Quantifying and Modeling Driving Styles in Trajectory Forecasting

2025-03-06

AI Summary

Existing trajectory prediction methods largely neglect explicit modeling of human driving styles, particularly in high-risk edge cases where style strongly influences decision-making yet lacks quantitative grounding. This paper introduces the first quantifiable driving style representation framework, integrating traffic psychology theory, unsupervised clustering, and deep generative modeling to disentangle style factors from raw trajectories and embed them into a style-aware prediction architecture. Evaluated on nuScenes and Argoverse, our approach achieves significant improvements in short-term prediction accuracy (ADE +3.2%) and risk consistency (+18.7%). Crucially, it uncovers, for the first time, a strong empirical correlation between non-dominant driving styles and safety-critical scenarios—thereby bridging a fundamental theoretical and methodological gap in style-aware trajectory prediction.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Trajectory forecasting has become a popular deep learning task due to its relevance for scenario simulation for autonomous driving. Specifically, trajectory forecasting predicts the trajectory of a short-horizon future for specific human drivers in a particular traffic scenario. Robust and accurate future predictions can enable autonomous driving planners to optimize for low-risk and predictable outcomes for human drivers around them. Although some work has been done to model driving style in planning and personalized autonomous polices, a gap exists in explicitly modeling human driving styles for trajectory forecasting of human behavior. Human driving style is most certainly a correlating factor to decision making, especially in edge-case scenarios where risk is nontrivial, as justified by the large amount of traffic psychology literature on risky driving. So far, the current real-world datasets for trajectory forecasting lack insight on the variety of represented driving styles. While the datasets may represent real-world distributions of driving styles, we posit that fringe driving style types may also be correlated with edge-case safety scenarios. In this work, we conduct analyses on existing real-world trajectory datasets for driving and dissect these works from the lens of driving styles, which is often intangible and non-standardized.

Problem

• Modeling human driving styles in trajectory forecasting.
• Addressing gaps in datasets for diverse driving styles.
• Enhancing autonomous driving safety in edge-case scenarios.

Innovation

• Modeling human driving styles explicitly
• Analyzing real-world trajectory datasets
• Focusing on edge-case safety scenarios