Drivers Classification Based on the Driver Lane Change Behavior Parameters

International Journal of Material and Mechanical Engineering (IJMME), Volume 5 2016 www.ijm‐me.org doi: 10.14355/ijmme.2016.05.008

Drivers Classification Based on the Driver Lane Change Behavior Parameters Tian E 1.Beijing Engineering Research Center of Smart Mechanical Innovation Design Service 2.School of Mechanical and Electronic Engineering, Beijing Union University, Beijing, P.R. China, 100020; jdttiane@mails.buu.edu.cn

Abstract To set up lateral safety forewarning system for adapting to different driving characteristics drivers, It is necessary to classify drivers. 38 drivers’ vehicle test data are carried on factor analysis, and obtained drivers’ lateral characteristic parameters. Taking driver’ lateral characteristic parameters as a whole, 38 drivers data is analysed with hierarchical cluster analysis and K‐means cluster, made comparison of two analysis methods and adjusted the class members, determined the classification results of 38 drivers determined, it provide reference for control strategies meeting the driver characteristics and different safety requirements.

Keywords Drivers Vehicle Test Data; Driver Characteristics; Lateral Characteristic Parameters; Cluster Analysis; Drivers Classification

Introduction According to statistics, in the United States each year there are about 1.5 million traffic accidents caused by unintentional lane departure. In China, traffic accidents caused by lane departure or change lane are even more, a lot of accidents caused by lane departure due to truck drivers or bus drivers’ fatigue driving resulted that many innocent people lost their lives. In order to effectively prevent traffic accidents caused by lane departure or change lane, researchers have focused on the study of the lateral safety warning system. To establish the lateral safety warning system for adapting to different driving characteristics drivers, first, it needs to classify the drivers with driving characteristics.

Test Data Taking a passenger car as the test platform, we began the design driver characteristic test on the highway and common city roads. According to their driving habits, drivers drive one hour on the third ring road of Beijing, the fourth ring road, the fifth ring road and the badaling highway, getting a lot of real vehicle test data by testing.

In order to make the test results can be applied to the design of the lateral safety warning system, the most critical job is to extract quantitative indicators on behalf of the driverʹs behavior characteristic trends from the change lane data. Extracted 33 parameters from drivers change lane data. Through factor analysis methods, we can get: when the driver operating the vehicle to change lane, change lane times and the maximum lateral acceleration are lateral feature parameters.

Drivers Classification To the driverʹs lane‐changing inherent characteristics trends and the category features, there is no prior knowledge, thus having to use the Unsupervised Classification Clustering analysis method. According to the driver Lateral characteristic parameters distribution characteristic, drivers of similar driving characteristics are placeed to the same cluster Taking the driver lateral characteristic parameters as a whole, comparing the driver class members of Hierarchical cluster analysis and k‐means clustering analysis, the classification results of 38 drivers can be determined, which

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provides reference for control strategies meeting the driver characteristics and different safety requirements.

Hierarchical Clustering Analysis Select the Between‐groups Linkage as hierarchical clustering method, you can get the category of the cases when class number is 3‐10. When the class number is 5, the 1st driver, the 2nd driver, the 3rd driver, the 4th driver, the 5th driver, the 7th driver, the 8th driver, the 11th driver, the 12th driver, the 13th driver, the 16th driver, the 17th driver, the 18th driver, the 21st driver, the 22nd driver, the 23rd driver, the 24th driver, the 25th driver, the 26th driver, the 27th driver, the 28th driver, the 29th driver, the 32nd driver, the 33rd driver, the 34th driver, the 35th driver, the 36th driver, the 37th driver belong to the first category, the 6th driver, the 19th driver, the 30th driver, the 31st driver the second category, the 9th driver, the 10th driver, the 14th driver, the 15th driver the third category, the 20th driver separately the fourth category, the 38th driver separately the fifth category. Class 1 has 28 drivers, class 2 has 4 drivers, class 3 has 5 drivers, class 4 has 1 driver, and class 5 has 1 driver.

Figure 1 shows each driver clustering change lane time and mean value of Habit lateral position Deviate from the road centerline. As shown by definition, the smaller the change lane time value, the driver has more aggressive style. The larger that driverʹs habit lateral position deviating from the road centerline, the more aggressive that the driverʹs style.

As can be seen from Figure 1, from the change lane time, the average distribution of each cluster is in good agreement with its clustering features, cluster 4, the smaller aggressive driving style tendency cluster, its value is significantly higher than the other four clusters. Indicating that has more conservative driving style, cluster 2, the larger aggressive driving style tendency cluster, its value is smaller. From the habit lateral position, deviating from the road centerline, cluster 2 has the largest habit lateral position. Deviating from the road centerline, it has the most aggressive driving style. Cluster 4 has the smallest habit lateral position. Deviating from the road centerline, it has more conservative driving styles. Combined with Figures 1(a) and 1(b), it can be concluded from conservative to aggressive driving style clustering order: 4‐1‐3‐5‐2.

Habit lateral position

change lane Time

Clustering

Clustering （a）change lane Time

（b）Habit lateral position Deviate from the road centerline

FIG. 1 EACH DRIVER CLUSTERING CHANGE LANE TIME AND MEAN VALUE OF HABIT LATERAL POSITION DEVIATE FROM THE ROAD CENTERLINE

The driver number ratio Clustering FIG. 2

THE DRIVER NUMBER RATIO OF EACH CLUSTER

According to the driver information provided by cluster analysis, statistical clustering driver each figure represents the ratio of the total number of clusters, Statistics each cluster driver numbers ratio. As shown in Figure 2, Cluster 1 and Cluster 4 have the lowest rate, followed by cluster 2, cluster 5, cluster 3 has the highest rate. This result is

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International Journal of Material and Mechanical Engineering (IJMME), Volume 5 2016 www.ijm‐me.org

consistent with the distribution of the driverʹs driving style. That is, the most conservative and the most radical drivers number are smaller, most drivers are gentle driving style, different clusters drivers do not have the same demand for security. Designing the lateral safety warning system, drivers belong to the cluster 4 and the cluster 1 should adopt a more secure system control strategy, for example, choose a more conservative hazard warning and automatic collision avoidance start threshold, and help with high security requirements driver as early as possible perception dangerous, ensure safely change lane. Drivers belong to the cluster 2 and the cluster 5, because of their driving style requirements, the system may use more efficient control strategies, in order to avoid system interference to the driver normal driving.

K‐means Clustering Analysis K‐means clustering algorithm is a basic analysis method and the most widely used classification algorithm, the method can handle large data sets, and has a high efficiency, the clustering feature is: each cluster itself should be as compact as possible, and separately as far as possible between the clusters. It is a known number of categories clustering algorithm. Specify the number of categories as k, cluster the sample collection, the results of clustering expressed by k clustering centers. Based on the given clustering objective function (or a clustering criterion), algorithm uses an iterative update method. Each iteration process is the direction of decreasing the value of the objective function, final clustering result achieved the minimum objective function value, achieve optimum clustering effect.

Followed is the class members list of k‐means clustering analysis. The 1st driver, the 2nd driver, the 3rd driver, the 4th driver, the 5th driver, the 7th driver, the 8th driver, the 11th driver, the 12th driver, the 13th driver, the 16th driver, the 17th driver, the 18th driver, the 21st driver, the 22nd driver, the 23rd driver, the 24th driver, the 25th driver, the 26th driver, the 27th driver, the 28th driver, the 29th driver, the 32nd driver, the 33rd driver, the 34th driver, the 35th driver, the 36th driver, the 37th driver belong to the first category, the 6th driver, the 19th driver, the 30th driver, the 31st driver the second category, the 9th driver, the 10th driver, the 14th driver, the 15th driver the third category, the 20th driver separately the fourth category, the 38th driver separately the fifth category.

The class membership numbers of five classes is: Class 1 has 2 drivers, Class 2 has 3 drivers, class 3 has 5 drivers, class 4 has 20 drivers, and Class 5 has 8 drivers.

Figure 3 shows each driver clustering change lane time and mean value of maximum lateral acceleration when change lane.

As can be seen from Figure 3, from the change lane time, the average distribution of each cluster is in good agreement with its clustering features. Combine Figure 3 (a) and 1 (b), it can be concluded from conservative to aggressive driving style clustering order: 2‐3‐4‐5‐1.

According to the driver information provided by cluster analysis, statistical clustering driver each figure represents the ratio of the total number of clusters, statistics each cluster driver numbers ratio. As shown in Figure 4, Cluster 2 and Cluster 3 have the lowest rate, followed by cluster 5, cluster 1, cluster 4 has the highest rate. This result is consistent with the distribution of the driverʹs driving style.

Habit lateral position

change lane Time Clustering

Clustering

（a）change lane Time

（b）Habit lateral position Deviate from the road centerline

FIG. 3 EACH DRIVER CLUSTERING CHANGE LANE TIME AND MEAN VALUE OF HABIT LATERAL POSITION DEVIATE FROM THE ROAD CENTERLINE

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The driver number ratio

FIG. 4

Clustering THE DRIVER NUMBER RATIO OF EACH CLUSTER

Determine the Class Number Class members’ comparison between hierarchical cluster analysis and k‐means clustering analysis are not exactly the same, according to the classification principle, adjusting some members of their class, the results are: the 2nd driver, the 7th driver, the 20th driver belong to the first category, the 9th driver, the 10th driver, the 14th driver, the 15th driver, the 38th driver belong to the second category, the 1st driver, the 3rd driver, the 4th driver, the 8th driver, the 11th driver, the 12th driver, the 13th driver, the 17th driver, the 18th driver, the 21st driver, the 22nd driver, the 25th driver, the 28th driver, the 29th driver, the 30th driver, the 32nd driver, the 35th driver, the 36th driver the third category, the 5th driver, the 16th driver, the 23rd driver, the 24th driver, the 26th driver, the 27th driver, the 34th driver, the 37th driver the fourth category, the 19th driver, the 31st driver the 33rd driver the fifth category.

Figure 5 shows driver clustering change lane time and habit lateral position, deviate mean value from the road centerline of the adjusted. And then, according to the driver information provided by adjusted cluster analysis, statistical clustering driver each figure represents the ratio of the total number of clusters, ss shown in Figure 6.

After the adjustment, the distance between the centers of various classes becomes larger, the ratios of the various classes are in line with the actual driverʹs driving style.

Habit lateral position

change lane Time Clustering

Clustering （b）Habit lateral position Deviate from the road centerline

（a）change lane Time

FIG. 5 EACH DRIVER CLUSTERING CHANGE LANE TIME AND MEAN VALUE OF HABIT LATERAL POSITION DEVIATE FROM THE ROAD CENTERLINE The driver number ratio

FIG. 6

Clustering THE DRIVER NUMBER RATIO OF EACH ADJUSTED CLUSTER

Conclusions Based on the driver real vehicle data, factor analyses, and cluster analysis, classify driver, compare Class members

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of hierarchical cluster analysis and k‐means clustering analysis; classification result of 38 drivers has been obtained, which provides reference for control strategies meeting the driver characteristics and different safety requirements.

ACKNOWLEDGMENT

This research was supported by The Importation and Development of High‐Caliber Talents Project of Beijing Municipal Institutions (Project No.: CIT&TCD201404093) REFERENCES

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