CN113158415B - Vehicle track similarity evaluation method based on error analysis - Google Patents
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Abstract
The invention discloses a vehicle track similarity evaluation method based on error analysis, which comprises the following steps: s1, forming a predicted track and a real track of a vehicle according to a track step k; s2, calculating related errors between the real track and the predicted track, wherein the related errors comprise scalar errors, vector errors, scalar error jitter and vector error jitter; s3, determining an influence factor and calculating the size of a jitter window; s4, calculating the similarity. According to the invention, the scalar error and vector error values of the predicted and real tracks are calculated, and the influence degree of the scalar error and vector error on the similarity is dynamically adjusted by the scalar error jitter and vector error jitter values, so that the calculation efficiency of the track similarity of the predicted track and the real track of the vehicle can be improved, the updating of the vehicle track prediction model can be efficiently assisted, and the running safety of the vehicle is enhanced.
Description
Technical Field
The invention belongs to the field of intelligent traffic, and particularly relates to a vehicle track similarity evaluation method based on error analysis.
Background
In recent years, global positioning technology has rapidly developed and has been widely used in intelligent transportation systems. Among them, predicting the travel track of a vehicle based on artificial intelligence technology is one of the key directions of current research. The prediction of the future driving track is realized by utilizing the history and real-time data of the vehicle.
The GPS equipped vehicle can locate its own position in real time while being able to sense surrounding vehicles through sensors onboard the vehicle. Based on the sensed collected information, the system may predict the travel track of surrounding vehicles in a future period of time in advance. According to the future driving track of the surrounding vehicle, the automatic driving vehicle can dynamically plan the driving route of the automatic driving vehicle. Thus, the ability of the autonomous vehicle to cope with a changing complex traffic environment is improved.
In a real-world problem, the track prediction model is directly put into use after being trained by a data set, but the track prediction model trained at one time cannot adapt to various surrounding driving vehicles due to randomness of driving behaviors of other traffic participants and constraint of road geometry. The lack of feedback and updated prediction models can lead to certain deviation of prediction results, and finally the aim of improving the safety of the automatic driving vehicle cannot be achieved. Therefore, a method for evaluating the similarity between the predicted track and the real track is needed to judge the accuracy of track prediction, and the index is used as feedback of the model to update the prediction model.
And secondly, predicting the position single point of the vehicle at the next moment through historical data, and if the similarity is measured through the Euclidean distance between the predicted single point of the track and the real single point of the track in a traditional mode, neglecting the running trend of the vehicle in a period of time. It is then necessary to consider the accuracy of the trajectory prediction over this time period by single point fitting of multiple trajectories into a trajectory.
Finally, in the process of predicting the track of surrounding vehicles by the automatic driving vehicle, certain errors exist in the track prediction model, so that certain measures are needed to be taken in the similarity evaluation method to eliminate the randomness of each moment.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides the vehicle track similarity assessment method based on error analysis, which is used for dynamically adjusting the influence degree of scalar errors and vector errors on the similarity by calculating the scalar errors and vector errors of the predicted and real tracks and then using the scalar error jitter and vector error jitter values, so that the calculation efficiency of the track similarity of the predicted track and the real track of the vehicle can be improved, the update of a vehicle track prediction model can be effectively assisted, and the running safety of the vehicle is enhanced.
The aim of the invention is realized by the following technical scheme: a vehicle track similarity evaluation method based on error analysis comprises the following steps:
s1, forming a predicted track and a real track of a vehicle according to a track step k;
s2, calculating related errors between the real track and the predicted track, wherein the related errors comprise scalar errors, vector errors, scalar error jitter and vector error jitter;
s3, determining an influence factor and calculating the size of a jitter window;
s4, calculating the similarity.
Further, the scalar error refers to the area of a closed graph formed by two tracks on a coordinate axis;
vector error refers to the sum of absolute values of the change amounts of the areas enclosed by every two adjacent tracks between 0.1s in the step length of k time;
scalar error dithering refers to the variance of the scalar error for each k time step over the time of the dithering window size;
vector error dithering refers to the variance of the vector error for each k time step over the time of the dithering window size.
Further, the specific implementation method of the step S3 is as follows: in the initial state, the default scalar error and the vector error affect the track similarity to the same extent, namely, a scalar error affecting factor alpha=0.5 and a vector error affecting factor beta=0.5; and the initial time system defines scalar error jitter thresholdAnd vector error jitter threshold->
When both the scalar error jitter and the vector error jitter are less than or equal to the set threshold, α=β=0.5 in the trajectory similarity; when the scalar error and the vector error jitter are both larger than a set threshold, the track similarity is 0; when one of the scalar error jitter and the vector error jitter exceeds a set threshold, the influence factor of the error corresponding to the jitter in the track similarity evaluation is increased, and the influence factor of the other error in the track similarity evaluation is correspondingly reduced, wherein the increasing and reducing methods can be customized according to application scenes and requirements, such as linear increase and reduction, and the alpha+beta=1 is satisfied.
Setting an initial time jitter window as 1, wherein error jitter can only calculate the variance of an initial k time step, so that the result is 0; when the scalar error jitter and the vector error jitter are both smaller than or equal to the set threshold, the jitter window size is unchanged; when the scalar error and the vector error jitter are both larger than the set threshold, adding 2 to the jitter window size; when one of the scalar error jitter and the vector error jitter exceeds a set threshold, the jitter window size is increased by 1.
Further, the specific implementation method of the step S3 is as follows:
s41, taking scalar error, vector error, scalar error jitter and vector error jitter together as track similarity measurement basis, and adjusting influence factors of the scalar error and the vector error on the track similarity by using the values of the scalar error jitter and the vector error jitter; the specific calculation method comprises the following steps: respectively normalizing scalar error, vector error, scalar error jitter and vector error jitter by using Gaussian kernel function to obtain:
wherein a, d, q, z is respectively a scalar error, a vector error, a scalar error jitter and a vector error jitter after normalization, A, D, Q, Z is respectively a scalar error, a vector error, a scalar error jitter and a vector error jitter before normalization; wherein sigma is the width parameter of the Gaussian kernel function, the radial action range of the function is controlled, and the width parameter can be determined according to the application scene and the requirement;
wherein the method comprises the steps ofMean value of scalar errors over time, A 1 ，A 2 ，...，A n Represents scalar error in step 1, 2, …, nth time;
mean value of vector error in all time, D 1 ，D 2 ，...，D n Representing vector errors in the 1 st, 2 nd, … th, n th step time;
s42, calculating the track similarity theta:
θ＝αa+βd
alpha+beta=1, alpha is not less than 0 and beta is not less than 0.
The beneficial effects of the invention are as follows: according to the invention, the scalar error and vector error values of the predicted and real tracks are calculated, and the influence degree of the scalar error and vector error on the similarity is dynamically adjusted by the scalar error jitter and vector error jitter values, so that the calculation efficiency of the track similarity of the predicted track and the real track of the vehicle can be improved, the updating of the vehicle track prediction model can be efficiently assisted, and the running safety of the vehicle is enhanced.
Drawings
FIG. 1 is a flow chart of a vehicle trajectory similarity assessment method of the present invention;
FIG. 2 is a schematic diagram of a real trajectory and predicted trajectory of the present invention;
FIG. 3 is a schematic diagram of the error between the real track and the predicted track according to the present invention;
fig. 4 is a schematic diagram of error jitter according to the present invention.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the vehicle track similarity evaluation method based on error analysis of the present invention includes the following steps:
s1, forming a predicted track and a real track of a vehicle according to a track step k; the track-based prediction mode refers to predicting the position of the vehicle at a future moment (usually after 0.1 s) at the current moment according to the historical track. When a time step k is selected, all tracks in the time k can be subjected to single-point fitting to form a predicted track. The same is true for the collection of the real track, and the single point of the position of the vehicle is collected every 0.1 s. And (3) performing single-point fitting on all the tracks acquired in the k time to form a real track. The method for forming the track by track single-point fitting when the time step k takes 0.5s is shown in fig. 2, and the predicted and collected track single points are generated in each 0.1s, so that the track single-point connecting line from 0 time to 0.1 time, the track single-point connecting line from 0.1 time to 0.2 time and the track single-point connecting line up to 0.4 time to 0.5 time are connected, and the track with X coordinates is formed. After the predicted track and the real track are obtained, a similarity evaluation process between the two tracks is performed.
S2, calculating related errors between the real track and the predicted track, wherein the related errors comprise scalar errors, vector errors, scalar error jitter and vector error jitter;
the track prediction mode is to separately predict the abscissa X and the ordinate Y, so that the track similarity is evaluated for the transverse track and the longitudinal track respectively when the track similarity is considered. The track similarity predicted by the automatic driving vehicle to surrounding vehicles is measured from the transverse direction and the longitudinal direction, and two track similarity values are finally obtained, so that the accuracy of the track prediction model in the transverse direction and the longitudinal direction is analyzed in more detail. Since the similarity evaluation methods of the lateral trajectories and the longitudinal trajectories are the same, the trajectory similarity evaluation method based on the error analysis will be explained herein by taking the lateral trajectories as an example. According to the relation of the two tracks on the coordinate axis, the two tracks can be divided into two cases of no overlapping and overlapping, and the two cases are respectively represented by a graph a and a graph b in fig. 2. The definition of these four parameters can be specifically analyzed in conjunction with fig. 3:
the scalar error A refers to the area of a closed graph formed by two tracks on a coordinate axis; in fig. a, scalar error a=a1+a2+a3+a4+a5, where A1, A2, A3, A4, A5 are the areas formed between adjacent two 0.1s tracks. In fig. B, scalar error a=b1+b2 [1] +b2[2] +b3+b4[1] +b4[2] +b5, where B1, B2 (b2=b2 [1] +b2[2 ]), B3, B4 (b4=b4 [1] +b4[2 ]), B5 is the area formed between two adjacent 0.1s tracks.
The vector error D refers to the sum of absolute values of the change amounts of the areas enclosed by every two adjacent tracks between 0.1s in the step length of k time; vector errors are a trend in measuring trajectories, and thus require adding directionality to the numerical deviation. When the predicted value is higher than the true value, the directivity is positive; when the predicted value is lower than the true value, the directivity is negative. In fig. a, the vector error d= |a2-a1|+|a3-a2|+|a4-a3|+|a5-a4|, where A1, A2, A3, A4, A5 are the areas formed between the adjacent two 0.1s trajectories. In fig. B, the vector error d= | (b2 [1] -b2[2 ]) -b1|+|b3- (b2 [1] -b2[2 ])|+| (b4 [2] -b4[1 ]) -b3|+|b5- (b4 [2] -b4[1 ])|, where B1, B2 (b2=b2 [1] +b2[2 ]), b3, B4 (b4=b4 [1] +b4[2 ]), B5 are the areas formed between two adjacent 0.1s trajectories.
Scalar error jitter Q refers to the variance of scalar errors for each k time steps over the time of the jitter window size; the scalar error jitter needs to be calculated according to the size of the jitter window, and the initial time jitter window is 1, and then the scalar error jitter in the first k steps is 0. The scalar error jitter thereafter requires pushing forward the jitter window size k time steps at the current time instant, calculating the variance of the scalar error in each k step during that time. From the illustration in FIG. 4, the jitter window size at this time is 3, then scalar error jitterWherein A is k1 、A k2 、A k3 Respectively represent k 1 Scalar error in time step, k 2 Time stepScalar error sum k in length 3 Scalar errors in time steps. />Is A k1 、A k2 、A k3 The average of these three scalar errors.
Vector error jitter Z refers to the variance of the vector error for each k time step over the time of the jitter window size. Vector error jitter also needs to be calculated according to the size of the jitter window, and if the initial time jitter window is 1, the vector error jitter in the first k steps is 0. The scalar error dithering after this requires pushing the dithering window size forward at the current k step by k time steps, calculating the variance of the vector error in each k step over that time. As shown in fig. 4, when the jitter window size is 3, the vector error is jitteredWherein D is k1 、D k2 、D k3 Respectively represent k 1 Vector error in time step, k 2 Vector error sum k in time step 3 Vector error in time step. />For D k1 、D k2 、D k3 The average of these three vector errors.
S3, determining an influence factor and calculating the size of a jitter window; the specific implementation method comprises the following steps: in the initial state, the default scalar error and the vector error affect the track similarity to the same extent, namely, a scalar error affecting factor alpha=0.5 and a vector error affecting factor beta=0.5; and the initial time system defines scalar error jitter thresholdAnd vector error jitter threshold->
When both the scalar error jitter and the vector error jitter are less than or equal to the set threshold, α=β=0.5 in the trajectory similarity; when the scalar error and the vector error jitter are both larger than a set threshold, the track similarity is 0; when one of the scalar error jitter and the vector error jitter exceeds a set threshold, the influence factor of the error corresponding to the jitter in the track similarity evaluation is increased, and the influence factor of the other error in the track similarity evaluation is correspondingly reduced, wherein the increasing and reducing methods can be customized according to application scenes and requirements, such as linear increase and reduction, and the alpha+beta=1 is satisfied.
Setting an initial time jitter window as 1, wherein error jitter can only calculate the variance of an initial k time step, so that the result is 0; when the scalar error jitter and the vector error jitter are both smaller than or equal to the set threshold, the jitter window size is unchanged; when the scalar error and the vector error jitter are both larger than the set threshold, adding 2 to the jitter window size; when one of the scalar error jitter and the vector error jitter exceeds a set threshold, the jitter window size is increased by 1.
S4, calculating the similarity; the specific implementation method comprises the following steps:
s41, taking scalar error, vector error, scalar error jitter and vector error jitter together as track similarity measurement basis, and adjusting influence factors of the scalar error and the vector error on the track similarity by using the values of the scalar error jitter and the vector error jitter; the specific calculation method comprises the following steps: since A, D, Q and Z are numbers equal to or greater than 0, the scalar error, the vector error, the scalar error jitter, and the vector error jitter are normalized by the gaussian kernel function, respectively, to obtain:
wherein a, d, q, z is respectively a scalar error, a vector error, a scalar error jitter and a vector error jitter after normalization, A, D, Q, Z is respectively a scalar error, a vector error, a scalar error jitter and a vector error jitter before normalization; wherein sigma is the width parameter of the Gaussian kernel function, the radial action range of the function is controlled, and the width parameter can be determined according to the application scene and the requirement;
wherein the method comprises the steps ofMean value of scalar errors over time, A 1 ，A 2 ，...，A n Represents scalar error in step 1, 2, …, nth time;
mean value of vector error in all time, D 1 ，D 2 ，...，D n Representing vector errors in the 1 st, 2 nd, … th, n th step time;
s42, calculating the track similarity theta:
θ＝αa+βd
alpha+beta=1, alpha is not less than 0 and beta is not less than 0
Since 1.gtoreq.a.gtoreq.0, 1.gtoreq.d.gtoreq.0, α+β=1, θ is a value ranging between [0,1], and thus can be used as an evaluation of the trajectory similarity.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (2)
1. The vehicle track similarity evaluation method based on error analysis is characterized by comprising the following steps of:
s1, forming a predicted track and a real track of a vehicle according to a track step k;
s2, calculating related errors between the real track and the predicted track, wherein the related errors comprise scalar errors, vector errors, scalar error jitter and vector error jitter;
s3, determining an influence factor and calculating the size of a jitter window; the specific implementation method comprises the following steps: in the initial state, the default scalar error and the vector error affect the track similarity to the same extent, namely, a scalar error affecting factor alpha=0.5 and a vector error affecting factor beta=0.5; and the initial time system defines scalar error jitter thresholdAnd vector error jitter threshold->
When both the scalar error jitter and the vector error jitter are less than or equal to the set threshold, α=β=0.5 in the trajectory similarity; when the scalar error and the vector error jitter are both larger than a set threshold, the track similarity is 0; when one of scalar error jitter and vector error jitter exceeds a set threshold, increasing the influence factor of the error corresponding to the jitter in track similarity evaluation, and correspondingly reducing the influence factor of the other error in track similarity evaluation, so that alpha+beta=1 is satisfied;
setting an initial time jitter window as 1, wherein error jitter can only calculate the variance of an initial k time step, so that the result is 0; when the scalar error jitter and the vector error jitter are both smaller than or equal to the set threshold, the jitter window size is unchanged; when the scalar error and the vector error jitter are both larger than the set threshold, adding 2 to the jitter window size; when one of the scalar error jitter and the vector error jitter exceeds a set threshold, increasing the jitter window size by 1;
s4, calculating the similarity, wherein the specific implementation method comprises the following steps:
s41, taking scalar error, vector error, scalar error jitter and vector error jitter together as track similarity measurement basis, and adjusting influence factors of the scalar error and the vector error on the track similarity by using the values of the scalar error jitter and the vector error jitter; the specific calculation method comprises the following steps: respectively normalizing scalar error, vector error, scalar error jitter and vector error jitter by using Gaussian kernel function to obtain:
1≥a≥0，1≥d≥0，1≥q≥0，1≥z≥0
wherein a, d, q, z is respectively a scalar error, a vector error, a scalar error jitter and a vector error jitter after normalization, A, D, Q, Z is respectively a scalar error, a vector error, a scalar error jitter and a vector error jitter before normalization; wherein sigma is the width parameter of the Gaussian kernel function, and the radial action range of the function is controlled;
wherein the method comprises the steps of Mean value of scalar errors over time, A 1 ，A 2 ，...，A n Represents scalar error in step 1, 2, …, nth time;
mean value of vector error in all time, D 1 ，D 2 ，...，D n Representing vector errors in the 1 st, 2 nd, … th, n th step time;
s42, calculating the track similarity theta:
θ＝αa+βd
alpha+beta=1, alpha is not less than 0 and beta is not less than 0.
2. The method for evaluating the similarity of vehicle trajectories based on error analysis according to claim 1, wherein the scalar error refers to the area of a closed figure defined by two trajectories on a coordinate axis;
vector error refers to the sum of absolute values of the change amounts of the areas enclosed by every two adjacent tracks between 0.1s in the step length of k time;
scalar error dithering refers to the variance of the scalar error for each k time step over the time of the dithering window size;
vector error dithering refers to the variance of the vector error for each k time step over the time of the dithering window size.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0696234A (en) * | 1992-09-09 | 1994-04-08 | Hitachi Ltd | Processing system for time sequential data |
| CN104657746A (en) * | 2015-01-29 | 2015-05-27 | 电子科技大学 | Anomaly detection method based on vehicle trajectory similarity |
| CN105512157A (en) * | 2014-10-28 | 2016-04-20 | 中国民用航空总局第二研究所 | Method for integration processing of comprehensive track data |
| CN107679558A (en) * | 2017-09-19 | 2018-02-09 | 电子科技大学 | A kind of user trajectory method for measuring similarity based on metric learning |
| CN110598311A (en) * | 2019-09-06 | 2019-12-20 | 广东工业大学 | Automatic driving vehicle track tracking method |
| CN111930791A (en) * | 2020-05-28 | 2020-11-13 | 中南大学 | Similarity calculation method and system for vehicle track and storage medium |
| CN112015956A (en) * | 2020-09-04 | 2020-12-01 | 杭州海康威视数字技术股份有限公司 | Similarity determination method, device, equipment and storage medium for mobile object |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8676432B2 (en) * | 2010-01-13 | 2014-03-18 | GM Global Technology Operations LLC | Fault prediction framework using temporal data mining |
| US9243916B2 (en) * | 2013-02-21 | 2016-01-26 | Regents Of The University Of Minnesota | Observability-constrained vision-aided inertial navigation |
| US9691237B2 (en) * | 2013-08-21 | 2017-06-27 | Raven Industries, Inc. | Non-visual navigation feedback system and method |
| US20150363865A1 (en) * | 2014-06-13 | 2015-12-17 | Truecar, Inc. | Systems and methods for vehicle purchase recommendations |
| KR20160150212A (en) * | 2015-06-19 | 2016-12-29 | (주)연결해 | Techniques for matching the movement path of the carpool driver and rider |
| DE102016216335B4 (en) * | 2016-08-30 | 2020-12-10 | Continental Automotive Gmbh | System and method for the analysis of driving trajectories for a route section |
| CN112116154B (en) * | 2020-09-18 | 2024-05-14 | 北京嘀嘀无限科技发展有限公司 | Data processing method, data processing device, storage medium and electronic equipment |
-
2021
- 2021-02-23 CN CN202110200652.XA patent/CN113158415B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0696234A (en) * | 1992-09-09 | 1994-04-08 | Hitachi Ltd | Processing system for time sequential data |
| CN105512157A (en) * | 2014-10-28 | 2016-04-20 | 中国民用航空总局第二研究所 | Method for integration processing of comprehensive track data |
| CN104657746A (en) * | 2015-01-29 | 2015-05-27 | 电子科技大学 | Anomaly detection method based on vehicle trajectory similarity |
| CN107679558A (en) * | 2017-09-19 | 2018-02-09 | 电子科技大学 | A kind of user trajectory method for measuring similarity based on metric learning |
| CN110598311A (en) * | 2019-09-06 | 2019-12-20 | 广东工业大学 | Automatic driving vehicle track tracking method |
| CN111930791A (en) * | 2020-05-28 | 2020-11-13 | 中南大学 | Similarity calculation method and system for vehicle track and storage medium |
| CN112015956A (en) * | 2020-09-04 | 2020-12-01 | 杭州海康威视数字技术股份有限公司 | Similarity determination method, device, equipment and storage medium for mobile object |
Non-Patent Citations (1)
| Title |
|---|
| 动态车辆导航系统车道级路径引导方法研究;张林;中国博士学位论文全文数据库 工程科技II辑;第1-167页 * |
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