论文标题

游戏理论计划,以自动驾驶风险意识的人类司机

Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers

论文作者

Chandra, Rohan, Wang, Mingyu, Schwager, Mac, Manocha, Dinesh

论文摘要

我们为在多代理交通情况下与人类代理商一起提供了一种新颖的风险计划计划方法。我们的方法考虑了道路上各种各样的人类驾驶员行为,从诸如超速和超车之类的侵略性动作到保守的特征,例如缓慢行驶并符合最正确的车道。在我们的方法中,我们从数据驱动的人类驾驶员行为模型中学习了一个名为Cmetric的映射到驾驶员的熵风险偏好。然后,我们在游戏理论风险敏感的计划者中使用派生的风险偏好来建模人类驾驶员之间的风险感知相互作用和在各种交通情况下的自动驾驶汽车。我们在合并方案中演示了我们的方法,我们的结果表明,从风险意识的计划者获得的最终轨迹产生了理想的紧急行为。特别是,我们的计划者认识到积极的人类驱动力和屈服,同时与他们保持更大的距离。在一项用户研究中,参与者能够根据我们对风险敏感的计划者产生的轨迹来区分侵略性和保守的模拟驱动程序。我们还观察到,侵略性的人类驾驶会导致计划者更频繁地改变车道。最后,我们将修改后的风险策划者的性能与现有方法进行了比较,并表明建模人类驾驶员行为会导致更安全的导航。

We present a novel approach for risk-aware planning with human agents in multi-agent traffic scenarios. Our approach takes into account the wide range of human driver behaviors on the road, from aggressive maneuvers like speeding and overtaking, to conservative traits like driving slowly and conforming to the right-most lane. In our approach, we learn a mapping from a data-driven human driver behavior model called the CMetric to a driver's entropic risk preference. We then use the derived risk preference within a game-theoretic risk-sensitive planner to model risk-aware interactions among human drivers and an autonomous vehicle in various traffic scenarios. We demonstrate our method in a merging scenario, where our results show that the final trajectories obtained from the risk-aware planner generate desirable emergent behaviors. Particularly, our planner recognizes aggressive human drivers and yields to them while maintaining a greater distance from them. In a user study, participants were able to distinguish between aggressive and conservative simulated drivers based on trajectories generated from our risk-sensitive planner. We also observe that aggressive human driving results in more frequent lane-changing in the planner. Finally, we compare the performance of our modified risk-aware planner with existing methods and show that modeling human driver behavior leads to safer navigation.

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