A Review of Personalization in Driving Behavior: Dataset, Modeling, and Validation

Personalization in driving behavior research is crucial for developing intelligent vehicles that can safely coexist with human-driven vehicles in mixed-traffic environments. By accounting for the diversity of human driving behaviors, personalized modeling can improve predictive capabilities of intelligent vehicles and foster a more balanced traffic ecosystem. This paper presents a systematic review on personalization in driving behavior, evaluating their potential to enhance road safety, transportation efficiency, and human-centric mobility. It proposes a taxonomy to categorize personalized driving behaviors and surveys relevant datasets, modeling methodologies, and techniques for validating personalized driver models. Focusing on personalized driving behavior, the study emphasizes the need for intelligent vehicles to adapt to the complex and heterogeneous behaviors exhibited by human drivers to enhance predictability, responsiveness, and ultimately create a safe and efficient traffic environment. Lastly, key challenges are identified, along with promising future research directions to advance personalized driving behavior research.