Abstract: A clustering algorithm based on driver's collision avoidance steering behavior's characteristics is proposed according to driver's steering behavior data under different working conditions in this paper. Firstly a driving simulator is built, on which the driving behavior data are collected under the conditions of fixed radius steering, conventional lane change and emergency collision avoidance steering. The features of emergency collision avoidance steering are qualitatively analyzed by comparing the steering behavior data of normal driving with that in emergency collision avoidance conditions. Then the Pearson correlation coefficient method is used to analyze the correlation between the measurement variables of driver's steering behavior and emergency collision avoidance steering behavior, with a result showing that the steering condition is most correlated to the rotational speed of steering wheel. After that, with the rotational speed of steering wheel as the clustering characteristic parameter, clustering is conducted on steering behavior data by using K-means++ algorithm, and the steering behaviors are divided into normal steering (including fixed-radius steering and lane change steering) and emergency collision avoidance steering, achieving the recognition of emergency collision avoidance steering. Finally real vehicle verification test is performed and the results indicate that the K-means++ clustering algorithm proposed can effectively identify the steering behavior of driver for emergency collision avoidance with a clustering accuracy up to 96.7%

Key words: collision avoidance steering behavior, correlation analysis, K-means++ clustering, recognition