Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively

• Main Authors: Lyu, Shengfei, Wang, Di, Yang, Xuehao, Miao, Chunyan
• Other Authors: College of Computing and Data Science
• Format: Article
• Language: English
• Published: 2024
• Subjects: Computer and Information Science; Driver profiling; Arbitrary roads
• Online Access: https://hdl.handle.net/10356/180908
• Institution: Nanyang Technological University

Driver profiling is a widely used tool in fleet management and driver-specific insurance because it differentiates drivers based on their driving behaviors, such as aggressive and non-aggressive, which correspond to different levels of driving risk. However, most existing driver profiling methods require all drivers to drive on the same predefined route or type of roads, simply to make sure their driving behaviors are comparable. This premise makes these methods not be able to profile drivers who drive on arbitrary roads, which constitute the real-world scenarios for most drivers. To enable the profiling of drivers using their naturalistic driving data, i.e., driving trajectories recorded while they were driving on arbitrary roads at their own free will, in this paper, we propose a novel method named cLustering rOads And Drivers Successively (LOADS). Specifically, LOADS first categorizes the roads into different types using the extracted characteristics of all drivers driving on the respective roads. It then groups drivers into different clusters to obtain their profile labels (e.g., aggressive or non-aggressive) using the extracted driving characteristics on each road type. We conduct extensive experiments using two real-world driving trajectory datasets comprising thousands of driving trajectories of hundreds of drivers. Statistical analysis results indicate that the driver groups identified by LOADS have significantly different driving styles. To the best of our knowledge, LOADS is the first method that focuses on profiling drivers who drive on arbitrary roads, showing a great potential to enable real-world driver profiling applications.