论文标题
具有多模式障碍不确定性预测的自动驾驶汽车的互动感运动计划
Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty Predictions
论文作者
论文摘要
本文提出了一种在不确定的多车辆交通环境中的自动驾驶汽车的互动和安全性运动计划方法。该方法集成了交互感知的多个模型Kalman滤波器（IAIMM-KF）预测周围车辆的交互式多模式操作的能力，以及在规划不确定动态环境中最佳轨迹的模型预测性控制（MPC）的优势。在计算参考目标和设计MPC的自我弹性运动计划的自我运动计划中，考虑了周围车辆的操纵和轨迹不确定性的多模式预测不确定性。 MPC通过合并可调参数来在安全限制的设计中调整预测的障碍占用率，从而实现安全意识，从而使预测的障碍物占用率在绩效和鲁棒性之间实现权衡。根据周围车辆的预测，MPC计算自我车辆的最佳参考轨迹，以遵循随时间变化的参考目标，并避免与障碍物发生碰撞。该方法的效率在挑战性的高速公路驾驶模拟方案和来自记录的流量数据集的驾驶场景中说明了。
This paper proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting multiple model Kalman filter (IAIMM-KF) to predict interactive multi-modal maneuvers of surrounding vehicles, and the advantage of model predictive control (MPC) in planning an optimal trajectory in uncertain dynamic environments. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in computing the reference targets and designing the collision-avoidance constraints of MPC for resilient motion planning of the ego vehicle. The MPC achieves safety awareness by incorporating a tunable parameter to adjust the predicted obstacle occupancy in the design of the safety constraints, allowing the approach to achieve a trade-off between performance and robustness. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by MPC to follow the time-varying reference targets and avoid collisions with obstacles. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset.
