论文标题
随机模型预测控制具有自动驾驶安全保证：扩展版本
Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving: Extended Version
论文作者
论文摘要
自动化车辆需要有效且安全的计划，以在不确定的环境中操纵。这种不确定性在很大程度上是由其他交通参与者（例如周围车辆）引起的。周围车辆的未来运动通常很难预测。尽管强大的控制方法实现了自动化车辆的安全而保守的运动计划，但随机模型预测控制（SMPC）在不确定性存在下提供了有效的计划。应用概率约束以确保最大风险保持在预定义的水平以下。但是，由于可能会违反概率约束，因此无法确保安全性，这对于自动车辆是不可接受的。在这里，我们提出了一个有效的轨迹计划框架，并为自动化车辆提供安全保证。 SMPC用于获得有限地平线的有效车辆轨迹。基于第一个优化的SMPC输入，计划使用可触及的集合来确保安全的备份轨迹。如果需要，此备份用于覆盖SMPC输入。证明了安全SMPC算法的递归可行性。公路模拟显示了拟议方法在性能和安全性方面的有效性。
Automated vehicles require efficient and safe planning to maneuver in uncertain environments. Largely this uncertainty is caused by other traffic participants, e.g., surrounding vehicles. Future motion of surrounding vehicles is often difficult to predict. Whereas robust control approaches achieve safe, yet conservative motion planning for automated vehicles, Stochastic Model Predictive Control (SMPC) provides efficient planning in the presence of uncertainty. Probabilistic constraints are applied to ensure that the maximal risk remains below a predefined level. However, safety cannot be ensured as probabilistic constraints may be violated, which is not acceptable for automated vehicles. Here, we propose an efficient trajectory planning framework with safety guarantees for automated vehicles. SMPC is applied to obtain efficient vehicle trajectories for a finite horizon. Based on the first optimized SMPC input, a guaranteed safe backup trajectory is planned using reachable sets. This backup is used to overwrite the SMPC input if necessary for safety. Recursive feasibility of the safe SMPC algorithm is proved. Highway simulations show the effectiveness of the proposed method regarding performance and safety.
