Motion planning for autonomous vehicles sharing the road with human drivers remains challenging. The difficulty arises from three challenging aspects: human drivers are 1) multi-modal, 2) interacting with the autonomous vehicle, and 3) actively making decisions based on the current state of the traffic scene. We propose a motion planning framework based on Branch Model Predictive Control to deal with these challenges. The multi-modality is addressed by considering multiple future outcomes associated with different decisions taken by the human driver. The interactive nature of humans is considered by modeling them as reactive agents impacted by the actions of the autonomous vehicle. Finally, we consider a model developed in human neuroscience studies as a possible way of encoding the decision making process of human drivers. We present simulation results in various scenarios, showing the advantages of the proposed method and its ability to plan assertive maneuvers that convey intent to humans.
翻译:与人驾驶员共享道路的自主车辆规划仍具有挑战性,困难来自三个具有挑战性的方面:人类驾驶员:1)多式驾驶员,2)与自主车辆互动,3)根据交通场现状积极作出决定;我们提议了一个以分处模型预测控制为基础的动议规划框架来应对这些挑战;通过考虑与人驾驶员不同决定相关的多重未来结果来解决多式问题;通过模拟人驾驶员作为受自主车辆行动影响的被动剂,将人的交互性视为具有反应性的剂;最后,我们认为人类神经科学研究开发的模型是将人类驾驶员的决策过程编码的一种可能的方法;我们提出模拟结果,显示拟议方法的优势及其规划向人类传递意图的果断动作的能力。