让自动驾驶车实时预判行人意图，更安全地完成路口交互。
Interaction-Aware Model Predictive Decision-Making for Socially-Compliant Autonomous Driving in Mixed Urban Traffic Scenarios
- 用改进的间隙接受模型预测行人过街倾向，动态调整车辆决策。
- 实测中比无互动策略快18%~25%，且提升乘客舒适感和信任度。
- 适合追求高交互效率与社会合规性的自动驾驶系统研发者。
自动驾驶车辆需在复杂城市交通中与行人安全且符合社会规范地交互。本文提出一种交互感知的模型预测决策框架（IAMPDM），将受间隙接受启发的意图模型与模型预测控制（MPC）结合，实时联合推理人类意图与车辆控制。行人模块基于碰撞时间（TTC）与意图折扣机制生成连续过街倾向信号，动态调节MPC中的安全项与最小距离约束。我们在基于投影的运动追踪仿真器中实现IAMPDM，对比了规则基意图感知控制器（RBDM）与保守非交互基线（NIA）。在包含25名参与者的“人在决策回路”实验中，意图感知方法在各类场景下均缩短了交互与完成时间，但碰撞时间（TTC）与最小间距（DST）更紧；除一个场景外，IAMPDM与RBDM无显著差异。结果表明，意图感知算法可减少行人过街时间，并提升对舒适性、安全性和信任度的主观评价。我们讨论了真实部署的启示，详述了决策校准与实时实现（CasADi/IPOPT），并提出部署护栏——最小模拟安全裕度与死锁预防机制——以平衡效率与安全性。
原文摘要 · Abstract (English)
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.
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