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：与人类驾驶员共享道路的自动驾驶汽车运动规划仍具挑战性。困难源于三个关键方面：人类驾驶员具有1）多模态性，2）与自动驾驶车辆的交互性，3）根据当前交通场景状态主动决策的特性。我们提出一种基于分支模型预测控制的运动规划框架来处理这些挑战。通过考虑人类驾驶员不同决策对应的多种未来结果，解决多模态性问题。通过将人类建模为受自动驾驶车辆行为影响的反作用主体，处理其交互特性。最后，借鉴人类神经科学研究中开发的模型，作为编码人类驾驶员决策过程的一种可行方案。我们在多种场景下的仿真结果表明，该方法具有优势，且能规划出向人类传递意图的果断动作。