Learning the Pareto Space of Multi-Objective Autonomous Driving: A Modular, Data-Driven Approach

2026-01-26

AI Summary

This study addresses the multi-objective trade-off among safety, efficiency, and interactivity in autonomous driving. Leveraging naturalistic driving trajectory data, the authors propose a modular, data-driven framework that constructs a unified multi-objective space using only kinematic and positional information, without requiring intrusive behavioral modeling. By applying Pareto dominance relations, the framework identifies non-dominated states and empirically characterizes the Pareto front. Experiments on the NGSIM dataset reveal that merely 0.23% of observed driving instances are Pareto-optimal; however, these instances significantly outperform non-optimal samples across all three objectives, with the greatest potential for improvement observed in interactivity. These findings validate the effectiveness of the proposed approach in learning performance-balanced driving behaviors from real-world scenarios.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Balancing safety, efficiency, and interaction is fundamental to designing autonomous driving agents and to understanding autonomous vehicle (AV) behavior in real-world operation. This study introduces an empirical learning framework that derives these trade-offs directly from naturalistic trajectory data. A unified objective space represents each AV timestep through composite scores of safety, efficiency, and interaction. Pareto dominance is applied to identify non-dominated states, forming an empirical frontier that defines the attainable region of balanced performance. The proposed framework was demonstrated using the Third Generation Simulation (TGSIM) datasets from Foggy Bottom and I-395. Results showed that only 0.23\% of AV driving instances were Pareto-optimal, underscoring the rarity of simultaneous optimization across objectives. Pareto-optimal states showed notably higher mean scores for safety, efficiency, and interaction compared to non-optimal cases, with interaction showing the greatest potential for improvement. This minimally invasive and modular framework, which requires only kinematic and positional data, can be directly applied beyond the scope of this study to derive and visualize multi-objective learning surfaces

Problem

• multi-objective optimization
• autonomous driving
• Pareto optimality
• safety-efficiency-interaction trade-off
• naturalistic driving data

Innovation

• Pareto optimization
• multi-objective learning
• autonomous driving
• data-driven framework
• trajectory analysis