Review of driver behaviour modelling for highway on‐ramp merging

Abstract

Abstract Autonomous driving is an exciting research field that has received growing attention in recent years. One of the most challenging and safety‐critical driving situations is highway on‐ramp merging. Most decision‐making strategies that perform highway on‐ramp merging are designed, firstly, to reduce the risk of crashes and improve the safety metrics. However, even with the development of such advanced driving systems, human drivers will still be involved in road traffic. Human drivers have various driving styles and different reactions to other traffic participants on the highway on‐ramp. Understanding driver behaviors is essential for designing safe and efficient real‐world driving strategies. Therefore, this paper provides a unique systematic review of existing techniques for modelling driver behaviors at highway on‐ramps, which are critical locations for traffic safety and efficiency. The novelty of this review is that it proposes a new classification of current state‐of‐the art techniques. Each category of techniques involves a unique paradigm. For each category of approaches, fundamental concepts are examined together with their challenges and limitations, and an overview on practical implementation. Furthermore, and based on the classification and chronological order, current research trend is identified, i.e. “data‐driven approaches”. Some future research avenues and disparities are also discussed.

Keywords

• autonomous driving
• behavioural sciences
• human factors
• learning (artificial intelligence)