博弈论自动驾驶的硬件在环评估
Hardware-in-the-Loop Evaluation of Game-Theoretic Autonomous Driving
- Virginia Tech（弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文通过三阶段评估流程（包括物理QCar 2硬件在环实验），评估了基于纳什和斯塔克尔伯格博弈的自主交叉路口控制器，并强调了从仿真到物理实现时考虑传感不确定性的重要性。
AI中文摘要：
本文使用一个三阶段评估流程，评估了用于自主交叉路口通行的基于纳什和斯塔克尔伯格博弈的决策控制器，该流程最终在硬件在环（HIL）执行下进行了物理Quanser QCar 2实验。控制器在MATLAB/Simulink中实现，通过Quanser实时控制（QUARC）软件部署，并在车载NVIDIA Jetson AGX Orin处理器上执行。评估包括MATLAB数值仿真、Quanser交互实验室（QLabs）中的定性验证以及物理QCar 2实验。实验考虑了对称和非对称交叉路口接近方式、领导者-跟随者交互、冲突的斯塔克尔伯格角色分配以及非合作障碍车辆行为。结果表征了层级分配、障碍车辆行为和物理实现对所考虑的博弈论自动驾驶控制器的影响。软件仿真与硬件实验之间的比较进一步强调了在将博弈论控制器从仿真迁移到物理系统时，考虑传感和状态估计不确定性的重要性。QLabs仿真和物理QCar 2硬件实验的视频演示可在以下网址获取：此https URL。
英文摘要：
This paper evaluates Nash- and Stackelberg-based decision-making controllers for autonomous intersection crossing using a three-stage evaluation pipeline culminating in physical Quanser QCar 2 experiments with hardware-in-the-loop (HIL) execution. The controllers are implemented in MATLAB/Simulink, deployed through Quanser Real-Time Control (QUARC) software, and executed on the onboard NVIDIA Jetson AGX Orin processor. The evaluation includes MATLAB numerical simulation, qualitative validation in Quanser Interactive Labs (QLabs), and physical QCar 2 experiments. The experiments consider symmetric and asymmetric intersection approaches, leader-follower interactions, conflicting Stackelberg role assignments, and non-cooperative obstacle-vehicle behaviors. The results characterize the effects of hierarchy assignment, obstacle-vehicle behavior, and physical implementation on the considered game-theoretic autonomous driving controllers. Comparison between software simulations and hardware experiments further highlights the importance of accounting for sensing and state-estimation uncertainty when translating game-theoretic controllers from simulation to physical systems. A video demonstration of the QLabs simulations and physical QCar 2 hardware experiments is available at https://youtu.be/gkV6lz0twRk.
