Dynamic clustering analysis for driving styles identification. (January 2021)

• Record Type: Journal Article
• Authors: de Zepeda, Maria Valentina Niño; Meng, Fanlin; Su, Jinya; Zeng, Xiao-Jun; Wang, Qian
• Is Part Of: Engineering applications of artificial intelligence. Volume 97(2021)
• Publication Date: 2021-01
• Subjects: Dynamic clustering analysis -- Driving style -- Mixed driving environment -- Vehicle trajectory; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.104096
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.