TY - GEN
T1 - Driver classification in vehicle following behavior by using dynamic potential field method
AU - Woo, Hanwool
AU - Ji, Yonghoon
AU - Tamura, Yusuke
AU - Kuroda, Yasuhide
AU - Sugano, Takashi
AU - Yamamoto, Yasunori
AU - Yamashita, Atsushi
AU - Asama, Hajime
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/3/14
Y1 - 2018/3/14
N2 - In this paper, a novel method is proposed to classify drivers in vehicle following behavior. The main contribution of this work is to construct a method to classify drivers as the fundamental model to consider characteristics of each driver under a scene that the target vehicle follows the preceding vehicle. Many methods have been proposed using data-driven approaches, however, each driver has an own driving style and shows different characteristics to be influenced by traffic conditions. As the result, the performance of previous methods to detect common patterns trained by machine learning techniques may realize the limitation. The proposed method extracts a new feature to describe a driving style by using a dynamic potential field method, and it can be a significant feature to classify drivers. It is demonstrated that our new feature dramatically improves the accuracy of driver classification through experimental results.
AB - In this paper, a novel method is proposed to classify drivers in vehicle following behavior. The main contribution of this work is to construct a method to classify drivers as the fundamental model to consider characteristics of each driver under a scene that the target vehicle follows the preceding vehicle. Many methods have been proposed using data-driven approaches, however, each driver has an own driving style and shows different characteristics to be influenced by traffic conditions. As the result, the performance of previous methods to detect common patterns trained by machine learning techniques may realize the limitation. The proposed method extracts a new feature to describe a driving style by using a dynamic potential field method, and it can be a significant feature to classify drivers. It is demonstrated that our new feature dramatically improves the accuracy of driver classification through experimental results.
UR - http://www.scopus.com/inward/record.url?scp=85046278273&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046278273&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2017.8317774
DO - 10.1109/ITSC.2017.8317774
M3 - Conference contribution
AN - SCOPUS:85046278273
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1
EP - 6
BT - 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
Y2 - 16 October 2017 through 19 October 2017
ER -