Structured Behavioral Utility Learning for Interactive Autonomous Driving

Interactive autonomous driving requires forecasts that are not only geometrically accurate but also informative for downstream decision making. Existing prediction–planning pipelines often optimize forecasting in motion space while planning reasons over interaction utility, creating a prediction–utility gap: geometrically similar predictions can still induce different downstream decisions. We propose HSP, a unified framework built on continuous behavioral objective coordinates, which place surrounding agents along structured safety, comfort, efficiency, and social-accommodation axes. Inferred from observed motion, these coordinates condition multimodal prediction, define a trajectory energy for gradient-based refinement, and shape agent-conditioned interaction utilities for ego planning. HSP further introduces a Prospect Value Model, motivated by prospect theory, to learn context-dependent nonlinear mappings from interaction utility to decision value. Experiments across five prediction and planning benchmarks demonstrate strong performance across diverse traffic settings. Under closely matched geometric prediction accuracy, HSP retains clear planning gains, indicating decision-relevant information beyond displacement error. Learned value functions further exhibit context-dependent nonlinear patterns, supporting nonlinear valuation beyond conventional linear utility evaluation.