混合都市交通における社会的適合型自動運転のための相互作用認識型モデル予測意思決定

原文アブストラクト

Autonomous vehicles must negotiate with pedestrians in ways that are both safe and socially compliant. We present an interaction-aware model predictive decision-making (IAMPDM) framework that integrates a gap-acceptance-inspired intention model with MPC to jointly reason about human intent and vehicle control in real time. The pedestrian module produces a continuous crossing-propensity signal - driven by time-to-collision (TTC) with an intention discounting mechanism - that modulates MPC safety terms and minimum-distance constraints. We implement IAMPDM in a projection-based, motion-tracked simulator and compare it against a rule-based intention-aware controller (RBDM) and a conservative non-interactive baseline (NIA). In a human-in-the-decision-loop study with 25 participants, intention-aware methods shortened negotiation and completion time relative to NIA across scenarios, at the expense of tighter TTC/DST margins, with no significant difference between IAMPDM and RBDM except for TTC in one scenario. Results indicate that intention-aware decision-making algorithms reduce pedestrian crossing time and improve subjective ratings of comfort, safety, and trust relative to a non-cooperative decision-making algorithm. We discuss implications for real-world deployment of interaction-aware autonomous vehicles. We detail decision-making calibration and real-time implementation (CasADi/IPOPT) and propose deployment guardrails - minimum surrogate-safety margins, deadlock prevention - to balance efficiency with safety.