TY - JOUR
T1 - Dynamic clustering analysis for driving styles identification
AU - de Zepeda, Maria Valentina Niño
AU - Meng, Fanlin
AU - Su, Jinya
AU - Zeng, Xiao Jun
AU - Wang, Qian
PY - 2021/1/1
Y1 - 2021/1/1
N2 - For intelligent driving systems, the ability to recognize different driving styles of surrounding vehicles is crucial in determining the safest, yet more efficient driving decisions especially in the context of the mixed driving environment. Knowing for instance if the vehicle in the adjacent lane is aggressive or cautious can greatly assist in the decision making of ego vehicle in terms of whether and when it is appropriate to make particular manoeuvres (e.g. lane change). In addition, vehicles behave differently under different surrounding environments, making the driving styles identification highly challenging. To this end, in this paper we propose a dynamic clustering based driving styles identification and profiling approach where clusters vary in response to the changing surrounding environment. To better capture dynamic driving patterns and understand the driving style switch behaviours and more complicated driving patterns, a position-dependent dynamic clustering structure is developed where a driver is assigned to a cluster sequence rather than a single cluster. To the best of our knowledge, this is the first research paper of its kind on the dynamic clustering of driving styles. The usefulness of the proposed method is demonstrated on a real-world vehicle trajectory dataset where results show that driving style switches and more complex driving behaviours can be better captured. The potential applications in intelligent driving systems are also discussed.
AB - For intelligent driving systems, the ability to recognize different driving styles of surrounding vehicles is crucial in determining the safest, yet more efficient driving decisions especially in the context of the mixed driving environment. Knowing for instance if the vehicle in the adjacent lane is aggressive or cautious can greatly assist in the decision making of ego vehicle in terms of whether and when it is appropriate to make particular manoeuvres (e.g. lane change). In addition, vehicles behave differently under different surrounding environments, making the driving styles identification highly challenging. To this end, in this paper we propose a dynamic clustering based driving styles identification and profiling approach where clusters vary in response to the changing surrounding environment. To better capture dynamic driving patterns and understand the driving style switch behaviours and more complicated driving patterns, a position-dependent dynamic clustering structure is developed where a driver is assigned to a cluster sequence rather than a single cluster. To the best of our knowledge, this is the first research paper of its kind on the dynamic clustering of driving styles. The usefulness of the proposed method is demonstrated on a real-world vehicle trajectory dataset where results show that driving style switches and more complex driving behaviours can be better captured. The potential applications in intelligent driving systems are also discussed.
KW - Driving style
KW - Dynamic clustering analysis
KW - Mixed driving environment
KW - Vehicle trajectory
UR - http://www.scopus.com/inward/record.url?scp=85097074685&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2020.104096
DO - 10.1016/j.engappai.2020.104096
M3 - Article
AN - SCOPUS:85097074685
SN - 0952-1976
VL - 97
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 104096
ER -