论文标题
在自动驾驶不确定性下的安全计划和控制
Safe planning and control under uncertainty for self-driving
论文作者
论文摘要
不确定性下的运动计划对于安全自动驾驶至关重要。在本文中，我们提出了一个统一的回避框架框架，该框架涉及1）自我车辆运动中的不确定性； 2）环境动态障碍的预测不确定性。在计划层中使用了包括短期和长期预测的两阶段交通轨迹预测器，以生成安全的自我车辆但不是保守的轨迹。预测模块与现有计划方法合作。我们的工作在Frenet框架计划者中展示了其有效性。使用管MPC的稳健控制器可确保在存在状态噪声和动态模型不确定性的情况下安全执行轨迹。高斯流程回归模型用于在线识别不确定性的界限。我们在Carla模拟器中展示了框架的有效性，安全性和实时性能。
Motion Planning under uncertainty is critical for safe self-driving. In this paper, we propose a unified obstacle avoidance framework that deals with 1) uncertainty in ego-vehicle motion; and 2) prediction uncertainty of dynamic obstacles from environment. A two-stage traffic participant trajectory predictor comprising short-term and long-term prediction is used in the planning layer to generate safe but not over-conservative trajectories for the ego vehicle. The prediction module cooperates well with existing planning approaches. Our work showcases its effectiveness in a Frenet frame planner. A robust controller using tube MPC guarantees safe execution of the trajectory in the presence of state noise and dynamic model uncertainty. A Gaussian process regression model is used for online identification of the uncertainty's bound. We demonstrate effectiveness, safety, and real-time performance of our framework in the CARLA simulator.
