Driving data analysis method of intelligent networked automobile and related product

Abstract

The invention provides a driving data analysis method of an intelligent networked automobile and a related product. The method comprises the steps that after historical driving data of a current automobile is obtained, historical driving modes of the current automobile in all preset driving scenes can be obtained, and optimal driving modes of all the preset driving scenes are obtained through a path optimization model based on an improved skill optimization algorithm; and comparing each historical driving mode with the optimal driving mode to obtain the historical driving ability of the driver in each preset driving mode, and then predicting the current driving ability of the driver in each preset driving mode through the historical driving ability. In the current driving environment, the optimal driving strategy of the current driving environment can be generated by adopting the path optimization model, and whether the driving prompt information is generated according to the optimal driving strategy is judged according to the current driving ability of the driver, so that the information amount of the driving prompt information is reduced, and the interference to the driver is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent networked vehicles, and in particular to a driving data analysis method for intelligent networked vehicles and related products. Background Art

[0002] With the continuous improvement of people's living standards and the rapid development of science and technology, cars have become a common industrial product in many families. At the same time, the traffic volume on the roads is also increasing, and the road driving safety problem is becoming more and more serious.

[0003] Intelligent assisted driving technology can develop an optimal driving strategy based on the vehicle's current driving environment and generate driving prompts based on this strategy to help the driver navigate the current driving scenario and improve driving safety. However, existing technologies fail to consider the driver's ability to cope with the current driving environment. As a result, driving prompts are used to guide the driver regardless of their ability to cope with the current driving environment. Excessive prompts can cause interference to the driver and even affect the current driving safety of the vehicle. Summary of the Invention

[0004] The purpose of the present invention is to provide a driving data analysis method and related products for intelligent networked vehicles, which are used to reduce the amount of driving prompt information, thereby reducing interference to the driver and achieving the purpose of improving vehicle driving safety.

[0005] In a first aspect, the present invention provides a method for analyzing driving data of an intelligent networked vehicle, comprising:

[0006] The steps of predicting the current driving ability of the driver based on the historical driving data of the current car, and generating driving prompt information based on the current driving ability;

[0007] The step of predicting the driver's current driving ability based on the historical driving data of the current vehicle includes:

[0008] Acquire historical driving data of the current vehicle in a plurality of consecutive historical time periods, and respectively acquire historical driving modes of the current vehicle in each preset driving scenario from each of the historical driving data;

[0009] Obtaining a preset driving cost function, and using a path optimization model based on an improved skill optimization algorithm to obtain an optimal driving mode for the preset driving scenario according to the preset driving cost function;

[0010] By comparing each of the historical driving modes with the optimal driving mode, the driver's historical driving ability in a plurality of preset driving modes during each of the historical time periods is obtained, and the driver's current driving ability in each of the preset driving modes is predicted based on each of the historical driving abilities;

[0011] The step of generating driving prompt information according to the current driving ability includes:

[0012] Obtaining a current driving environment via the Internet, generating an optimal driving strategy for the current driving environment using the path optimization model, and determining minimum driving capabilities required by each of the preset driving modes for the current driving environment based on the optimal driving strategy;

[0013] Determining whether each of the current driving capabilities is greater than a corresponding minimum driving capability;

[0014] If not, the driving prompt information is generated according to the optimal driving strategy.

[0015] Furthermore, the step of predicting the driver's current driving ability in each of the preset driving modes based on each of the historical driving abilities includes:

[0016] Obtaining a correction coefficient for each of the preset driving modes based on the frequency of occurrence of each of the preset driving scenarios in each of the historical driving data;

[0017] constructing driving ability change curves for each of the preset driving modes based on the historical driving abilities;

[0018] The current driving abilities are predicted according to the driving ability change curves, and the correction coefficients are used to correct the corresponding current driving abilities.

[0019] Furthermore, after the step of determining whether each of the current driving capabilities is greater than the corresponding minimum driving capabilities, the method further includes:

[0020] If each of the current driving capabilities is greater than the corresponding minimum driving capabilities, simplified driving prompt information is generated according to the optimal driving strategy.

[0021] Furthermore, after the step of generating the driving prompt information according to the optimal driving strategy, the method further includes:

[0022] The driving prompt information is simplified according to the current driving ability.

[0023] Furthermore, after the step of generating the driving prompt information according to the optimal driving strategy, the method further includes:

[0024] Sending a request message to a user terminal to obtain a user instruction from the user terminal;

[0025] When the user instruction is to allow the driving guidance to be turned on, the driving prompt information is played in the form of voice.

[0026] In a second aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the driving data analysis method for an intelligent networked vehicle as described above are implemented.

[0027] In a third aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for analyzing driving data of an intelligent networked vehicle.

[0028] In a fourth aspect, the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the driving data analysis method for an intelligent networked vehicle as described in any one of the above items.

[0029] The technical solution of the present invention, after obtaining the current vehicle's historical driving data, can then determine the current vehicle's historical driving patterns under various preset driving scenarios. A path optimization model based on an improved skill optimization algorithm is then used to determine the optimal driving pattern for each preset driving scenario. By comparing each historical driving pattern with the optimal driving pattern, the driver's historical driving ability for each preset driving pattern is determined. This historical driving ability is then used to predict the driver's current driving ability for each preset driving pattern. Under the current driving environment, the path optimization model is used to generate the optimal driving strategy for the current driving environment. Based on the driver's current driving ability, it is determined whether driving prompts should be generated based on the optimal driving strategy. This reduces the amount of driving prompts, minimizes driver interference, and ultimately improves vehicle safety.

[0030] Based on the following detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings, those skilled in the art will become more aware of the above and other objects, advantages and features of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Hereinafter, some specific embodiments of the present invention will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0032] Figure 1 is a schematic flow chart of a method for analyzing driving data of an intelligent networked vehicle according to one embodiment of the present invention;

[0033] Figure 2 is a schematic flow chart of predicting a driver's current driving ability based on historical driving data of a current vehicle according to one embodiment of the present invention;

[0034] Figure 3 is a schematic flow chart of generating driving prompt information according to the driver's current driving ability according to one embodiment of the present invention;

[0035] Figure 4 is a schematic flow chart of predicting a driver's current driving ability in various preset driving modes according to one embodiment of the present invention;

[0036] Figure 5 is a schematic flow chart of generating driving prompt information according to the driver's current driving ability according to another embodiment of the present invention;

[0037] Figure 6 is a schematic diagram of a computer program product according to one embodiment of the present invention;

[0038] Figure 7 is a schematic diagram of a computer-readable storage medium according to one embodiment of the present invention; and

[0039] Figure 8 is a schematic diagram of a computer device according to one embodiment of the present invention. DETAILED DESCRIPTION

[0040] Refer to the following Figures 1 to 8 To describe a driving data analysis method and related products for an intelligent networked car according to an embodiment of the present invention. In the description of this embodiment, it should be understood that the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features, that is, include one or more of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. When a feature "includes or contains" one or some of the features it covers, unless otherwise specifically described, this indicates that other features are not excluded and may further include other features.

[0041] See also Figure 1 , Figure 1 This is a schematic flow chart of a driving data analysis method for an intelligent networked vehicle according to an embodiment of the present invention. The method can reduce the amount of driving prompt information to reduce interference to the driver and achieve the purpose of improving vehicle driving safety.

[0042] like Figure 1 As shown, the driving data analysis method of the intelligent networked car of the present invention can generally include the following steps:

[0043] Step S101: predicting the driver's current driving ability based on the historical driving data of the current car;

[0044] Step S102: Generate driving prompt information based on the driver's current driving ability.

[0045] In the above step S101, the method for predicting the current driving ability of the driver based on the historical driving data of the current car is as follows: Figure 2 As shown, the following steps are included:

[0046] Step S111: Acquire historical driving data of the current vehicle in multiple consecutive historical time periods;

[0047] Step S112: extracting information from each historical driving data to obtain the historical driving mode of the current vehicle in each preset driving scenario within each historical time period;

[0048] Step S113: obtaining a preset driving cost function, and using a path optimization model based on an improved skill optimization algorithm to generate an optimal driving mode for each preset driving scenario according to the preset driving cost function;

[0049] Step S114: comparing the historical driving mode and the optimal driving mode for each preset driving scenario to obtain the driver's historical driving ability in each preset driving mode during each historical time period;

[0050] Step S115: predicting the driver's current driving ability in each preset driving mode based on the driver's historical driving ability in each preset driving mode.

[0051] In this embodiment, the current car can record driving data in real time during driving, and store each driving data according to the time when the driving data is generated; in the above step S111, the current car can obtain the historical driving data of the current car in multiple consecutive historical time periods from the stored data.

[0052] In step S112, the preset driving scenarios include road maintenance, vehicle accident, road breakdown, and road turning. In this embodiment, the preset driving scenarios can be obtained based on road condition information detected during the current vehicle's driving, road condition information collected by cameras and other information collection devices installed on the road, and road condition information collected by other vehicles on the road section the current vehicle has traveled and uploaded to the internet. The historical driving patterns for each preset driving scenario include the current vehicle's speed, distance to the preceding vehicle, turn position and direction, and lane change position and direction for each preset driving scenario.

[0053] In the above step S113, it is assumed that in a driving mode, the number of left lane changes is S left , the number of right lane changes is S right , the number of left turns is Turn left , the number of right turns is Turn right , the driving speed between the i-1th driving point and the i-th driving point is v i , then the preset driving cost function Value of this driving mode is

[0054]

[0055] Among them, α1, α2 and α3 are lane change coefficient, steering coefficient and speed coefficient respectively. The above driving points include intermediate points, starting point and end point. N is the total number of intermediate points. Y i The driving speed is v i 's evaluation value, and:

[0056]

[0057] Among them, a, b, and c are the coefficient of the quadratic term, the coefficient of the linear term, and the constant, respectively.

[0058] In this embodiment, a path optimization model based on an improved skill optimization algorithm is used to obtain the optimal driving mode for a preset driving scenario according to the preset driving cost function, including:

[0059] First, the starting position and the end position of the preset driving scene are obtained, and the number N of intermediate points is set according to the distance between the starting position and the end position.

[0060] Then, the path optimization model based on the improved skill optimization algorithm is in the exploration stage and the development stage. The exploration stage is to acquire skills from experts, which is equivalent to searching for new solutions in the optimization process; the development stage is skill improvement based on time and personal efforts, that is, fine-tuning on the basis of existing solutions to obtain the optimal solution.

[0061] In this embodiment, N intermediate points are randomly selected from a preset driving scenario to obtain a candidate solution. Then, N intermediate points are randomly selected from the same scenario again to obtain another candidate solution. This process is repeated to obtain multiple candidate solutions, thereby forming a candidate solution population consisting of multiple candidate solutions, completing the initialization of the candidate solution cluster. The path optimization model and an improved skill optimization algorithm are then used to iteratively update the candidate solutions. During the iterative update of the candidate solution population, if the number of iterative updates is less than a set iteration threshold, the candidate solution cluster is updated in the exploration phase. If the number of iterative updates is greater than or equal to the set iteration threshold, the candidate solution cluster is updated in the development phase.

[0062] In the exploration phase of the path optimization model, the methods for updating the candidate solution population include:

[0063] An adaptive weight factor is generated according to the number of iterative updates, and the candidate solution population is updated according to the adaptive weight factor. The formula used for the update is:

[0064]

[0065] in, is the i-th candidate solution after the t-th iteration update in the exploration phase; K represents the position information of the mth intermediate point in the i-th candidate solution; i,m,t is the position information of the mth intermediate point in the ith candidate solution before the tth iteration update; r t is a random number in the range of [0,1] in the tth iteration update; E i,m,t I represents the expert set of the mth middle point in the i-th candidate solution in the t-th iteration update, and the expert set includes the location information of the mth middle point of a random candidate solution among the candidate solutions with the smallest travel cost value calculated by the preset travel cost function at the t-th iteration; t is the value randomly selected from the set {1,2} in the tth iteration update; w t is the adaptive weight factor in the tth iteration update, and the larger the number of iteration updates t is, the larger the adaptive weight factor w is. t The smaller the value of .

[0066] In the exploration phase, the adaptive weight factor w t After the improved skill optimization algorithm is introduced, the adaptive weight factor w t When it is larger, it can enhance the global search ability of the algorithm, and when the adaptive weight factor w tWhen is small, the algorithm’s search accuracy and speed can be improved. Therefore, after introducing the adaptive weight factor, the algorithm’s search accuracy and speed can be gradually reduced during the iterative update process, while the search capability can be gradually increased, thus avoiding the problem of falling into local optimality during the exploration phase.

[0067] In the development phase of the exploration phase of the path optimization model, the method of updating the candidate solution population using the improved skill optimization algorithm includes:

[0068] In the random number r t When less than 0.5:

[0069]

[0070] In the random number r t When greater than or equal to 0.5:

[0071]

[0072] in, is the i-th candidate solution after the t-th iteration update in the development phase, is the location information of the mth intermediate point in the i-th candidate solution, K i,m,t is the position information of the mth intermediate point in the ith candidate solution before the tth iterative update in the development phase, t is the number of iterative updates, lb and ub are the lower and upper bounds of the preset driving scenario, respectively.

[0073] It should be noted that the middle point in each candidate solution should be located on the lane of the preset driving scenario. If, during the iterative update process, the middle point in the candidate solution is not on the lane of the preset driving scenario, the position closest to the middle point on the lane of the preset driving scenario will be set.

[0074] After the candidate solution population is iteratively updated a set number of times using the improved skill optimization algorithm, the resulting candidate solution population is the optimal candidate solution population. Then, the driving mode corresponding to each candidate solution in the optimal candidate population is generated, and the preset driving cost function is used to calculate the driving cost value of each driving mode. The driving mode with the smallest driving cost value is taken as the optimal driving mode corresponding to the preset driving environment.

[0075] In this embodiment, taking a candidate solution from the optimal candidate solution population as an example, a method for generating a driving mode for the candidate solution includes: constructing a driving path between adjacent driving points in the candidate solution to obtain a driving path from a starting point to an end point, and determining a driving speed between each adjacent driving point based on the length of the driving path between each adjacent driving point.

[0076] The method for constructing a driving path between adjacent driving points in the candidate solution includes:

[0077] Determine whether adjacent driving points in the candidate solution are located on the same lane;

[0078] If two adjacent driving points are not located in the same lane, determine whether the current driving direction of the car at these two driving points is the same;

[0079] If they are the same, it is determined that a lane change has occurred between the two driving points. The direction of the lane change is then determined based on the relative positions of the two driving points, and the location of the lane change is determined based on whether there is an obstacle between the two driving points.

[0080] If they are not the same, it is determined that a turn has occurred at the two driving points. The direction of the turn is determined based on the relative positions of the two driving points, and the path and angle of the turn are determined based on whether there is an obstacle between the two driving points.

[0081] If two adjacent driving points are located on the same lane, determine whether there is an obstacle between the two adjacent driving points;

[0082] If yes, then the driving path between the two driving points is determined with bypassing the obstacle as the constraint condition, and it can be determined that at least two lane changes are required between the two driving points;

[0083] If not, the straight line path between the two driving points is used as the driving path of the two driving points.

[0084] The method for determining the driving speed between adjacent driving points according to the length of the driving path between the adjacent driving points includes:

[0085] Preset the correspondence between multiple path length value intervals and preset driving speeds, and set the preset optimal speeds for lane changes and turns. The larger the threshold of the path length value interval, the higher the corresponding preset driving speed;

[0086] If the driving path between two adjacent driving points is a straight path, then the preset driving speed between the two adjacent driving points is obtained according to the path length value interval of the straight path;

[0087] If a lane change occurs between two adjacent driving points, the preset optimal speed for lane change is used as the driving speed between the two adjacent driving points;

[0088] If a turn occurs between two adjacent driving points, the preset optimal speed for the turn is used as the driving speed between the two adjacent driving points.

[0089] In the above step S114, the preset driving modes include a left lane change driving mode, a right lane change driving mode, a left turn driving mode, a right turn driving mode and a straight driving mode.

[0090] Assume that there are M preset driving scenarios in the i-th historical time period, and the number of left lane changes in the historical driving mode of the j-th preset driving scenario is SL' i,j , the number of left lane changes in the optimal driving mode of the preset driving scene is Then the driver's historical driving score FL for left lane change in the jth preset driving scenario is i,j for:

[0091]

[0092] The driver's driving ability CL in the left lane change driving style in the i-th historical period i for:

[0093]

[0094] where β left is the left lane change capability correction factor.

[0095] In the i-th historical time period, the number of right lane changes in the historical driving mode of the j-th preset driving scenario is SR' i,j , the number of right lane changes in the optimal driving mode of the preset driving scene is Then the driver's historical driving score FR for right lane change in the jth preset driving scenario is i,j for:

[0096]

[0097] The driver's historical driving ability CR in the right lane change driving style in the i-th historical time period i for:

[0098]

[0099] where β right is the right lane change capability correction factor.

[0100] In the i-th historical time period, the number of left turns in the historical driving mode of the j-th preset driving scenario is M left , the angle of the kth left turn is RL' i,j,k , the angle of the kth left turn in the optimal driving mode of the preset driving scene is Then the driver's historical driving score FRL in the left-turn driving mode in the j-th preset driving scenario is i,j,k for

[0101]

[0102] The driver's historical driving capability CRL in left-turn driving mode in the i-th historical period i for:

[0103]

[0104] Among them, γ left is the left-turn capability correction factor.

[0105] In the i-th historical time period, the number of right turns in the historical driving mode of the j-th preset driving scenario is M right , the angle of the kth right turn is RR' i,j,k , the angle of the kth right turn in the optimal driving mode of the preset driving scene is Then the driver's historical driving score FRR in the right turn driving mode in the jth preset driving scenario is i,j for

[0106]

[0107] The driver's historical driving ability CRR in the right turn driving mode in the i-th historical period i for:

[0108]

[0109] Among them, γ right is the right-turn capability correction factor.

[0110] In the i-th historical time period, the average driving speed in the historical driving mode of the j-th preset driving scenario is V i,j , the average driving speed in the optimal driving mode of the preset driving scene is Then the driver’s historical driving score FV in the j-th preset driving scenario is i,j for

[0111]

[0112] In the above step S102, the method of generating driving prompt information according to the current driving ability of the driver is as follows: Figure 3 As shown, the following steps are included:

[0113] Step S121: obtaining the current driving environment through the Internet, and using a path optimization model based on an improved skill optimization algorithm to obtain the optimal driving strategy for the current driving environment;

[0114] Step S122: Determine the minimum driving capability of each preset driving mode required by the current driving environment based on the optimal driving strategy;

[0115] Step S123: determining whether the driver's current driving ability in each preset driving mode is greater than the corresponding minimum driving ability required by the current driving environment;

[0116] If yes, proceed to step S124;

[0117] Step S124: Generate driving prompt information for the current driving scene based on the optimal driving strategy for the current driving environment.

[0118] In this embodiment, the current driving environment can be obtained based on road condition information collected by information collection devices such as cameras installed on the road, as well as road condition information collected by other vehicles in the current driving environment and uploaded to the Internet.

[0119] In this embodiment, the correspondence between multiple left lane change value intervals and preset left lane change coefficients, the correspondence between multiple right lane change value intervals and preset right lane change coefficients, the correspondence between multiple left lane width intervals and left lane change difficulty, and the correspondence between multiple right lane width intervals and right lane change difficulty can be pre-set.

[0120] Assume that the number of left lane changes in the optimal driving strategy of the current driving environment is SL, and the minimum lane width after each left lane change is L min , we can get the preset left lane change coefficient corresponding to the left lane change value interval where the number of left lane changes SL is located, and get the corresponding preset left lane change coefficient θ left , according to the minimum lane width L min Left lane change difficulty HL corresponding to the left lane width range left , the minimum driving ability required for the left lane change in the current driving environment is θ left ×HL left .

[0121] Assume that the number of right lane changes in the optimal driving strategy of the current driving environment is SR, and the minimum lane width after each right lane change is R min , we can get the preset right lane change coefficient corresponding to the right lane change value interval where the number of right lane changes SR is located, and get the corresponding preset right lane change coefficient θ right , according to the minimum lane width R min The right lane width interval corresponding to the right lane change difficulty HL right , the minimum driving ability required for the right lane change in the current driving environment is θ right ×HL right .

[0122] In this embodiment, the number of left turns in the optimal driving strategy of the current driving environment is SRL, and the minimum lane width during each left turn is RL. min , assuming the driving ability of the reference left-turn driving mode is CL0, if the minimum lane width is RL min If the minimum lane width is greater than the preset left-turn lane width threshold RL0, the minimum driving capability required for the left lane change in the current driving environment is lnSRL×CL0; if the minimum lane width is RL min is greater than the preset left-turn lane width threshold RL0, then the minimum driving ability required for the left lane change in the current driving environment is

[0123] In this embodiment, the number of right turns in the optimal driving strategy of the current driving environment is SRR, and the minimum lane width during each right turn is RR. min , assuming the driving ability of the reference right-turn driving mode is CR0, if the minimum lane width is RR min If the minimum lane width is greater than the preset right turn lane width threshold RR0, the minimum driving capability required for the right lane change in the current driving environment is lnSRR×CR0; if the minimum lane width is RL min is greater than the preset right-turn lane width threshold RR0, then the minimum driving ability required for the right lane change driving mode in the current driving environment is

[0124] In this embodiment, it is assumed that the shortest straight distance in the optimal driving strategy of the current driving environment is DL min , then the minimum driving ability required for straight driving in the current driving environment is DR0 is the preset driving capability for straight driving, and DL0 is the preset straight length.

[0125] The driving prompt information generated for the current driving scene in this embodiment may be voice prompt information, and the driver is guided to control the current vehicle in the current driving environment according to the optimal driving strategy based on the voice prompt information.

[0126] As can be seen from the above, in this embodiment, after obtaining the current vehicle's historical driving data, the vehicle's historical driving patterns under various pre-set driving scenarios can be obtained. A path optimization model based on an improved skill optimization algorithm is then used to determine the optimal driving pattern for each pre-set driving scenario. By comparing each historical driving pattern with the optimal driving pattern, the driver's historical driving ability for each pre-set driving pattern is determined. This historical driving ability is then used to predict the driver's current driving ability for each pre-set driving pattern. Under the current driving environment, the path optimization model can be used to generate the optimal driving strategy for the current driving environment. Based on the driver's current driving ability, a determination is made as to whether driving prompts should be generated based on the optimal driving strategy. This reduces the amount of driving prompts, minimizes driver interference, and ultimately improves vehicle safety.

[0127] In some embodiments of the present invention, in the above step S115, the method for predicting the driver's current driving ability in each preset driving mode based on the driver's historical driving ability in each preset driving mode is as follows: Figure 4 As shown, the following steps are included:

[0128] Step S201: Counting the number of occurrences of each preset driving scenario in each historical driving data, and obtaining a correction coefficient for each preset driving mode according to the number of occurrences of each preset driving scenario;

[0129] Step S202: constructing a driving ability change curve of the driver in each preset driving mode based on the driver's historical driving ability in each historical time period;

[0130] Step S203: predicting the driver's current driving ability in each preset driving mode based on each driving ability change curve, and correcting the corresponding current driving ability using each correction coefficient.

[0131] In the above step S201, it is assumed that there are H historical time periods, and in the hth historical time period, the number of times the i-th type of preset driving scene occurs is G. h.i , then the average occurrence frequency of the i-th type of preset driving scene is for:

[0132]

[0133] Assume that the correlation between the i-th type of preset driving scene and the j-th type of preset driving mode is U i.j , then the correction coefficient d of the jth type of preset driving style j for

[0134]

[0135] Among them, G0 is the preset standard number of times.

[0136] In the above step S202, taking the jth preset driving mode as an example, assuming that the driver's historical driving ability in the jth preset driving mode in the hth historical time period is FC h.j , a curve fitting can be performed based on the driver's historical driving ability in the jth preset driving mode in each historical time period to obtain the driver's driving ability change curve in the jth preset driving mode.

[0137] In the above step S203, the driving ability of the driver in each preset driving mode in the current time period can be obtained based on the driving ability change curve of the driver in each preset driving mode, thereby obtaining the current driving ability of the driver in each preset driving mode, and then the correction coefficient of the preset driving mode is used to correct the current driving ability.

[0138] For example, suppose the current driving ability of the driver in the jth type of preset driving style is obtained as FC through the driving ability change curve 0,j , then after correction, the current driving ability is FC 0,j ×d j .

[0139] In this embodiment, the current driving ability of each type of preset driving mode can be corrected according to the number of occurrences of each type of preset driving scenario in the historical driving data, thereby improving the accuracy of the obtained current driving ability of the driver.

[0140] In some embodiments of the present invention, Figure 5 As shown, after determining in the above step S123 whether the driver's current driving ability in each preset driving mode is greater than the corresponding minimum driving ability required by the current driving environment, the following steps are further included:

[0141] If the driver's current driving ability in each preset driving mode is greater than the corresponding minimum driving ability required by the current driving environment, step S125 is executed;

[0142] Step S125: Generate simplified driving prompt information for the current driving scene based on the optimal driving strategy for the current driving scene.

[0143] In this embodiment, driving prompt information for the current driving scenario can be generated based on the optimal driving strategy for the current driving scenario; then the driving prompt information is divided into multiple prompt information segments, and finally keywords in each prompt information segment are extracted, and corresponding simplified prompt information is generated based on each keyword to obtain simplified driving prompt information.

[0144] For example, if the optimal driving strategy for the current driving scenario requires a left lane change at a certain location, then when generating driving prompt information for the current driving scenario, one of the prompt information segments includes a "Change lanes left after 50 meters ahead" prompt 50 meters before the location, a "Change lanes left after 30 meters ahead" prompt 30 meters before the location, and a "Change lanes left" prompt after reaching the location. After simplification, the simplified version of the prompt information is a "Change lanes left after 30 meters ahead" prompt 30 meters before the location, and only one prompt is required.

[0145] The technical solution of this embodiment can generate a simplified prompt information based on the optimal driving strategy for the current driving scenario when the driver's current driving ability in each preset driving mode is greater than the corresponding minimum driving ability required by the current driving environment. This not only provides the driver with safe driving guidance, but also avoids frequent prompts that affect the driver's driving safety.

[0146] In some embodiments of the present invention, Figure 5 As shown, after generating driving prompt information of the current driving scene according to the optimal driving strategy of the current driving environment in the above step S124, the following steps are further included:

[0147] Step S126: Simplifying the generated driving prompt information according to the driver's current driving ability in each preset driving mode.

[0148] In this embodiment, the driving prompt information may be first divided into a plurality of prompt information segments, and then each prompt information segment may be traversed to determine whether each prompt information segment needs to be simplified; if so, the prompt information segment may be simplified.

[0149] Taking one of the prompt information segments as an example, the method for determining whether the prompt information segment needs to be simplified includes:

[0150] Obtaining correlations between the prompt information segment and each preset driving mode, and selecting a set number of preset driving modes with the greatest similarity as preset associated aspects of the prompt information;

[0151] Determining whether the driver's current driving ability in each of the preset associated aspects is greater than a preset ability threshold;

[0152] If so, it is determined that the prompt information segment needs to be simplified.

[0153] In this embodiment, the method for simplifying the prompt information segment includes: extracting keywords from the prompt information segment, and generating corresponding simplified prompt information according to the keywords.

[0154] The technical solution of this embodiment can generate simplified prompt information based on the optimal driving strategy of the current driving scenario in the current driving scenario that the driver's current driving ability can cope with. It can not only provide the driver with safe driving guidance, but also avoid frequent prompts that affect the driver's driving safety.

[0155] In some embodiments of the present invention, after generating driving prompt information for the current driving scenario according to the optimal driving strategy for the current driving environment in step S124, the following steps may be further included:

[0156] Sending a request message to a user terminal to obtain a user instruction from the user terminal;

[0157] Determine whether the acquired user instruction is an instruction to start driving guidance;

[0158] If so, driving prompts are played in the form of voice to guide the driver to control the current vehicle according to the optimal driving strategy for the current driving environment.

[0159] In this embodiment, after receiving the request information, the user terminal may display a button on the screen indicating whether to accept the driving instructions, for the user to select. If the user terminal detects that the user has selected the button to accept the driving instructions, the user instruction to enable the driving instructions is sent to the current vehicle. If the user terminal detects that the user has selected the button to reject the driving instructions, or if no user selection is detected for a long period of time, the user instruction to disallow the activation of the driving instructions is sent to the current vehicle.

[0160] The technical solution of this embodiment adds a link of interaction with the user after generating the driving prompt information of the current car, so that the user can independently choose whether to accept the driving guidance, thereby further improving the user experience.

[0161] The flowcharts provided in this embodiment are not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in all cases. In addition, each of the above methods may include additional operations. Additional changes may be made to the above method within the scope of the technical ideas provided by the method of this embodiment.

[0162] It should be understood that in some embodiments, each part can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0163] This embodiment also provides a computer program product 10 , a computer-readable storage medium 20 , and a computer device 30 . Figure 6 is a schematic diagram of a computer program product 10 according to one embodiment of the present invention, Figure 7is a schematic diagram of a computer-readable storage medium 20 according to one embodiment of the present invention, Figure 8 is a schematic diagram of a computer device 30 according to one embodiment of the present invention. A computer program product 10 includes a computer program 11. When executed by a processor 32, this computer program 11 implements the steps of any of the aforementioned methods for analyzing driving data for a connected vehicle. A computer-readable storage medium 20 stores the computer program 11. When executed by the processor 32, this computer program 11 implements the steps of any of the aforementioned methods for analyzing driving data for a connected vehicle. The computer device 30 may include a memory 31, a processor 32, and the computer program 11 stored in the memory 31 and executed by the processor 32.

[0164] The computer program 11 for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source code or object code written in any combination of one or more programming languages and procedural programming languages. The computer program 11 may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform various aspects of the present invention, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuit.

[0165] In the description of this embodiment, the computer program product 10 is a related product including the computer program 11 .

[0166] For the purposes of the description of this embodiment, the computer-readable storage medium 20 is a tangible device capable of retaining and storing the computer program 11, and can be any device that can contain, store, communicate, propagate, or use the computer program 11 for an instruction execution system, apparatus, or device, or in conjunction with such an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable storage medium 20 include the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination of the foregoing.

[0167] The computer device 30 can be, for example, a server, a desktop computer, a laptop computer, a tablet computer, or a smartphone. In some examples, the computer device 30 can be a cloud computing node. The computer device 30 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc. that perform specific tasks or implement specific abstract data types. The computer device 30 can be implemented in a distributed cloud computing environment where remote processing devices linked via a communication network perform tasks. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media, including storage devices.

[0168] The computer device 30 may include a processor 32 adapted to execute stored instructions, and a memory 31 that provides temporary storage for the instructions during operation. The processor 32 may be a single-core processor, a multi-core processor, a computing cluster, or any number of other configurations. The memory 31 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0169] The computer device 30 may also include a network adapter / interface and an input / output (I / O) interface. The I / O interface allows data to be input and output with external devices that can be connected to the computer device. The network adapter / interface can provide communication between the computer device and a network, which is generally shown as a communication network.

[0170] At this point, those skilled in the art will recognize that, although a number of exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications consistent with the principles of the present invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and deemed to cover all such other variations or modifications.

Claims

1. A method for analyzing driving data of an intelligent networked car, characterized in that: include: The steps of predicting the current driving ability of the driver based on the historical driving data of the current car, and generating driving prompt information based on the current driving ability; The step of predicting the driver's current driving ability based on the historical driving data of the current vehicle includes: Acquire historical driving data of the current vehicle in a plurality of consecutive historical time periods, and respectively acquire historical driving modes of the current vehicle in each preset driving scenario from each of the historical driving data; Obtaining a preset driving cost function, and using a path optimization model based on an improved skill optimization algorithm to obtain an optimal driving mode for the preset driving scenario according to the preset driving cost function; By comparing each of the historical driving modes with the optimal driving mode, the driver's historical driving ability in a plurality of preset driving modes during each of the historical time periods is obtained, and the driver's current driving ability in each of the preset driving modes is predicted based on each of the historical driving abilities; The step of generating driving prompt information according to the current driving ability includes: Obtaining a current driving environment via the Internet, generating an optimal driving strategy for the current driving environment using the path optimization model, and determining minimum driving capabilities required by each of the preset driving modes for the current driving environment based on the optimal driving strategy; Determining whether each of the current driving capabilities is greater than a corresponding minimum driving capability; If not, the driving prompt information is generated according to the optimal driving strategy.

2. The method for analyzing driving data of an intelligent networked vehicle according to claim 1, characterized in that: The step of predicting the driver's current driving ability in each of the preset driving modes based on each of the historical driving abilities comprises: Obtaining a correction coefficient for each of the preset driving modes based on the frequency of occurrence of each of the preset driving scenarios in each of the historical driving data; constructing driving ability change curves for each of the preset driving modes based on the historical driving abilities; The current driving abilities are predicted according to the driving ability change curves, and the correction coefficients are used to correct the corresponding current driving abilities.

3. The method for analyzing driving data of an intelligent networked vehicle according to claim 1, characterized in that: After the step of determining whether each of the current driving capabilities is greater than the corresponding minimum driving capability, the method further includes: If each of the current driving capabilities is greater than the corresponding minimum driving capabilities, simplified driving prompt information is generated according to the optimal driving strategy.

4. The method for analyzing driving data of an intelligent networked vehicle according to claim 1, wherein: After the step of generating the driving prompt information according to the optimal driving strategy, the method further includes: The driving prompt information is simplified according to the current driving ability.

5. The method for analyzing driving data of an intelligent networked vehicle according to claim 1, wherein: After the step of generating the driving prompt information according to the optimal driving strategy, the method further includes: Sending a request message to a user terminal to obtain a user instruction from the user terminal; When the user instruction is to allow the driving guidance to be turned on, the driving prompt information is played in the form of voice.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the driving data analysis method for an intelligent networked vehicle according to any one of claims 1 to 5 are implemented.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the driving data analysis method for an intelligent networked vehicle according to any one of claims 1 to 5 are implemented.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the driving data analysis method for an intelligent networked vehicle according to any one of claims 1 to 5.

Images