Dynamic updating method and device for safe distance model, equipment, medium and vehicle

Abstract

The embodiment of the invention provides a dynamic updating method and device for a safety distance model, equipment, a medium and a vehicle, and relates to the technical field of safety monitoring. The method comprises the following steps: determining that a longitudinal safe distance calculation model meets a preset updating condition, and obtaining a dynamic estimation result corresponding to at least one to-be-updated parameter in the longitudinal safe distance calculation model; wherein the at least one to-be-updated parameter comprises at least one of driver response time, deceleration of a front vehicle and maximum deceleration of an own vehicle; and updating the longitudinal safe distance calculation model based on the dynamic estimation result corresponding to each to-be-updated parameter to obtain a dynamically optimized longitudinal safe distance calculation model. According to the embodiment of the invention, the updating condition is set, and the dynamic estimation result of the at least one parameter of the model is obtained when the condition is met to update the parameters of the model, so that the calculation model can adapt to a complex driving scene, and the accuracy of calculating the longitudinal safe distance is improved.

Description

Technical Field

[0001] The present application relates to the field of security monitoring technology, and in particular to a method, device, equipment, medium and vehicle for dynamically updating a safety distance model. Background Art

[0002] Longitudinal safety distance is an important indicator to measure the distance between the front and rear of a vehicle. Accurately obtaining the longitudinal safety distance during driving is a prerequisite for guiding the driver to drive safely and achieving safe autonomous driving.

[0003] At present, traditional solutions for calculating longitudinal safety distance usually rely on preset fixed parameters. This solution is difficult to adapt to the complex situations in actual driving scenarios, resulting in low accuracy of the calculated longitudinal safety distance, which affects the safety and performance of vehicle driving. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device, equipment, medium and vehicle for dynamically updating a safety distance model, so as to improve the accuracy of calculating the longitudinal safety distance.

[0005] In a first aspect, an embodiment of the present application provides a method for dynamically updating a safety distance model, comprising:

[0006] Determining that the longitudinal safety distance calculation model satisfies a preset update condition, and obtaining a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model; wherein the at least one parameter to be updated includes at least one of a driver's reaction time, a preceding vehicle deceleration, and a maximum deceleration of the own vehicle;

[0007] The longitudinal safety distance calculation model is updated based on the dynamic estimation results corresponding to the parameters to be updated to obtain a dynamically optimized longitudinal safety distance calculation model.

[0008] In an embodiment of the present application, the model parameters are updated by setting update conditions and obtaining a dynamic estimation result of at least one parameter of the model when the conditions are met, so that the calculation model can adapt to complex driving scenarios, thereby improving the accuracy of calculating the longitudinal safety distance.

[0009] In some possible embodiments, the obtaining a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model includes:

[0010] Acquire a current first feature value set corresponding to at least one preset reaction time influencing factor; wherein the at least one reaction time influencing factor includes at least one of the driver's age, driving mileage, and fatigue level;

[0011] Based on the first eigenvalue set, a fuzzy theory method is used to obtain a dynamic estimation result corresponding to the driver's reaction time in the longitudinal safety distance calculation model.

[0012] In the embodiment of the present application, the accuracy of calculating the longitudinal safety distance is improved by obtaining corresponding characteristic values ​​according to the influencing factors corresponding to the reaction time, and adaptively updating the driver reaction time parameters according to the characteristic values ​​by using the fuzzy theory method.

[0013] In some possible embodiments, the obtaining a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model includes:

[0014] Acquire a current second feature value set corresponding to at least one preset deceleration influencing factor of the preceding vehicle; wherein the at least one deceleration influencing factor of the preceding vehicle includes at least one of vehicle speed, vehicle flow, vehicle flow density, visibility, weather, lighting, road surface condition, and time period type;

[0015] Based on the second eigenvalue set, a fuzzy theory method is used to obtain a dynamic estimation result corresponding to the deceleration of the leading vehicle in the longitudinal safety distance calculation model.

[0016] In the embodiment of the present application, the accuracy of calculating the longitudinal safety distance is improved by obtaining corresponding characteristic values ​​according to the influencing factors corresponding to the deceleration of the leading vehicle, and adaptively updating the deceleration parameters of the leading vehicle according to the characteristic values ​​by using the fuzzy theory method.

[0017] In some possible embodiments, the obtaining a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model includes:

[0018] Acquire a current third eigenvalue set corresponding to at least one preset vehicle deceleration influencing factor; wherein the at least one vehicle deceleration influencing factor includes at least one of a road surface condition and a tire pressure;

[0019] Based on a preset correspondence table between eigenvalues ​​and deceleration coefficients, a current deceleration coefficient corresponding to the third eigenvalue set is obtained, and based on the current deceleration coefficient, a dynamic estimation result corresponding to the maximum deceleration of the vehicle in the longitudinal safety distance calculation model is obtained.

[0020] In the embodiment of the present application, the accuracy of calculating the longitudinal safety distance is improved by obtaining corresponding characteristic values ​​according to the influencing factors corresponding to the deceleration of the own vehicle, and adaptively updating the maximum deceleration parameter of the own vehicle according to the characteristic values ​​by using a table lookup method.

[0021] In some possible embodiments, the method of obtaining a dynamic estimation result corresponding to the driver reaction time in the longitudinal safety distance calculation model based on the first eigenvalue set by using a fuzzy theory method includes:

[0022] Based on the first eigenvalue set, respectively obtaining fuzzy subsets corresponding to each reaction time influencing factor;

[0023] Determine the corresponding reaction time fuzzy quantity based on the fuzzy subsets of each reaction time influencing factor and the preset reaction time fuzzy rule control table;

[0024] The reaction time fuzzy quantity is defuzzified based on a preset reaction time membership function to obtain a dynamic estimation result corresponding to the driver's reaction time in the longitudinal safety distance calculation model.

[0025] In an embodiment of the present application, a corresponding fuzzy subset is obtained according to the characteristic value corresponding to the reaction time, and then a fuzzy quantity of the reaction time is obtained according to the fuzzy subset and the fuzzy rule. Finally, the fuzzy quantity of the reaction time is defuzzified to obtain a dynamically estimated reaction time parameter, thereby further improving the accuracy of calculating the longitudinal safety distance.

[0026] In some possible embodiments, the obtaining, based on the second eigenvalue set, a dynamic estimation result corresponding to the preceding vehicle deceleration in the longitudinal safety distance calculation model by using a fuzzy theory method includes:

[0027] Based on the second eigenvalue set, respectively obtaining fuzzy subsets corresponding to the deceleration influencing factors of each preceding vehicle;

[0028] Based on the fuzzy subsets of the factors affecting the deceleration of the preceding vehicle and the preset fuzzy rule base set, the initial fuzzy quantities corresponding to the estimation dimensions are respectively obtained; wherein the fuzzy rule base set includes a traffic condition fuzzy rule base, an environmental condition fuzzy rule base and a traffic demand fuzzy rule base; the initial fuzzy quantities corresponding to the estimation dimensions include a traffic condition fuzzy quantity, an environmental condition fuzzy quantity and a traffic demand fuzzy quantity;

[0029] Determine the corresponding fuzzy amount of the preceding vehicle deceleration based on the initial fuzzy amount corresponding to each estimation dimension and a preset preceding vehicle deceleration fuzzy rule control table;

[0030] The fuzzy value of the preceding vehicle deceleration is defuzzified based on a preset preceding vehicle deceleration membership function to obtain a dynamic estimation result corresponding to the preceding vehicle deceleration in the longitudinal safety distance calculation model.

[0031] In an embodiment of the present application, the corresponding initial fuzzy quantities are obtained according to fuzzy subsets of different influencing factors and fuzzy rule libraries of different estimation dimensions, and then the fuzzy quantity of the deceleration parameter of the preceding vehicle is obtained according to the initial fuzzy quantity and the deceleration fuzzy rule. Finally, the fuzzy quantity of the deceleration parameter of the preceding vehicle is defuzzified to obtain the dynamically estimated deceleration parameter of the preceding vehicle, thereby further improving the accuracy of calculating the longitudinal safety distance.

[0032] In some possible embodiments, the obtaining, based on the second eigenvalue set, a dynamic estimation result corresponding to the preceding vehicle deceleration in the longitudinal safety distance calculation model by using a fuzzy theory method includes:

[0033] Based on the second eigenvalue set, respectively obtaining fuzzy subsets corresponding to the deceleration influencing factors of each preceding vehicle;

[0034] Determine the corresponding fuzzy value of the preceding vehicle deceleration based on the fuzzy subsets of the influencing factors of the preceding vehicle deceleration and the preset preceding vehicle deceleration fuzzy rule control table;

[0035] The fuzzy value of the preceding vehicle deceleration is defuzzified based on a preset preceding vehicle deceleration membership function to obtain a dynamic estimation result corresponding to the preceding vehicle deceleration in the longitudinal safety distance calculation model.

[0036] In an embodiment of the present application, a corresponding fuzzy subset is obtained according to the characteristic value corresponding to the deceleration of the leading vehicle, and then the fuzzy amount of the leading vehicle deceleration is directly obtained according to the fuzzy subset and the fuzzy rule, and finally the fuzzy amount of the leading vehicle deceleration is defuzzified to obtain the dynamically estimated deceleration parameter of the leading vehicle, thereby further improving the accuracy of calculating the longitudinal safety distance.

[0037] In some possible embodiments, determining that the longitudinal safety distance calculation model meets a preset update condition includes:

[0038] The accumulated time from the historical moment when the longitudinal safety distance calculation model was last updated to the current moment is obtained in real time. When it is determined that the accumulated time exceeds the preset model update cycle, it is determined that the longitudinal safety distance calculation model meets the preset update conditions.

[0039] In the embodiment of the present application, by updating the safety distance calculation model according to a set period, the parameters of the calculation model can be adaptively adjusted in a timely and regular manner, thereby improving the accuracy of calculating the longitudinal safety distance.

[0040] In some possible embodiments, determining that the longitudinal safety distance calculation model meets a preset update condition includes:

[0041] Determine the key influencing factors corresponding to the at least one parameter to be updated, and obtain the characteristic value index of each key influencing factor in real time;

[0042] When it is determined that the characteristic value index of the target key influencing factor meets the corresponding preset conditions, it is determined that the longitudinal safety distance calculation model meets the preset update conditions;

[0043] The target key influencing factor is one of the key influencing factors; and the characteristic value indicator for judging whether the target key influencing factor meets the corresponding preset condition includes at least one of the following:

[0044] If it is determined that the current characteristic value of the target key influencing factor exceeds the preset threshold range, then it is determined that the characteristic value index of the target key influencing factor meets the corresponding preset condition;

[0045] If it is determined that the characteristic value change per unit time of the target key influencing factor exceeds a preset change value threshold, it is determined that the characteristic value index of the target key influencing factor meets the corresponding preset condition.

[0046] In an embodiment of the present application, by judging whether the model update conditions are met based on changes in characteristic indicators of key influencing factors, the parameters of the calculation model can be adaptively adjusted in a timely manner in response to changes in the characteristics of key factors, thereby improving the accuracy of calculating the longitudinal safety distance.

[0047] In some possible embodiments, the obtaining a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model includes:

[0048] The parameter to be updated corresponding to the target key influencing factor is determined as the at least one parameter to be updated, and a dynamic estimation result corresponding to the at least one parameter to be updated in the longitudinal safety distance calculation model is obtained.

[0049] In an embodiment of the present application, by monitoring the indicator changes of key influencing factors and determining the parameters corresponding to the target key influencing factors that meet the conditions as the parameters that currently need to be updated, the flexibility and efficiency of updating the longitudinal safety distance model are further improved.

[0050] In a second aspect, an embodiment of the present application provides a device for dynamically updating a safety distance model, including:

[0051] A parameter dynamic estimation module, used to determine whether the longitudinal safety distance calculation model meets the preset update condition, and obtain a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model; wherein the at least one parameter to be updated includes at least one of the driver's reaction time, the deceleration of the preceding vehicle, and the maximum deceleration of the own vehicle;

[0052] The calculation model updating module is used to update the longitudinal safety distance calculation model based on the dynamic estimation results corresponding to each parameter to be updated, so as to obtain a dynamically optimized longitudinal safety distance calculation model.

[0053] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement the method described in any embodiment of the first aspect when executing the program.

[0054] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any embodiment of the first aspect can be implemented.

[0055] In a fifth aspect, an embodiment of the present application provides a vehicle, comprising the electronic device described in the third aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A schematic diagram of a flow chart of a method for dynamically updating a safety distance model provided in an embodiment of the present application;

[0058] Figure 2 A schematic diagram of a process for estimating a driver's reaction time based on fuzzy theory provided in an embodiment of the present application;

[0059] Figure 3 A schematic diagram of a process for estimating the deceleration of a preceding vehicle based on fuzzy theory provided in an embodiment of the present application;

[0060] Figure 4 A schematic diagram of the structure of a dynamic updating device for a safety distance model provided in an embodiment of the present application;

[0061] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0063] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0064] It should be noted that in the field of driving, especially in the field of autonomous driving, safety monitoring is an important function to ensure vehicle driving safety. Among them, the longitudinal safety distance is one of the most important indicators in the safety monitoring function. By reasonably calculating the longitudinal safety distance, the driver is guided or the vehicle is automatically controlled to maintain an appropriate safety distance from the vehicle in front. In the process of calculating the longitudinal safety distance, if the calculation result of this indicator is too large, the risk based on the safety distance estimate will be too conservative, affecting the normal operation of the vehicle; if the calculation result of this indicator is too small, the risk based on the safety distance will be underestimated, increasing the risk of rear-end collisions and other collisions.

[0065] Generally, the longitudinal safety distance can be calculated using the following formula (longitudinal safety distance calculation model):

[0066]

[0067] Among them, v1 represents the speed of the main vehicle (ego vehicle), v2 represents the speed of the front vehicle, ρ represents the driver's reaction time, and a 1accel represents the real-time acceleration of the vehicle, a 1max,decel represents the maximum deceleration of the vehicle, a 2max,decel Indicates the braking deceleration of the vehicle ahead.

[0068] For the three parameters of driver reaction time, front vehicle deceleration and vehicle maximum deceleration in the above longitudinal safety distance calculation model, existing solutions usually use preset fixed parameters, which makes it difficult to adapt to complex situations in actual driving scenarios, resulting in low accuracy of the calculated longitudinal safety distance, thus affecting the safety and performance of vehicle driving.

[0069] In response to the problems existing in the above-mentioned prior art, an embodiment of the present application provides a method for dynamically updating a safety distance model. By setting update conditions and dynamically updating the parameters in the longitudinal safety distance calculation model when the conditions are met, the dynamically optimized calculation model can adapt to complex driving scenarios, thereby improving the accuracy of calculating the longitudinal safety distance, thereby ensuring that the vehicle can maintain a high level of safety and driving performance.

[0070] like Figure 1 As shown, the embodiment of the present application provides a method for dynamically updating a safety distance model, which may include the steps of:

[0071] S1. Determine whether the longitudinal safety distance calculation model satisfies a preset update condition, and obtain a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model; wherein the at least one parameter to be updated includes at least one of the driver's reaction time, the deceleration of the preceding vehicle, and the maximum deceleration of the own vehicle.

[0072] It should be noted that updating the calculation model and using the model to calculate the longitudinal safety distance can use two different threads. In this way, on the one hand, the calculation model can be updated in time according to needs, and on the other hand, the most recently updated model can be used to calculate the longitudinal safety distance.

[0073] When it is detected that the longitudinal safety distance calculation model needs to be updated, one or more parameters to be updated in the longitudinal safety distance calculation model may be updated, wherein the parameters to be updated may include three parameters: driver reaction time, front vehicle deceleration, and self-vehicle maximum deceleration.

[0074] S2. The longitudinal safety distance calculation model is updated based on the dynamic estimation results corresponding to the parameters to be updated to obtain a dynamically optimized longitudinal safety distance calculation model.

[0075] After the dynamic estimation results corresponding to the parameters to be updated are calculated, the corresponding parameters in the longitudinal safety distance calculation model may be replaced with the dynamic estimation results of the corresponding parameters currently calculated to obtain an updated longitudinal safety distance calculation model.

[0076] Based on this, by adaptively and dynamically updating the parameters in the longitudinal safety distance calculation model, the updated calculation model can adapt to complex driving scenarios, thereby effectively improving the accuracy of calculating the longitudinal safety distance.

[0077] In some possible embodiments, in step S1, obtaining a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model may include:

[0078] S101, obtaining a current first feature value set corresponding to at least one preset reaction time influencing factor; wherein the at least one reaction time influencing factor includes at least one of the driver's age, driving mileage, and fatigue level;

[0079] S102: Based on the first eigenvalue set, a fuzzy theory method is used to obtain a dynamic estimation result corresponding to the driver's reaction time in the longitudinal safety distance calculation model.

[0080] Furthermore, step S102, based on the first eigenvalue set, using a fuzzy theory method to obtain a dynamic estimation result corresponding to the driver's reaction time in the longitudinal safety distance calculation model, may include:

[0081] S1021, based on the first eigenvalue set, respectively obtaining fuzzy subsets corresponding to each reaction time influencing factor;

[0082] S1022, determining the corresponding reaction time fuzzy quantity based on the fuzzy subsets of each reaction time influencing factor and a preset reaction time fuzzy rule control table;

[0083] S1023. Defuzzify the reaction time fuzzy quantity based on a preset reaction time membership function to obtain a dynamic estimation result corresponding to the driver's reaction time in the longitudinal safety distance calculation model.

[0084] It should be noted that for the parameter of driver reaction time, fuzzy theory can be used to obtain the corresponding parameter estimation value. The working process of the estimation strategy based on fuzzy theory can be as follows: Figure 2 As shown. Considering that the factors affecting the driver's reaction time mainly include the driver's age, driving mileage and fatigue level, three reaction time influencing factors can be set, and the respective membership functions can be defined according to each reaction time influencing factor.

[0085] 1. For the driver's age, since the road traffic laws and regulations stipulate that the driver's age is between 18 and 70 years old, the value range of the driver's age domain is set to [18,70]. For example, the driver's age can be divided into three fuzzy subsets: young N1, middle-aged N2 and old N3. Based on the trapezoidal membership function, the membership functions of the fuzzy subsets N1, N2 and N3 are set to n1, n2 and n3 respectively:

[0086]

[0087] Among them, 18≤x≤70, and the value range of n1, n2 and n3 is [0,1].

[0088] 2. For driving mileage, the driver's driving mileage starts from 0 kilometers, so the value range of its domain is set to [0, +∞). For example, the driving mileage can also be divided into three fuzzy subsets: short mileage S1, medium mileage S2 and long mileage S3. Based on the trapezoidal membership function, the membership functions of the fuzzy subsets S1, S2 and S3 are set to s1, s2 and s3 respectively. In order to simplify the calculation, the mileage is in "10,000 kilometers". The above three membership functions are expressed as follows:

[0089]

[0090] Among them, 0≤x<+∞, the value range of s1, s2 and s3 is [0,1].

[0091] 3. For fatigue degree, the value range of its domain can be set to [0,2] by way of example. For example, fatigue degree can be divided into three fuzzy subsets: mild fatigue P1, moderate fatigue P2 and severe fatigue P3. Since fatigue degree changes nonlinearly, the trapezoidal membership function can no longer be simply used. The membership functions of fuzzy subsets P1, P2 and P3 can be set to p1, p2 and p3 based on the Gaussian membership function:

[0092]

[0093] The value range of x is [0,2], the value range of p1, p2 and p3 are all (0,1]; the value of σ can be set to 0.3 based on experience.

[0094] It should be noted that the number of fuzzy subsets corresponding to the above-mentioned influencing factors can be set according to demand. It is not difficult to understand that the more the number of fuzzy subset divisions is, the more accurate the driver reaction time calculated based on fuzzy theory will be.

[0095] In the process of estimating the driver's reaction time, the eigenvalue set corresponding to each reaction time factor is first obtained, where one reaction time influencing factor corresponds to one eigenvalue; then the eigenvalue is substituted into the corresponding membership function mentioned above, and the fuzzy subset corresponding to each reaction time influencing factor is calculated respectively (the fuzzy subset corresponding to the membership result with the largest value is taken as the fuzzy subset result).

[0096] Then, the corresponding fuzzy quantity of reaction time can be determined according to the fuzzy subset results and the preset fuzzy rule control table. Among them, the fuzzy rule control table can be formulated according to the influence of the driver's age, driving mileage and fatigue level on the driver's reaction time. It can be understood that the correspondence between each influencing factor and the reaction time in the fuzzy rule control table follows the following principles: the older the age, the longer the reaction time; the more driving mileage, the richer the experience, the shorter the reaction time; the higher the fatigue level, the longer the reaction time. Based on this, the fuzzy rule control table can be set as shown in the following table:

[0097]

[0098]

[0099] The reaction time T can be divided into four fuzzy subsets: fast T1, relatively fast T2, relatively slow T3 and slow T4. The specific number of fuzzy subsets and the corresponding relationship of fuzzy rules can be set according to actual conditions.

[0100] It should be noted that after determining the reaction time fuzzy value, it is necessary to further defuzzify the fuzzy value and obtain an accurate driver reaction time value.

[0101] For example, for driver reaction time, according to the results of literature search, the driver's reaction time is generally between 0.4 seconds and 2.2 seconds, so the domain value range of the driver's reaction time can be set to [0.4, 2.2]. For example, the reaction time fuzzy quantity can be divided into four fuzzy subsets: fast T1, fast T2, slow T3 and slow T4. Due to the nonlinearity of the driver's reaction time result, the membership function of its fuzzy subset also adopts the Gaussian membership function. The corresponding membership functions of the above four fuzzy subsets are set to t1, t2, t3 and t4 respectively:

[0102]

[0103] Where, the value range of x is [0.4, 2.2], the value range of t1, t2, t3 and t4 are all (0, 1]; the value of σ can be set to 0.2 based on experience.

[0104] According to the currently determined reaction time fuzzy quantity, the membership function corresponding to the fuzzy subset can be determined as the target membership function for defuzzification. For example, the centroid method can be used for defuzzification to obtain the final accurate value of the driver's reaction time, and the formula is as follows:

[0105]

[0106] Among them, t(x) is the membership function corresponding to the currently determined fuzzy value of reaction time, and T is the precise value of the driver's reaction time after defuzzification.

[0107] In some possible embodiments, in step S1, obtaining a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model may include:

[0108] S111, obtaining a current second feature value set corresponding to at least one preset deceleration influencing factor of the preceding vehicle; wherein the at least one deceleration influencing factor of the preceding vehicle includes at least one of vehicle speed, vehicle flow, vehicle flow density, visibility, weather, lighting, road surface condition, and time period type;

[0109] S112. Based on the second eigenvalue set, a fuzzy theory method is used to obtain a dynamic estimation result corresponding to the deceleration of the preceding vehicle in the longitudinal safety distance calculation model.

[0110] Furthermore, step S112, based on the second eigenvalue set, using a fuzzy theory method to obtain a dynamic estimation result corresponding to the front vehicle deceleration in the longitudinal safety distance calculation model, may include:

[0111] S112-01, based on the second eigenvalue set, respectively obtaining fuzzy subsets corresponding to the deceleration influencing factors of each preceding vehicle;

[0112] S112-02, based on the fuzzy subsets of the influencing factors of the deceleration of each preceding vehicle and the preset fuzzy rule base set, respectively obtain the initial fuzzy quantity corresponding to each estimation dimension; wherein the fuzzy rule base set includes a traffic condition fuzzy rule base, an environmental condition fuzzy rule base and a traffic demand fuzzy rule base; the initial fuzzy quantity corresponding to each estimation dimension includes a traffic condition fuzzy quantity, an environmental condition fuzzy quantity and a traffic demand fuzzy quantity;

[0113] S112-03, determining the corresponding fuzzy amount of the preceding vehicle deceleration based on the initial fuzzy amount corresponding to each estimation dimension and a preset preceding vehicle deceleration fuzzy rule control table;

[0114] S112-04. Defuzzify the fuzzy value of the preceding vehicle deceleration based on a preset preceding vehicle deceleration membership function to obtain a dynamic estimation result corresponding to the preceding vehicle deceleration in the longitudinal safety distance calculation model.

[0115] It should be noted that for the parameter of the preceding vehicle deceleration, the corresponding parameter estimation value can also be obtained by using a fuzzy theory-based method. The working process of the estimation strategy based on fuzzy theory is similar to that of Figure 2 The principle shown is similar. Considering that the factors affecting the deceleration of the preceding vehicle mainly include vehicle speed, traffic volume, traffic density, visibility, weather, lighting, road surface conditions and time period type, fuzzy subsets can be set based on the above factors, and their respective membership functions can be defined according to the characteristics of each influencing factor.

[0116] See also Figure 3 It should be noted that, as an optional method, the factors affecting the deceleration of the preceding vehicle can be divided into three dimensions: traffic conditions, environmental conditions, and traffic demand. Among them, the factors affecting the traffic conditions dimension include vehicle speed, traffic volume, and traffic density; the factors affecting the environmental conditions dimension include visibility, weather, lighting, and road conditions; and the traffic demand includes three time period factors: the hour period of the day, the week period of the week, and the workday / holiday type.

[0117] By obtaining the characteristic values ​​corresponding to the factors affecting the deceleration of the preceding vehicle and substituting them into the corresponding membership functions, the fuzzy subset results corresponding to the factors can be determined based on the maximum value of the membership function calculation results.

[0118] Then, the initial fuzzy quantity corresponding to each estimation dimension is obtained based on the fuzzy subsets of the factors affecting the deceleration of the preceding vehicle and the preset fuzzy rule base set, wherein the fuzzy rule base set includes a traffic condition fuzzy rule base, an environmental condition fuzzy rule base and a traffic demand fuzzy rule base.

[0119] Among them, for the traffic condition dimension, based on the fuzzy subset results corresponding to the three factors of vehicle speed, traffic flow and traffic density, and according to the preset traffic condition fuzzy rule library, the corresponding traffic condition fuzzy quantity can be obtained; similarly, for the environmental condition dimension, based on the fuzzy subset results corresponding to the four factors of visibility, weather, lighting, and road condition, and according to the preset environmental condition fuzzy rule library, the corresponding environmental condition fuzzy quantity can be obtained; similarly, for the traffic demand dimension, based on the fuzzy subset results corresponding to the three time period type factors of hour period of the day, week period of the week, and weekday / holiday type, and according to the preset traffic demand fuzzy rule library, the corresponding traffic demand fuzzy quantity can be obtained.

[0120] Then, the corresponding fuzzy quantity of the preceding vehicle deceleration is obtained by matching the initial fuzzy quantity corresponding to each estimation dimension and the preset fuzzy rule control table of the preceding vehicle deceleration. Finally, the fuzzy quantity of the preceding vehicle deceleration is defuzzified based on the preset membership function of the preceding vehicle deceleration, and the dynamic estimation result corresponding to the preceding vehicle deceleration in the longitudinal safety distance calculation model is obtained.

[0121] Furthermore, step S112, based on the second eigenvalue set, using a fuzzy theory method to obtain a dynamic estimation result corresponding to the front vehicle deceleration in the longitudinal safety distance calculation model, may also include:

[0122] S112-11, based on the second eigenvalue set, respectively obtaining fuzzy subsets corresponding to the deceleration influencing factors of each preceding vehicle;

[0123] S112-12, determining the corresponding fuzzy value of the preceding vehicle deceleration based on the fuzzy subsets of the preceding vehicle deceleration influencing factors and a preset preceding vehicle deceleration fuzzy rule control table;

[0124] S112-13. Defuzzify the fuzzy value of the preceding vehicle deceleration based on a preset preceding vehicle deceleration membership function to obtain a dynamic estimation result corresponding to the preceding vehicle deceleration in the longitudinal safety distance calculation model.

[0125] It should be noted that, in addition to dividing the dimensions and obtaining the estimation result of the deceleration of the preceding vehicle in two stages, the deceleration of the preceding vehicle may also be estimated by steps S112 - 11 to S112 - 13.

[0126] Specifically, first, based on the second eigenvalue set, the fuzzy subset results corresponding to each leading vehicle deceleration influencing factor (such as the ten factors listed above) are obtained; then, the fuzzy amount of the leading vehicle deceleration is directly determined according to these fuzzy subset results and the preset leading vehicle deceleration fuzzy rule control table; then, the fuzzy amount of the leading vehicle deceleration is defuzzified according to the preset leading vehicle deceleration membership function, and the dynamic estimation result corresponding to the leading vehicle deceleration in the longitudinal safety distance calculation model is obtained.

[0127] It should be noted that the type and quantity of the fuzzy subsets of the leading vehicle deceleration influencing factors and the corresponding relationship in the leading vehicle deceleration fuzzy rule control table can be set according to actual needs.

[0128] In some possible embodiments, in step S1, obtaining a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model may include:

[0129] S121, obtaining a third characteristic value set corresponding to at least one preset vehicle deceleration influencing factor; wherein the at least one vehicle deceleration influencing factor includes at least one of a road surface condition and a tire pressure;

[0130] S122. Based on a preset correspondence table between eigenvalues ​​and deceleration coefficients, obtain a current deceleration coefficient corresponding to the third eigenvalue set, and obtain a dynamic estimation result corresponding to the maximum deceleration of the vehicle in the longitudinal safety distance calculation model based on the current deceleration coefficient.

[0131] It should be noted that, for the parameter of the maximum deceleration of the vehicle, since this parameter is mainly affected by the road surface condition, a table lookup method can be used to obtain the maximum deceleration of the vehicle corresponding to the current road surface condition.

[0132] Specifically, firstly, a correspondence table between road surface conditions (factors affecting the deceleration of the vehicle) and deceleration coefficients is configured based on statistical data and experience. Exemplarily, the correspondence table may be shown in the following table:

[0133]

[0134] After obtaining the current third eigenvalue set corresponding to each influencing factor, the corresponding deceleration coefficient (such as adhesion coefficient) can be obtained by looking up the table, and then the estimated value of the maximum deceleration of the vehicle can be obtained according to the product of the adhesion coefficient and the acceleration of gravity.

[0135] Among them, exemplarily, when the weather condition is sunny, the corresponding road surface condition is determined to be dry; when the weather condition is rainy, the corresponding road surface condition is determined to be wet; when the weather condition is snowy and the outdoor temperature is above zero, the corresponding road surface condition is determined to be snowy; when the weather condition is snowy and the outdoor temperature is below zero, the corresponding road surface condition is determined to be icy. In addition, exemplarily, when the monitored tire pressure is less than or equal to a preset threshold, the tire condition is determined to be a low-pressure tire; when the monitored tire pressure is greater than the preset threshold, the tire condition is determined to be a high-pressure tire.

[0136] In some possible embodiments, in step S1, determining whether the longitudinal safety distance calculation model meets a preset update condition may include:

[0137] S131. Obtain in real time the cumulative time from the historical moment when the longitudinal safety distance calculation model was last updated to the current moment. If it is determined that the cumulative time exceeds a preset model update cycle, determine that the longitudinal safety distance calculation model meets a preset update condition.

[0138] It should be noted that, in the process of determining whether the longitudinal safety distance calculation model satisfies the preset update condition, the preset model update cycle may be used as a basis for determination.

[0139] Specifically, each time the model is updated, the time of updating the model is recorded, and then the cumulative time from the current time to the historical time when the model was last updated is counted in real time. When the cumulative time exceeds the preset model update cycle, it is determined that the longitudinal safety distance calculation model meets the preset update conditions.

[0140] Based on this, by updating the safety distance calculation model according to a set period, various parameters of the calculation model can be adaptively adjusted in a timely and regular manner, thereby improving the accuracy of calculating the longitudinal safety distance.

[0141] In some possible embodiments, in step S1, determining whether the longitudinal safety distance calculation model meets a preset update condition may include:

[0142] S141, determining a key influencing factor corresponding to at least one parameter to be updated, and obtaining a characteristic value index of each key influencing factor in real time;

[0143] S142, when it is determined that the characteristic value index of the target key influencing factor meets the corresponding preset condition, determining that the longitudinal safety distance calculation model meets the preset update condition;

[0144] The target key influencing factor is one of the key influencing factors; judging whether the characteristic value index of the target key influencing factor meets the corresponding preset condition includes at least one of the following:

[0145] 1. If the current characteristic value of the target key influencing factor exceeds the preset threshold range, the characteristic value index of the target key influencing factor is judged to meet the corresponding preset condition;

[0146] 2. If the change in the characteristic value per unit time of the target key influencing factor exceeds a preset change threshold, it is determined that the characteristic value index of the target key influencing factor meets the corresponding preset conditions.

[0147] It should be noted that, in the process of judging whether the longitudinal safety distance calculation model satisfies the preset update condition, the key influencing factors corresponding to the parameters to be updated may also be used as a basis for judgment.

[0148] Specifically, for driver reaction time, since the driver's age and driving mileage change relatively slowly, while the fatigue level changes relatively quickly, the fatigue level can be used as a key influencing factor of the driver's reaction time parameter.

[0149] By monitoring the driver's fatigue level in real time, and then judging whether the characteristic value index of the fatigue level meets the corresponding preset conditions, it is determined whether the longitudinal safety distance calculation model meets the preset update conditions. Among them, it is possible to judge whether the characteristic value index of the fatigue level meets the corresponding preset conditions in the following two ways: 1. When the current characteristic value of the fatigue level exceeds the preset normal threshold range, for example, when it changes to a heavier fatigue level, it is determined that the characteristic value index of the fatigue level meets the corresponding preset conditions; 2. When the change in the characteristic value per unit time of the fatigue level exceeds the preset change threshold, for example, when the fatigue level index value rises rapidly, it is determined that the characteristic value index of the fatigue level meets the corresponding preset conditions. It can be understood that the characteristic value of the fatigue level can be obtained based on image recognition, vital sign data monitoring, etc.

[0150] Similarly, for the parameter of the preceding vehicle deceleration, factors that change relatively quickly, such as vehicle speed and traffic volume, can be set as key influencing factors of the preceding vehicle deceleration parameter.

[0151] In some possible embodiments, in step S1, obtaining a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model may include:

[0152] S151. Determine a parameter to be updated corresponding to the target key influencing factor as at least one parameter to be updated, and obtain a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model.

[0153] It should be noted that when steps S141 to S142 are used to determine whether the longitudinal safety distance calculation model meets the preset update conditions, when updating the longitudinal safety distance calculation model, only the parameters corresponding to the factors that have undergone significant changes may be updated.

[0154] Specifically, when it is determined that the characteristic value index of a certain target key influencing factor meets the corresponding preset conditions, it can be determined which parameter to be updated (target parameter to be updated) the target key influencing factor belongs to. Since the reason for triggering the update of the model this time is that the characteristic value index of the target key influencing factor corresponding to the target parameter to be updated has changed significantly, when the longitudinal safety distance calculation model is updated this time, other parameters to be updated can remain unchanged temporarily, and only the parameter of the target parameter to be updated can be dynamically estimated and the model can be updated.

[0155] Based on this, by monitoring the changes in indicators of key influencing factors and determining the parameters corresponding to the qualified target key influencing factors as the parameters that need to be updated currently, temporarily ignoring the dynamic adjustment of other parameters to be updated whose indicators do not change significantly, the flexibility and efficiency of updating the longitudinal safety distance model can be further improved.

[0156] Please refer to Figure 4 , Figure 4 FIG. 1 shows a block diagram of a dynamic updating device for a safety distance model provided by some embodiments of the present application. It should be understood that the dynamic updating device for the safety distance model is similar to the above-mentioned Figure 1 Corresponding to the method embodiment, each step involved in the above method embodiment can be executed. The specific functions of the dynamic updating device of the safety distance model can be found in the above description. To avoid repetition, the detailed description is appropriately omitted here.

[0157] Figure 4 The dynamic updating device of the safety distance model includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the dynamic updating device of the safety distance model, and the dynamic updating device of the safety distance model includes:

[0158] The parameter dynamic estimation module 410 is used to determine whether the longitudinal safety distance calculation model meets the preset update condition, and obtain a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model; wherein the at least one parameter to be updated includes at least one of the driver's reaction time, the deceleration of the preceding vehicle, and the maximum deceleration of the own vehicle;

[0159] The calculation model updating module 420 is used to update the longitudinal safety distance calculation model based on the dynamic estimation results corresponding to the parameters to be updated, so as to obtain a dynamically optimized longitudinal safety distance calculation model.

[0160] It can be understood that the above-mentioned device item embodiment corresponds to the method item embodiment of the present invention. The dynamic update device of a safety distance model provided by the embodiment of the present invention can implement the dynamic update method of the safety distance model provided by any method item embodiment of the present invention.

[0161] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method, and will not be described in detail here.

[0162] like Figure 5 As shown, some embodiments of the present application provide an electronic device 500, which includes: a memory 510, a processor 520, and a computer program stored in the memory 510 and executable on the processor 520, wherein the processor 520 reads the program from the memory 510 through a bus 530 and executes the program to implement a method of any embodiment of the dynamic update method of the safety distance model as described above.

[0163] Processor 520 can process digital signals and can include various computing structures, such as complex instruction set computer structure, reduced instruction set computer structure, or a structure that implements a combination of multiple instruction sets. In some examples, processor 520 can be a microprocessor.

[0164] The memory 510 may be used to store instructions executed by the processor 520 or data related to the execution of instructions. These instructions and / or data may include codes for implementing some or all functions of one or more modules described in the embodiments of the present application. The processor 520 of the disclosed embodiment may be used to execute instructions in the memory 510 to implement the method shown above. The memory 510 includes a dynamic random access memory, a static random access memory, a flash memory, an optical memory, or other memory known to those skilled in the art.

[0165] Some embodiments of the present application further provide a computer-readable storage medium having a computer program stored thereon. The computer program is executed by a processor to execute the method described in the method embodiment.

[0166] Some embodiments of the present application further provide a computer program product, which, when executed on a computer, enables the computer to execute the method described in the method embodiment.

[0167] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0168] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0169] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0170] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0171] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0172] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0173] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for dynamically updating a safety distance model, characterized in that: include: Determining that the longitudinal safety distance calculation model satisfies a preset update condition, and obtaining a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model; wherein the at least one parameter to be updated includes at least one of a driver's reaction time, a preceding vehicle deceleration, and a maximum deceleration of the own vehicle; The longitudinal safety distance calculation model is updated based on the dynamic estimation results corresponding to the parameters to be updated to obtain a dynamically optimized longitudinal safety distance calculation model.

2. The method for dynamically updating the safety distance model according to claim 1, characterized in that: The obtaining of a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model includes: Acquire a current first feature value set corresponding to at least one preset reaction time influencing factor; wherein the at least one reaction time influencing factor includes at least one of the driver's age, driving mileage, and fatigue level; Based on the first eigenvalue set, a fuzzy theory method is used to obtain a dynamic estimation result corresponding to the driver's reaction time in the longitudinal safety distance calculation model.

3. The method for dynamically updating the safety distance model according to claim 1, characterized in that: The obtaining of a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model includes: Acquire a current second feature value set corresponding to at least one preset deceleration influencing factor of the preceding vehicle; wherein the at least one deceleration influencing factor of the preceding vehicle includes at least one of vehicle speed, vehicle flow, vehicle flow density, visibility, weather, lighting, road surface condition, and time period type; Based on the second eigenvalue set, a fuzzy theory method is used to obtain a dynamic estimation result corresponding to the deceleration of the leading vehicle in the longitudinal safety distance calculation model.

4. The method for dynamically updating the safety distance model according to claim 1, characterized in that: The obtaining of a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model includes: Acquire a current third eigenvalue set corresponding to at least one preset vehicle deceleration influencing factor; wherein the at least one vehicle deceleration influencing factor includes at least one of a road surface condition and a tire pressure; Based on a preset correspondence table between eigenvalues ​​and deceleration coefficients, a current deceleration coefficient corresponding to the third eigenvalue set is obtained, and based on the current deceleration coefficient, a dynamic estimation result corresponding to the maximum deceleration of the vehicle in the longitudinal safety distance calculation model is obtained.

5. The method for dynamically updating the safety distance model according to claim 2, characterized in that: The method of obtaining a dynamic estimation result corresponding to the driver reaction time in the longitudinal safety distance calculation model based on the first eigenvalue set by using a fuzzy theory method includes: Based on the first eigenvalue set, respectively obtaining fuzzy subsets corresponding to each reaction time influencing factor; Determine the corresponding reaction time fuzzy quantity based on the fuzzy subsets of each reaction time influencing factor and the preset reaction time fuzzy rule control table; The reaction time fuzzy quantity is defuzzified based on a preset reaction time membership function to obtain a dynamic estimation result corresponding to the driver's reaction time in the longitudinal safety distance calculation model.

6. The method for dynamically updating the safety distance model according to claim 3, characterized in that: The method of obtaining the dynamic estimation result corresponding to the deceleration of the preceding vehicle in the longitudinal safety distance calculation model by using a fuzzy theory method based on the second eigenvalue set includes: Based on the second eigenvalue set, respectively obtaining fuzzy subsets corresponding to the deceleration influencing factors of each preceding vehicle; Based on the fuzzy subsets of the factors affecting the deceleration of the preceding vehicle and the preset fuzzy rule base set, the initial fuzzy quantities corresponding to the estimation dimensions are respectively obtained; wherein the fuzzy rule base set includes a traffic condition fuzzy rule base, an environmental condition fuzzy rule base and a traffic demand fuzzy rule base; the initial fuzzy quantities corresponding to the estimation dimensions include a traffic condition fuzzy quantity, an environmental condition fuzzy quantity and a traffic demand fuzzy quantity; Determine the corresponding fuzzy amount of the preceding vehicle deceleration based on the initial fuzzy amount corresponding to each estimation dimension and a preset preceding vehicle deceleration fuzzy rule control table; The fuzzy value of the preceding vehicle deceleration is defuzzified based on a preset preceding vehicle deceleration membership function to obtain a dynamic estimation result corresponding to the preceding vehicle deceleration in the longitudinal safety distance calculation model.

7. The method for dynamically updating the safety distance model according to claim 3, characterized in that: The method of obtaining the dynamic estimation result corresponding to the deceleration of the preceding vehicle in the longitudinal safety distance calculation model by using a fuzzy theory method based on the second eigenvalue set includes: Based on the second eigenvalue set, respectively obtaining fuzzy subsets corresponding to the deceleration influencing factors of each preceding vehicle; Determine the corresponding fuzzy value of the preceding vehicle deceleration based on the fuzzy subsets of the influencing factors of the preceding vehicle deceleration and the preset preceding vehicle deceleration fuzzy rule control table; The fuzzy value of the preceding vehicle deceleration is defuzzified based on a preset preceding vehicle deceleration membership function to obtain a dynamic estimation result corresponding to the preceding vehicle deceleration in the longitudinal safety distance calculation model.

8. The method for dynamically updating the safety distance model according to claim 1, characterized in that: The step of determining whether the longitudinal safety distance calculation model satisfies a preset update condition includes: The accumulated time from the historical moment when the longitudinal safety distance calculation model was last updated to the current moment is obtained in real time. When it is determined that the accumulated time exceeds the preset model update cycle, it is determined that the longitudinal safety distance calculation model meets the preset update conditions.

9. The method for dynamically updating the safety distance model according to claim 1, characterized in that: The step of determining whether the longitudinal safety distance calculation model satisfies a preset update condition includes: Determine the key influencing factors corresponding to the at least one parameter to be updated, and obtain the characteristic value index of each key influencing factor in real time; When it is determined that the characteristic value index of the target key influencing factor meets the corresponding preset conditions, it is determined that the longitudinal safety distance calculation model meets the preset update conditions; The target key influencing factor is one of the key influencing factors; and the characteristic value indicator for judging whether the target key influencing factor meets the corresponding preset condition includes at least one of the following: If it is determined that the current characteristic value of the target key influencing factor exceeds the preset threshold range, then it is determined that the characteristic value index of the target key influencing factor meets the corresponding preset condition; If it is determined that the characteristic value change per unit time of the target key influencing factor exceeds a preset change value threshold, it is determined that the characteristic value index of the target key influencing factor meets the corresponding preset condition.

10. The method for dynamically updating the safety distance model according to claim 9, characterized in that: The obtaining of a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model includes: The parameter to be updated corresponding to the target key influencing factor is determined as the at least one parameter to be updated, and a dynamic estimation result corresponding to the at least one parameter to be updated in the longitudinal safety distance calculation model is obtained.

11. A dynamic updating device for a safety distance model, characterized in that: include: A parameter dynamic estimation module, used to determine whether the longitudinal safety distance calculation model meets the preset update condition, and obtain a dynamic estimation result corresponding to at least one parameter to be updated in the longitudinal safety distance calculation model; wherein the at least one parameter to be updated includes at least one of the driver's reaction time, the deceleration of the preceding vehicle, and the maximum deceleration of the own vehicle; The calculation model updating module is used to update the longitudinal safety distance calculation model based on the dynamic estimation results corresponding to each parameter to be updated, so as to obtain a dynamically optimized longitudinal safety distance calculation model.

12. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for dynamically updating the safety distance model described in any one of claims 1 to 10 can be implemented.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for dynamically updating the safety distance model according to any one of claims 1 to 10 is executed.

14. A vehicle, characterized in that: Comprising the electronic device as claimed in claim 12.

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