing refers to the delivery and usage model of IT infrastructure, enabling on-demand, scalable access to required resources over the internet. In a broader sense, cloud computing refers to the delivery and usage model of services, enabling on-demand, scalable access to required services over the internet. These services can be IT-related, software-related, internet-related, or other services. Cloud computing is the product of the convergence of traditional computer and network technologies, including grid computing, distributed computing, parallel computing, utility computing, network storage technologies, virtualization, and load balancing.

[0031] Cloud computing has rapidly grown, driven by the internet, real-time data streams, the diversification of connected devices, and the growing demand for search services, social networks, mobile commerce, and open collaboration. Unlike previous parallel and distributed computing approaches, the emergence of cloud computing will fundamentally revolutionize the entire internet and enterprise management model.

[0032] Artificial Intelligence (AI) is the theory, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive technology within computer science that seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. AI also studies the design principles and implementation methods of various intelligent machines, enabling them to possess the capabilities of perception, reasoning, and decision-making.

[0033] Artificial intelligence (AI) technology is a comprehensive discipline encompassing a wide range of fields, encompassing both hardware and software technologies. Foundational AI technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0034] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0035] Figure 1b A flow chart of a vehicle driving control method provided in an embodiment of the present application is shown, and the method can be executed by a server or a terminal device. Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a vehicle-mounted terminal device (including a vehicle-mounted computer), etc., but is not limited to this. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and this application does not limit this. Optionally, when the method provided in the embodiment of the present application is executed by the 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 server can send the obtained result to the vehicle-mounted computer, and the vehicle-mounted computer will display it to the user.

[0036] like Figure 1b As shown, the method may include:

[0037] Step S101: For any time period, obtain the historical traffic accident probability corresponding to the road section where the vehicle is located in the time period.

[0038] The duration of each time period can be pre-configured based on actual needs. The duration of each time period can be the same or different. For example, if a 24-hour day is evenly divided into n time periods, the duration of each time period is the same, that is, 24 / n. Of course, a 24-hour day can also be divided into n time periods with different durations. The embodiment of the present application does not limit the duration of each time period. Optionally, in order to improve safety, the duration of each time period should not be set too long. Since the driver's attention span usually does not exceed 4 hours, the duration of each time period can be set to less than 4 hours.

[0039] For each time period, the historical traffic accident probability corresponding to the road section where the vehicle is located in that time period refers to the probability that a traffic accident may occur to the vehicle while it is traveling in the road section within that time period, wherein the historical traffic accident probability can be obtained based on the first traffic accident probability corresponding to the vehicle type to which the vehicle belongs in all road sections and the second traffic accident probability corresponding to the vehicle type to which the vehicle belongs in the road section where the vehicle is located in that time period, and the first traffic accident probability and the second traffic accident probability can be obtained based on statistical traffic accident data.

[0040] Optionally, when the road section where the vehicle is located during a certain time period includes at least two road sections, the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period can be obtained based on the historical traffic accident probability of each road section corresponding to the time period. For example, the average historical traffic accident probability can be obtained based on the historical traffic accident probability of each road section corresponding to the time period, and the obtained average historical traffic accident probability can be used as the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period; or the maximum historical traffic accident probability among the included road sections can be directly used as the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period, etc. Of course, in actual applications, the historical traffic accident probability of each included road section corresponding to the time period can also be used as the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period. Accordingly, when other processing is subsequently performed based on the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period, the historical traffic accident probability of each road section corresponding to the time period can be processed separately.

[0041] Step S102: Acquire the driving status information of each candidate driver corresponding to the time period.

[0042] The driver's driving status information represents the driver's ability to safely drive, and may be represented by the duration of time the driver can continuously drive at one time. For example, the driving status information may include the maximum duration, minimum duration, or average duration of time the driver can continuously drive at one time. Alternatively, the driver's driving status information may be represented by the average duration of time the driver can continuously drive at one time.

[0043] Optionally, different time periods can be pre-configured with at least one (usually multiple) candidate driver, and the candidate drivers corresponding to each time period are the drivers configured for that time period. Among them, the method of obtaining the driving status information of each candidate driver corresponding to any time period can be pre-configured, and the embodiments of the present application are not limited. For example, the driving status information of each candidate driver corresponding to each time period can be pre-stored in a database. When it is necessary to obtain the driving status information of each candidate driver corresponding to any time period, it can be directly obtained from the database; parameter information for determining the driving status information of each candidate driver corresponding to each segment can also be pre-configured. When it is necessary to obtain the driving status information of each candidate driver corresponding to any time period, the parameter information for determining the driving status information of each candidate driver corresponding to the segment can be obtained from the database, and then the driving status information of each candidate driver can be calculated based on the obtained parameter information.

[0044] Step S103 : determining a target driver for the time period from among the candidate drivers based on the driving status information and historical traffic accident probabilities of the candidate drivers corresponding to the time period.

[0045] The target driver refers to the driver who is determined from among the candidate drivers to actually drive the vehicle, and the target driver in any period refers to the driver who needs to actually drive the vehicle during the period.

[0046] Optionally, when there are multiple target drivers for a particular time period, this indicates that multiple drivers are required to drive the vehicle during that time period. The allocation of driving time among these multiple drivers within that time period can be pre-configured based on actual needs and is not limited by the embodiments of the present application. For example, within that time period, the driving time of each target driver can be allocated based on their driving status information. For example, a target driver with better driving status information can be allocated a longer driving time, while a target driver with poorer driving status information can be allocated a shorter driving time.

[0047] In this embodiment of the present application, when determining the driver for a vehicle, the driver for each time period can be determined based on the historical traffic accident probability corresponding to the road section the vehicle is on during each time period, as well as the driving status information of each candidate driver corresponding to each time period. In other words, in this embodiment of the present application, the number of drivers required for each time period can be dynamically and reasonably adjusted based on the actual historical traffic accident probability of the road section the vehicle is on and the driving status information of each candidate driver. This eliminates the need for frequent driver changes, effectively preventing driver fatigue while maintaining vehicle driving efficiency.

[0048] In an optional embodiment of the present application, obtaining the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period includes:

[0049] Obtain the first traffic accident probability corresponding to the vehicle type of the vehicle in the time period on all road sections;

[0050] Obtaining a second traffic accident probability corresponding to the vehicle type in the road section where the vehicle is located during the time period;

[0051] Based on the first traffic accident probability and the second traffic accident probability corresponding to the vehicle type in the road section where the vehicle is located during the period, the historical traffic accident probability corresponding to the road section where the vehicle is located during the period is determined.

[0052] Among them, the first traffic accident probability mentioned above refers to the proportion of the number of traffic accidents of the vehicle type to which the vehicle belongs in all road sections within the time period to the total number of traffic accidents of the vehicle type to which the vehicle belongs in all road sections within the time period, which characterizes the relative frequency of traffic accidents of the vehicle type to which the vehicle belongs in different time periods; the second traffic accident probability refers to the proportion of the number of traffic accidents of the vehicle type to which the vehicle belongs in the road section where the vehicle is located within the time period to the total number of traffic accidents of the vehicle type to which the vehicle belongs in all road sections within the time period, which characterizes the probability that the vehicle is involved in a traffic accident of the vehicle type to which the vehicle belongs in the road section where the vehicle is located during the time period.

[0053] Optionally, for the first traffic accident probability, the total number of traffic accidents occurring in all road sections by different vehicle types within a set time (e.g., every day) and the total number of traffic accidents occurring in all road sections by different vehicle types within each time period can be counted to obtain the first traffic accident probability corresponding to each vehicle type in all road sections in each time period. Specifically, when counting the total number of traffic accidents occurring in all road sections by different vehicle types within a set time period, the total number of traffic accidents occurring in a set area by different vehicle types per day by the road section where the vehicle is located can be counted. For example, if the road section where the vehicle is located belongs to city A, only the total number of traffic accidents occurring in the area of ​​city A by different vehicle types per day can be counted. Of course, in actual applications, the total number of traffic accidents occurring in a national area by different vehicle types per day can also be counted, and this is not limited in the embodiments of the present application.

[0054] In one example, assuming that there are time periods 1 and 2, and the vehicle types include trucks and cars, in time period 1, the number of traffic accidents involving trucks is 50, and the number of traffic accidents involving cars is 20; in time period 2, the number of traffic accidents involving trucks is 30, and the number of traffic accidents involving cars is 10; at this time, it can be obtained that the total traffic accident probability of trucks corresponding to time period 1 is 50 / (50+30)=62.5%, and the total traffic accident probability corresponding to time period 2 is 30 / (50+30)=37.5%; the total traffic accident probability of cars corresponding to time period 1 is 20 / (10+20)=66.7%, and the total traffic accident probability corresponding to time period 2 is 10 / (10+20)=33.3%.

[0055] Optionally, the first traffic accident probability can be calculated by counting the number of traffic accidents occurring by different vehicle types in each time period, as well as the number of traffic accidents occurring by different vehicle types in each road section in each time period. Then, based on the number of traffic accidents occurring by different vehicle types in each time period and the number of traffic accidents occurring by different vehicle types in each road section in each time period, a second traffic accident probability corresponding to different vehicle types when the vehicle is in different road sections in each time period can be obtained. For example, the vehicle type is a truck. For time period 1, a total of 20 traffic accidents involving trucks occurred, of which 5 occurred in road section 1 and 15 occurred in road section 2. In this case, the second traffic accident probability corresponding to trucks in road section 1 in time period 1 is 5 / 20 = 25%, and the second traffic accident probability corresponding to trucks in road section 1 in time period 1 is 15 / 20 = 75%.

[0056] It is understandable that in the embodiment of the present application, the first traffic accident probability corresponding to each time period for each vehicle type, and the second traffic accident probability of each vehicle type occurring on each road section in each time period can be predetermined and stored in a database (such as a local database of an on-board device (such as an on-board computer) or a designated cloud database). When it is necessary to obtain the first traffic accident probability corresponding to a certain vehicle type in a certain time period, and the second traffic accident probability corresponding to the road section where a certain vehicle type is located in a certain time period, it can be directly obtained from the database. Among them, the first traffic accident probability corresponding to each vehicle type in each time period, and the second traffic accident probability of each vehicle type occurring on each road section in each time period can refer to the data corresponding to the day before the current time, or the average data corresponding to the set time period before the current time, etc. The embodiment of the present application does not limit this.

[0057] Accordingly, for any time period, the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period can be obtained based on the first traffic accident probability and the second traffic accident probability. The specific implementation method of obtaining the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period based on the obtained first traffic accident probability and the second traffic accident probability can be pre-configured and is not limited in the embodiments of the present application. For example, the product of the first traffic accident probability and the second traffic accident probability can be directly used as the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period, or the first traffic accident probability and the second traffic accident probability can be assigned different weights, and the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period can be obtained based on their respective corresponding weights.

[0058] In one example, assuming that the vehicle type is a truck, the vehicle is in section 1 during time period 1, and the first traffic accident probability corresponding to the truck in section 1 during time period 1 is W1, and the second traffic accident probability corresponding to the truck in section 1 during time period 1 is P. history At this time, the historical traffic accident probability P = W1*P corresponding to the road section 1 where the vehicle is located in time period 1 can be obtained history .

[0059] In another example, assuming that a truck is first in section 1 and then in section 2 in period 1, the second traffic accident probability P corresponding to the truck in section 1 in period 1 can be obtained respectively. 1 history , and the second traffic accident probability P corresponding to the truck in section 2 during time period 1 2 history , then according to P 1 history and P 2 history Calculate the average value, and determine the historical traffic accident probability corresponding to the section of road where the vehicle is located in period 1 based on the average value and the total traffic accident probability corresponding to the truck in period 1. Of course, you can also calculate P separately 1 history and P 2 history Regarding the total traffic accident probability corresponding to the truck in time period 1, it can be determined that the historical traffic accident probabilities corresponding to the road section where the vehicle is located in time period 1 are two. Subsequently, when performing calculations based on the historical traffic accident probabilities corresponding to the road section where the vehicle is located in time period 1, the corresponding two historical traffic accident probabilities can be used for processing separately.

[0060] In an optional embodiment of the present application, for any candidate driver, the driving status information of the candidate driver includes an average duration of continuous driving of the candidate driver;

[0061] Based on the driving status information and historical traffic accident probability of each candidate driver corresponding to the time period, a target driver for the time period is determined from the candidate drivers, including:

[0062] Determine the safe driving probability of each candidate driver in the period based on the average duration of each candidate driver's continuous driving and the duration of the period;

[0063] A target driver is determined from among the candidate drivers based on the safe driving probability and historical traffic accident probability of each candidate driver corresponding to the time period.

[0064] Optionally, the driving status information of a candidate driver can be characterized by the average duration of continuous driving by the driver at one time. Optionally, the average duration of continuous driving for each candidate driver can be determined based on the candidate driver's historical driving experience. For example, while the driver is driving the vehicle, an in-vehicle camera or other facial recognition device can capture the candidate driver in real time, obtaining a video frame image including the candidate driver's facial image. The driver's facial features can then be extracted based on the video frame image. Based on the facial features, the driver's driving status can be analyzed to determine whether it has changed and the duration of the driver's stay in a certain state, thereby determining the average duration of the driver's continuous driving at one time.

[0065] Optionally, in order to ensure that drivers can drive safely and reduce traffic accidents, in an embodiment of the present application, the safe driving probability of each candidate driver in each time period can be determined. When determining the target driver within a certain time period, the target driver can 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 section where the vehicle is located in the time period.

[0066] The safe driving probability of each candidate driver in any period of time may refer to the probability that the average duration of the candidate driver's continuous driving exceeds the duration of the period. Optionally, the probability that the average duration of the candidate driver's continuous driving exceeds the duration of the period of time is subject to the index The distribution of , where ΔT represents the time period, represents the average duration of a candidate driver's continuous driving; accordingly, when determining the safe driving probability of each candidate driver in the period, the average duration of a candidate driver's continuous driving and the duration of the period can be used to For example, for any candidate driver, the average duration of continuous driving of the candidate driver is 3 hours, and the duration of period 1 is 2 hours. At this time, the safe driving probability of the candidate driver in period 1 can be e -2 / 3 .

[0067] In an optional embodiment of the present application, determining a target driver from among the candidate drivers based on the safe driving probability and historical traffic accident probability of each candidate driver corresponding to the time period includes:

[0068] According to the historical traffic accident probability, determine the non-traffic accident probability corresponding to the period;

[0069] Based on the non-historical traffic accident probability and the safe driving probability of each candidate driver in the period, a target driver is determined from the candidate drivers.

[0070] The non-traffic accident probability corresponding to a certain time period refers to the probability that a traffic accident will not occur on the road section where the vehicle is located during that time period. This probability can be determined based on the historical traffic accident probability corresponding to the road section where the vehicle is located during that time period. The specific determination method can be pre-configured and is not limited in the embodiments of this application. For example, the non-traffic accident probability can be determined based on the following formula.

[0071] P k =1-P n

[0072] Among them, P k Represents the probability of non-traffic accidents corresponding to a period of time, P n Indicates the historical traffic accident probability corresponding to the road section where the vehicle is located during this period.

[0073] Optional, when based on the formula P k =1-P n When determining the probability of non-traffic accidents, the coefficients of the parameters in the formula can be changed according to actual needs to obtain different non-traffic accident probabilities. For example, the probability of non-traffic accidents can be obtained based on the formula P k =1-2P n Determine the probability of non-traffic accidents.

[0074] Optionally, for any time period, if the candidate driver's probability of safe driving in that time period is greater than the corresponding non-traffic accident probability of that time period, it can be said that the candidate driver is not prone to fatigue driving when driving the vehicle during that time period, and safe driving can be effectively guaranteed. Based on this, in an embodiment of the present application, the target driver can be determined from among the candidate drivers based on the relationship between the determined non-historical traffic accident probability and the safe driving probability of each candidate driver in that time period.

[0075] In an optional embodiment of the present application, a target driver is determined from among the candidate drivers based on the probability of non-historical traffic accidents and the safe driving probability of each candidate driver in the period, including:

[0076] At least one target driver is determined from the candidate drivers based on 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.

[0077] Optionally, for any time period, at least one target driver can be determined from each candidate driver based on the relationship between the non-historical traffic accident probability corresponding to that time period and each candidate driver's safe driving probability during that time period, where the sum of the safe driving probabilities of the at least one determined target driver during that time period is not less than the non-historical traffic accident probability. In this embodiment of the present application, since the sum of the safe driving probabilities of the at least one target driver during that time period is not less than the non-historical traffic accident probability, the at least one target driver is attentive when driving the vehicle during that time period, thereby effectively reducing the probability of a traffic accident.

[0078] In an optional embodiment of the present application, a target driver is determined from among the candidate drivers based on the probability of non-historical traffic accidents and the safe driving probability of each candidate driver in the period, including:

[0079] At least one target driver is selected from each candidate driver in descending order of the safe driving probability of each candidate driver in the period, until the sum of the safe driving probabilities of each selected target driver in the period is not less than the non-historical traffic accident probability.

[0080] Optionally, for any time period, the safe driving probability of each candidate driver corresponding to the time period can be arranged in descending order, and then the target driver can be selected from the candidate drivers in turn 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 probability of non-historical traffic accidents.

[0081] In one example, suppose that for time period k, the probability of non-historical traffic accidents corresponding to the time period is P k , there are m candidate drivers corresponding to this period. The safe driving probability of the first candidate driver in this period is E1, the safe driving probability of the second candidate driver in this period is E2, ..., the safe driving probability of the mth candidate driver in this period is E m Accordingly, when determining the target driver corresponding to the time period, the driver with the largest safe driving probability E can be selected from the safe driving probabilities of the candidate drivers in the order of the safe driving probabilities of the candidate drivers in the time period from large to small. k1 , if E k1 Not less than P k , then it can be determined that the target driver in this period is E k1 Corresponding candidate driver; if E k1 Less than P k , then the next largest safe driving probability E is selected from the safe driving probabilities of the candidate drivers in descending order of their safe driving probabilities during the period. k2 , and determine Ek1 +E k2 Is it not less than P k , if E k1 +E k2 Not less than P k , then E k1 The corresponding candidate driver and E k2 The corresponding candidate driver is the target driver for this period; if E k1 +E k2 Less than P k , then again according to the order of the safe driving probabilities of each candidate driver in this period from large to small, first select the third largest safe driving probability E from the safe driving probabilities of each candidate driver k3 , and determine E k1 +E k2 +E k3 Is it not less than P k , and so on, until the sum of the safe driving probabilities of each selected target driver in this period is not less than P k .

[0082] In an embodiment of the present application, the method further includes:

[0083] If there are duplicate drivers in the target drivers corresponding to the adjacent time periods, the target drivers corresponding to the adjacent time periods are adjusted according to the preset driver adjustment strategy.

[0084] Alternatively, if there are duplicate target drivers for adjacent time periods, this indicates that the duplicate driver will have driven the vehicle in two consecutive time periods. This can easily lead to driver fatigue, increasing the probability of a traffic accident. Based on this, in an embodiment of the present application, if there are duplicate target drivers for adjacent time periods, the target drivers for the adjacent time periods can be adjusted according to a preset driver adjustment strategy to ensure that there are no duplicate target drivers for the adjacent time periods, effectively preventing driver fatigue and reducing the probability of a traffic accident.

[0085] The specific adjustment method of the driver adjustment strategy can be pre-configured based on actual circumstances and is not limited in the embodiments of this application. For example, when a target driver corresponding to adjacent time periods is repeated, for any of the adjacent time periods, the candidate driver with the highest probability of safe driving can be selected from the unselected candidate drivers corresponding to that time period to replace the repeated driver. It is understood that the candidate driver replacing the repeated driver does not need to be the target driver corresponding to the adjacent time period of the time period.

[0086] Optionally, the method provided in the embodiment of the present application can be applied in multiple application scenarios, such as in the fields of intelligent connected vehicles and smart travel. For example, the method provided in the embodiment of the present application can be integrated into an application applet or a map application. When a vehicle needs to be driven for a long time, the driver corresponding to the vehicle at different time periods during the driving period can be determined based on the method through the application applet or the map application. This can not only reduce driving risks, but also improve the level of refined operations of freight companies. Optionally, when determining the drivers corresponding to different time periods based on the method provided in the embodiment of the present application, it can be determined in real time during driving the vehicle, such as determining the target driver required for the next time period based on the current time period; or when the route driven by the vehicle and the time to start driving are known, the road section that the vehicle may be in in each section after the vehicle starts driving is predicted, and then the target driver required for each time period is pre-selected and determined. When the vehicle starts driving, the vehicle is driven directly according to the target driver required for each time period determined in advance.

[0087] In order to better understand the method provided in the embodiment of the present application, the following Figure 2 The method is described in detail in the application scenario of large truck driving shown in FIG. Figure 3 Each truck (truck 1 to truck 5) is equipped with an onboard computer. When driving the truck, the onboard computer installed in each truck can obtain parameter data for determining the target driver required for each time period from the traffic management cloud platform, and then determine the target driver required for each time period based on the obtained parameter data. Optionally, the method of determining the target driver required for each time period is described below using one of the trucks as an example. Figure 4 shown.

[0088] Step S401: The onboard computer obtains the ratio of the number of truck accidents per million kilometers in different time periods of the day to the total number of truck accidents per million kilometers of the day (i.e., the first traffic accident probability mentioned above);

[0089] Specifically, the onboard computer obtains the percentage of truck accidents per million kilometers in different time periods of the day to the total number of truck accidents per million kilometers of the day from the traffic management cloud platform. For example, assuming there are n time periods, these n time periods are called single time periods 1, 2, ..., n (that is, the length of each time period is 24 / n), and a1, a2, ..., a n represents the number of truck accidents per million kilometers in each of the n time periods. The proportion of the number of truck accidents per million kilometers in each time period to the total number of truck accidents per million kilometers in the whole day can be further calculated, and w1 = a1 / (a1+a2+...+a n ),w2=a2 / (a1+a2+...+a n ),...,wn =a n / (a1+a2+...+a n )express.

[0090] Step S402: The onboard computer determines the driving state characteristic distribution of each candidate driver;

[0091] The onboard computer pre-configures which drivers can be used in each time period 1, 2, ..., n for the truck. That is, the candidate drivers corresponding to each time period are known. For any time period, there are m candidate drivers for driving in that time period. These m candidate drivers are called drivers 1, 2, ..., m respectively. Then, the average duration of each candidate driver's one-time stay in the driving state is obtained from the traffic management cloud platform. Based on the exponential distribution and the duration ΔT of the period, the probability that each candidate driver 1, 2, ..., m stays in the driving state for a duration exceeding ΔT (i.e., the driving state characteristic distribution) is determined and recorded as

[0092] In step S403, the onboard computer determines the traffic accident rate (i.e., the historical traffic accident probability mentioned above) of truck traffic accidents occurring on each road section at different time periods of the day:

[0093] Specifically, in step S401, the percentage of truck accidents per million kilometers in different time periods of the day to the total number of truck accidents per million kilometers of the day is determined. This percentage can describe the relative frequency of truck traffic accidents in different time periods of the day. To this end, the onboard computer obtains the historical traffic accident rate involving trucks (i.e., the second traffic accident probability mentioned above) for the current section of the road where the truck is located from the traffic management department, and records it as p. history Then, based on the proportion of the number of truck accidents per million kilometers in each time period determined in step S401 to the total number of truck accidents per million kilometers in the day, the traffic accident rate of truck traffic accidents in each section of the road in different time periods of the day is obtained, which is recorded as p1=p history w1,p2=p history w2,...,p n =p history w n ;

[0094] In step S404, the onboard computer determines how many truck drivers are needed in different time periods.

[0095] Specifically, since the onboard computer already knows the candidate drivers corresponding to each time period, the probability sets of the candidate drivers corresponding to each time period staying in the driving state for a duration exceeding ΔT are S1, S2, ..., S n ,S1,S2,...,S nCan be expressed as Correspondingly, for time period k, from the probability set S k Select the maximum probability, denoted as E k1 , judge E k1 ≥1-p n Is it true? If so, then in time period k, only the corresponding E k1 The other drivers can rest; if E k1 <1-p n ), and then the second largest probability from the probability set, denoted as E k2 , judge E k1 +E k2 ≥1-p n Is it true? If so, then in time period k, only the corresponding E k1 and E k2 The driver who needs to be used in each time period can be used, and other drivers can rest, and so on.

[0096] Optionally, in the embodiment of the present application, based on the solution in the prior art (such as the solution of frequently changing truck drivers) and the solution provided in the embodiment of the present application, ten comparative experiments were conducted on the fatigue driving time of truck drivers passing through the same traffic intersection. The specific implementation results are shown in Table 1:

[0097] Table 1 Experimental results

[0098] Experimental level sequence Ratio of the duration of driver fatigue driving in the prior art and this application First experiment 1.74 Second experiment 1.72 The third experiment 1.75 The fourth experiment 1.74 The fifth experiment 1.76 The sixth experiment 1.72 The seventh experiment 1.71 The eighth experiment 1.76 Ninth experiment 1.73 The tenth experiment 1.76

[0099] Among them, the first column in Table 1 (i.e., the experimental order) indicates the number of experiments, such as the second row in the first column indicates the first experiment, and the second column (i.e., the ratio of the fatigue driving time of the driver in the prior art and the scheme provided in the embodiment of the present application) indicates the experimental results of each experiment, that is, the ratio of the fatigue driving time of the truck driver passing through the same traffic intersection when the scheme based on the prior art scheme and the scheme provided in the embodiment of the present application are compared. For example, the second row in the second column indicates that in the first experiment, 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 1.74. Correspondingly, 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. It can be seen that the performance of the scheme provided in the embodiment of the present application is better than the prior art scheme, and it can more effectively alleviate the driver's fatigue driving state compared to the scheme in the prior art.

[0100] The embodiment of the present application provides a vehicle driving control device, such as Figure 5 As shown, 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:

[0101] Traffic accident probability acquisition module 601, for acquiring, for any time period, the historical traffic accident probability corresponding to the road section where the vehicle is located during that time period;

[0102] A driving status information acquisition module 602 is used to acquire the driving status information of each candidate driver corresponding to the time period;

[0103] The target driver determination module 603 is configured to determine a target driver for the time period from among the candidate drivers based on the driving status information and historical traffic accident probabilities of the candidate drivers corresponding to the time period.

[0104] Optionally, when obtaining the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period, the traffic accident probability acquisition module is specifically used to:

[0105] Obtain the first traffic accident probability corresponding to the vehicle type of the vehicle in the time period on all road sections;

[0106] Obtain a second traffic accident probability corresponding to the vehicle type in the road section where the vehicle is located during the time period;

[0107] Based on the first traffic accident probability and the second traffic accident probability corresponding to the vehicle type in the road section where the vehicle is located during the period, the historical traffic accident probability corresponding to the road section where the vehicle is located during the period is determined.

[0108] Optionally, for any candidate driver, the driving status information of the candidate driver includes an average duration of continuous driving of the candidate driver;

[0109] The target driver determination module is specifically configured to determine the target driver for the time period from the candidate drivers based on the driving status information and historical traffic accident probabilities of the candidate drivers corresponding to the time period:

[0110] Determine the safe driving probability of each candidate driver in the period based on the average duration of each candidate driver's continuous driving and the duration of the period;

[0111] A target driver is determined from among the candidate drivers based on the safe driving probability and historical traffic accident probability of each candidate driver corresponding to the time period.

[0112] Optionally, when determining the target driver from the candidate drivers based on the safe driving probability and historical traffic accident probability of each candidate driver corresponding to the time period, the target driver determination module is specifically configured to:

[0113] According to the historical traffic accident probability, determine the non-traffic accident probability corresponding to the period;

[0114] Based on the non-historical traffic accident probability and the safe driving probability of each candidate driver in the period, a target driver is determined from the candidate drivers.

[0115] Optionally, when 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, the target driver determination module is specifically configured to:

[0116] At least one target driver is determined from the candidate drivers based on 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.

[0117] Optionally, when 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, the target driver determination module is specifically configured to:

[0118] At least one target driver is selected from each candidate driver in descending order of the safe driving probability of each candidate driver in the time period, until the sum of the safe driving probabilities of each selected target driver in the time period is not less than the non-historical traffic accident probability.

[0119] Optionally, the device further includes an adjustment module, configured to:

[0120] When there are duplicate target drivers corresponding to adjacent time periods, the target drivers corresponding to the adjacent time periods are adjusted according to a preset driver adjustment strategy.

[0121] The vehicle driving control device of the embodiment of the present application can execute a vehicle driving control method provided by the embodiment of the present application. The implementation principle is similar and will not be repeated here.

[0122] In this embodiment of the present application, when determining the driver for a vehicle, the driver for each time period can be determined based on the historical traffic accident probability corresponding to the road section the vehicle is on during each time period, as well as the driving status information of each candidate driver corresponding to each time period. In other words, in this embodiment of the present application, the number of drivers required for each time period can be dynamically and reasonably adjusted based on the actual historical traffic accident probability of the road section the vehicle is on and the driving status information of each candidate driver. This eliminates the need for frequent driver changes, effectively preventing driver fatigue while maintaining vehicle driving efficiency.

[0123] The present application embodiment provides an electronic device, such as Figure 6 As shown, Figure 6 The electronic device 2000 shown includes a processor 2001 and a memory 2003. The processor 2001 and the memory 2003 are connected, for example, via a bus 2002. Optionally, the electronic device 2000 may further include a transceiver 2004. It should be noted that in actual applications, the number of transceivers 2004 is not limited to one, and the structure of the electronic device 2000 does not constitute a limitation on the embodiments of the present application.

[0124] The processor 2001 is used in the embodiment of the present application to implement Figure 5 The functions of each module are shown.

[0125] Processor 2001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 2001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0126] The bus 2002 may include a path for transmitting information between the above components. The bus 2002 may be a PCI bus or an EISA bus, etc. The bus 2002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0127] The memory 2003 may be a ROM or other type of static storage device that can store static information and computer programs, a RAM or other type of dynamic storage device that can store information and computer programs, or an EEPROM, a CD-ROM or other optical disk storage, an optical disc storage (including a compact disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store or in the form of a data structure the desired computer program and can be accessed by a computer, but is not limited thereto.

[0128] The memory 2003 is used to store the computer program for executing the application program of the present application solution, and the execution is controlled by the processor 2001. The processor 2001 is used to execute the computer program of the application program stored in the memory 2003 to implement Figure 5 The illustrated embodiment provides an operation of the vehicle driving control device.

[0129] 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, and when the computer program is executed by the processor, the processor performs any one of the methods in the above embodiments.

[0130] An embodiment of the present application provides a computer-readable storage medium for storing a computer program. When the computer program is run on a computer, the computer can execute any one of the methods in the above embodiments.

[0131] An embodiment of the present application provides a vehicle-mounted device, which includes: a processor and a memory: the memory is configured to store a computer program, and when the computer program is executed by the processor, the processor implements any one of the methods in the above embodiments.

[0132] According to one aspect of the present application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the vehicle driving control method provided in the various optional implementations described above.

[0133] The nouns and implementation principles involved in a computer-readable storage medium in this application can be specifically referred to a vehicle driving control method in an embodiment of this application, and will not be repeated here.

[0134] In this embodiment of the present application, when determining the driver for a vehicle, the driver for each time period can be determined based on the historical traffic accident probability corresponding to the road section the vehicle is on during each time period, as well as the driving status information of each candidate driver corresponding to each time period. In other words, in this embodiment of the present application, the number of drivers required for each time period can be dynamically and reasonably adjusted based on the actual historical traffic accident probability of the road section the vehicle is on and the driving status information of each candidate driver. This eliminates the need for frequent driver changes, effectively preventing driver fatigue while maintaining vehicle driving efficiency.

[0135] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0136] The above description is only part of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A vehicle driving control method, characterized in that: include: For any period of time, obtain the historical traffic accident probability corresponding to the road section where the vehicle is located during that period; Obtaining driving status information of each candidate driver corresponding to the time period; wherein, for any candidate driver, the driving status information of the candidate driver includes an average duration of continuous driving of the candidate driver; Determine the safe driving probability of each candidate driver in the period based on the average duration of each candidate driver's continuous driving and the duration of the period; Based on the safe driving probability of each candidate driver corresponding to the time period and the historical traffic accident probability, a target driver for the time period is determined from the candidate drivers.

2. The method according to claim 1, characterized in that The obtaining of the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period includes: Obtain the first traffic accident probability corresponding to the vehicle type of the vehicle in all road sections during the time period; Obtaining a second traffic accident probability corresponding to the vehicle type in the road section where the vehicle is located during the time period; Based on the first traffic accident probability and the second traffic accident probability corresponding to the vehicle type in the road section where the vehicle is located during the period, the historical traffic accident probability corresponding to the road section where the vehicle is located during the period is determined.

3. The method according to claim 1 or 2, characterized in that The step of determining a target driver for the time period from among the candidate drivers based on the safe driving probability of each candidate driver corresponding to the time period and the historical traffic accident probability includes: Determining the non-historical traffic accident probability corresponding to the time period based on the historical traffic accident probability; Based on the non-historical traffic accident probability and the safe driving probability of each candidate driver in the time period, a target driver for the time period is determined from the candidate drivers.

4. The method according to claim 3, characterized in that Determining a target driver for the time period from among the candidate drivers based on the non-historical traffic accident probability and the safe driving probability of each candidate driver for the time period includes: At least one target driver is determined from the candidate drivers based on 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.

5. The method according to claim 4, characterized in that Determining a target driver for the time period from among the candidate drivers based on the non-historical traffic accident probability and the safe driving probability of each candidate driver for the time period includes: At least one target driver is selected from each of the candidate drivers in descending order of the safe driving probability of each of the candidate drivers in the time period, 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.

6. The method according to claim 1, characterized in that For any of the time periods, the method further comprises: If there are duplicate drivers in the target drivers corresponding to the adjacent time periods, the target drivers corresponding to the adjacent time periods are adjusted according to the preset driver adjustment strategy.

7. A vehicle driving control device, characterized in that: include: The traffic accident probability acquisition module is used to obtain the historical traffic accident probability corresponding to the road section where the vehicle is located in any time period; a driving state information acquisition module, configured to acquire driving state information of each candidate driver corresponding to the time period; wherein, for any candidate driver, the driving state information of the candidate driver includes an average duration of continuous driving of the candidate driver; The target driver determination module is used to determine the safe driving probability of each candidate driver in a period based on the average duration of each candidate driver's continuous driving and the duration of the period; and to determine the target driver for the period from the candidate drivers based on the safe driving probability of each candidate driver in the period corresponding to the period and the historical traffic accident probability.

8. The device according to claim 7, characterized in that When obtaining the historical traffic accident probability corresponding to the road section where the vehicle is located during the time period, the traffic accident probability acquisition module is used to: Obtain the first traffic accident probability corresponding to the vehicle type of the vehicle in all road sections during the time period; Obtaining a second traffic accident probability corresponding to the vehicle type in the road section where the vehicle is located during the time period; Based on the first traffic accident probability and the second traffic accident probability corresponding to the vehicle type in the road section where the vehicle is located during the period, the historical traffic accident probability corresponding to the road section where the vehicle is located during the period is determined.

9. The device according to claim 7 or 8, characterized in that The target driver determination module is configured to determine the target driver for the time period from the candidate drivers based on the safe driving probability of each candidate driver corresponding to the time period and the historical traffic accident probability: Determining the non-historical traffic accident probability corresponding to the time period based on the historical traffic accident probability; Based on the non-historical traffic accident probability and the safe driving probability of each candidate driver in the time period, a target driver for the time period is determined from the candidate drivers.

10. The device according to claim 9, characterized in that The target driver determination module is configured to determine the target driver for the time period from the candidate drivers based on the non-historical traffic accident probability and the safe driving probability of each candidate driver for the time period: At least one target driver is determined from the candidate drivers based on 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.

11. The device according to claim 10, characterized in that The target driver determination module is configured to determine the target driver for the time period from the candidate drivers based on the non-historical traffic accident probability and the safe driving probability of each candidate driver for the time period: At least one target driver is selected from each of the candidate drivers in descending order of the safe driving probability of each of the candidate drivers in the time period, 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.

12. The device according to claim 7, characterized in that The device further includes an adjustment module, which is configured to: If there are duplicate drivers in the target drivers corresponding to the adjacent time periods, the target drivers corresponding to the adjacent time periods are adjusted according to the preset driver adjustment strategy.

13. A vehicle-mounted device, characterized in that: Including processor and memory: The memory is configured to store a computer program, which, when executed by the processor, causes the processor to perform the method according to any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store a computer program. When the computer program is run on a computer, the computer can execute the method according to any one of claims 1 to 6.

Images