Method and system for constructing predictive vehicle driving condition, device and medium thereof
The present disclosure discloses a method and system for constructing a predictive vehicle driving condition, a device and a medium. The method includes: acquiring a first road set and historical vehicle driving data; screening distances less than or equal to a distance threshold from the first distance set, generating a road point-in-time set; sequencing a plurality of point-in-time corresponding to the same road segment, and cutting at two adjacent point-in-time with a time difference greater than a time threshold, to obtain a plurality of road segments; establishing road segment databases; screening segments with segment feature data meeting a second preset condition from all the road segment databases, and generating a road-condition segment set; and sequencing a plurality of condition segments corresponding to each road segment, and screening condition segments meeting a third preset condition from all the segment sequences, to generate an optimal driving condition.
1 . A method for constructing a predictive vehicle driving condition, including the following steps:
acquiring a first road set of a planned route and historical vehicle driving data; wherein the first road set at least includes a plurality of road parameter data and its corresponding parameter data identified by a global positioning system (GPS); and the historical vehicle driving data at least includes a plurality of point-in-time data and corresponding driving data;
calculating first distances from longitudes and latitudes of the point-in-time data to all roads in the first road set so as to obtain a first distance set; and meanwhile, screening distances less than or equal to a distance threshold from the first distance set so as to obtain a second distance set; generating a road-point-in-time set according to the second distance set, wherein the road-point-in-time set at least includes a plurality of road segments and a plurality of point-in-time corresponding to each road segment;
sequencing the plurality of point-in-time data corresponding to the same road segment in the road-point-in-time set according to a time sequence so as to obtain a data sequence; meanwhile, traversing the data sequence, and cutting at two adjacent point-in-time with a time difference greater than a time threshold so as to obtain a plurality of road segments corresponding to a current road;
establishing a road segment database of each road segment according to the segments meeting a first preset condition in the road segment; wherein the road segment database at least includes the plurality of road segments and corresponding segment feature data;
screening segments with the segment feature data meeting a second preset condition from all the road segment databases so as to generate a road-condition segment set, wherein the road-condition segment set at least includes a plurality of road segments and a plurality of condition segments corresponding to each road segment; and
sequencing the plurality of condition segments corresponding to each road segment in the road-condition segment set according to a sequence from a high confidence level to a low confidence level so as to obtain a plurality of segment sequences; and meanwhile, screening condition segments meeting a third preset condition from all the segment sequences to generate an optimal driving condition.
2 . The method for constructing a predictive vehicle driving condition according to claim 1 , wherein the step of acquiring a first road set of a planned route comprise:
traversing the first road set, and labeling roads with lengths less than a first threshold as first-type roads; labeling roads with lengths greater than or equal to the first threshold as second-type roads;
normalizing all the first-type roads and roads connected with the first-type roads, calculating a first Euclidean distance and a second Euclidean distance between the normalized first-type roads and roads connected with the first-type roads, and selecting roads corresponding to the smaller Euclidean distance in the first Euclidean distance or the second Euclidean distance for combination, to obtain a plurality of segments of combined roads; meanwhile, denoting non-combined first-type roads as third-type roads; and
generating the first road set according to the combined roads, the third-type roads and the second-type roads.
3 . The method for constructing a predictive vehicle driving condition according to claim 1 , wherein the first preset condition comprises:
a difference between a segment orientation angle of the road segment and an orientation angle of a road corresponding to the road segment is less than a first difference, and a difference between a segment length of the road segment and a length of the road corresponding to the road segment is less than a second difference.
4 . The method for constructing a predictive vehicle driving condition according to claim 1 , wherein the segment feature data at least includes: a mean speed of the segment, a mean acceleration of the segment, a mean deceleration of the segment and a confidence level of a speed-acceleration distribution;
the second preset condition comprises:
a difference between the mean acceleration of the road segment and a mean acceleration of the road corresponding to the road segment is less than a fifth difference, a difference between the mean deceleration of the road segment and a mean deceleration of the road corresponding to the road segment is less than a sixth difference, and the confidence level of the road segment is greater than a preset confidence level.
5 . The method for constructing a predictive vehicle driving condition according to claim 4 , wherein the confidence level of the speed-acceleration distribution is acquired according to the following steps:
calculating a chi-square value of the speed-acceleration distribution;
acquiring a corresponding chi-square distribution database according to a freedom degree of the speed-acceleration distribution; wherein the chi-square distribution database at least includes a plurality of chi-square values and corresponding confidence levels; and
searching the confidence level corresponding to data associated with the chi-square values in the chi-square distribution database, which serves as the confidence level of the speed-acceleration distribution.
6 . An electronic device, including a memory, a processor and computer programs stored on the memory and capable of running on the processor, wherein, the processor implements the steps of a method for constructing the predictive vehicle driving condition, the method comprising the following steps:
acquiring a first road set of a planned route and historical vehicle driving data; wherein the first road set at least includes a plurality of road parameter data and its corresponding parameter data identified by a global positioning system (GPS); and the historical vehicle driving data at least includes a plurality of point-in-time data and corresponding driving data;
calculating first distances from longitudes and latitudes of the point-in-time data to all roads in the first road set so as to obtain a first distance set; and meanwhile, screening distances less than or equal to a distance threshold from the first distance set so as to obtain a second distance set; generating a road-point-in-time set according to the second distance set, wherein the road-point-in-time set at least includes a plurality of road segments and a plurality of point-in-time corresponding to each road segment;
sequencing the plurality of point-in-time data corresponding to the same road segment in the road-point-in-time set according to a time sequence so as to obtain a data sequence;
meanwhile, traversing the data sequence, and cutting at two adjacent point-in-time with a time difference greater than a time threshold so as to obtain a plurality of road segments corresponding to a current road;
establishing a road segment database of each road segment according to the segments meeting a first preset condition in the road segment; wherein the road segment database at least includes the plurality of road segments and corresponding segment feature data;
screening segments with the segment feature data meeting a second preset condition from all the road segment databases so as to generate a road-condition segment set, wherein the road-condition segment set at least includes a plurality of road segments and a plurality of condition segments corresponding to each road segment; and
sequencing the plurality of condition segments corresponding to each road segment in the road-condition segment set according to a sequence from a high confidence level to a low confidence level so as to obtain a plurality of segment sequences; and meanwhile, screening condition segments meeting a third preset condition from all the segment sequences to generate an optimal driving condition.
7 . The electronic device of claim 6 , wherein the step of acquiring a first road set of a planned route comprises the following steps:
traversing the first road set, and labeling roads with lengths less than a first threshold as first-type roads; labeling roads with lengths greater than or equal to the first threshold as second-type roads;
normalizing all the first-type roads and roads connected with the first-type roads, calculating a first Euclidean distance and a second Euclidean distance between the normalized first-type roads and roads connected with the first-type roads, and selecting roads corresponding to the smaller Euclidean distance in the first Euclidean distance or the second Euclidean distance for combination, to obtain a plurality of segments of combined roads; meanwhile, denoting non-combined first-type roads as third-type roads; and
generating the first road set according to the combined roads, the third-type roads and the second-type roads.
8 . The electronic device of claim 6 , wherein the first preset condition comprises:
a difference between a segment orientation angle of the road segment and an orientation angle of a road corresponding to the road segment is less than a first difference, and a difference between a segment length of the road segment and a length of the road corresponding to the road segment is less than a second difference.
9 . The electronic device of claim 6 , wherein the segment feature data at least includes: a mean speed of the segment, a mean acceleration of the segment, a mean deceleration of the segment and a confidence level of a speed-acceleration distribution;
the second preset condition comprises:
a difference between the mean acceleration of the road segment and a mean acceleration of the road corresponding to the road segment is less than a fifth difference, a difference between the mean deceleration of the road segment and a mean deceleration of the road corresponding to the road segment is less than a sixth difference, and the confidence level of the road segment is greater than a preset confidence level.
10 . The electronic device of claim 9 , wherein the confidence level of the speed-acceleration distribution is acquired according to the following steps:
calculating a chi-square value of the speed-acceleration distribution;
acquiring a corresponding chi-square distribution database according to a freedom degree of the speed-acceleration distribution; wherein the chi-square distribution database at least includes a plurality of chi-square values and corresponding confidence levels; and
searching the confidence level corresponding to data associated with the chi-square values in the chi-square distribution database, which serves as the confidence level of the speed-acceleration distribution.