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

翻译：在自动驾驶智能体设计中以及理解自动驾驶车辆（AV）在实际运行中的行为时，平衡安全性、效率和交互性至关重要。本研究引入了一个经验学习框架，可直接从自然主义轨迹数据中推导出这些权衡关系。一个统一的目标空间通过安全性、效率和交互性的综合评分来表示每个自动驾驶车辆的时间步。应用帕累托占优来识别非支配状态，形成一个经验前沿，从而定义了平衡性能的可达区域。所提出的框架使用来自Foggy Bottom和I-395的第三代仿真（TGSIM）数据集进行了验证。结果表明，仅有0.23%的自动驾驶车辆驾驶实例是帕累托最优的，这突显了跨目标同时优化的罕见性。与非最优情况相比，帕累托最优状态在安全性、效率和交互性方面显示出显著更高的平均得分，其中交互性显示出最大的改进潜力。这种微创且模块化的框架仅需要运动学和位置数据，可直接应用于本研究范围之外，以推导和可视化多目标学习曲面。