自动驾驶车辆必须以既安全又符合社会规范的方式与行人进行协商。本文提出一种交互感知模型预测决策（IAMPDM）框架，将受间隙接受理论启发的意图模型与模型预测控制（MPC）相融合，以实时联合推理人类意图与车辆控制。行人模块生成连续的过街倾向信号——该信号由到达碰撞时间（TTC）驱动，并引入意图衰减机制——用于动态调节MPC中的安全性约束项及最小距离约束。我们在基于投影法的运动追踪仿真器中实现了IAMPDM，并将其与基于规则的意图感知控制器（RBDM）及保守的非交互式基线方法（NIA）进行对比。在一项包含25名参与者的“人在决策环路中”实验中，意图感知类方法相较于NIA显著缩短了协商时间与任务完成时间，但以更小的TTC/行驶距离（DST）余量为代价；除某一场景中TTC存在显著差异外，IAMPDM与RBDM之间无统计学显著差异。结果表明，意图感知决策算法相较于非协作式决策算法可减少行人过街时间，并提升受试者对舒适性、安全性与信任度的主观评分。我们进一步探讨了交互感知自动驾驶车辆在真实世界部署中的意义，详细说明了决策算法的校准方法与实时实现方案（基于CasADi/IPOPT），并提出了部署保障措施——包括最小代理安全性余量与死锁预防机制——以在效率与安全性之间取得平衡。

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.