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.
翻译:在多试剂交通场景中,我们提出了一种与人类代理人进行风险意识规划的新办法。我们的方法考虑到公路上人类驾驶者行为的广泛范围,从诸如超速和超速等攻击性动作到慢速驾驶和符合最右车道等保守特征。在我们的方法中,我们从数据驱动的人类驾驶者行为模型(称为CMetric)到驾驶者对热带风险的偏好中学习了一张地图。然后,我们用游戏理论风险敏感规划者中衍生的风险偏好到模拟人类驾驶者和各种交通场景中自主车辆之间的风险意识互动。我们还在合并的场景中展示了我们的方法,我们的结果显示,从风险意识规划者那里获得的最后轨迹显示,从风险意识规划者那里获得的最后轨迹将产生理想的突发行为。特别是,我们的规划者在保持与驾驶者的更远的距离的同时,对驱动者的行为模式进行自我攻击性和保守的模拟驾驶者之间,根据我们的风险敏感规划者所生成的轨迹。我们还观察了人类驾驶者更频繁的车道变化的结果,最后我们比较了人类驾驶者的行为模式。