论文标题
具有概率安全保证的反应性运动计划
Reactive motion planning with probabilistic safety guarantees
论文作者
论文摘要
具有多个代理的环境中的运动计划对于许多重要的自动应用程序（例如自动驾驶汽车和辅助机器人）至关重要。本文考虑了运动计划的问题，其中受控代理与多个不受控制的代理人共享环境。首先，对不受控制的代理的预测模型进行了训练，以根据场景在短范围内预测所有可能的轨迹。然后，基于模型预测控制，预测将馈送到运动计划模块。我们证明了使用三种不同的方法，即散布后，支持向量机（SVM）和保形分析，证明了预测模型的概括，所有方法都可以生成随机保证预测变量的正确性。在模拟自主公路驾驶的情况下，在模拟中证明了所提出的方法。
Motion planning in environments with multiple agents is critical to many important autonomous applications such as autonomous vehicles and assistive robots. This paper considers the problem of motion planning, where the controlled agent shares the environment with multiple uncontrolled agents. First, a predictive model of the uncontrolled agents is trained to predict all possible trajectories within a short horizon based on the scenario. The prediction is then fed to a motion planning module based on model predictive control. We proved generalization bound for the predictive model using three different methods, post-bloating, support vector machine (SVM), and conformal analysis, all capable of generating stochastic guarantees of the correctness of the predictor. The proposed approach is demonstrated in simulation in a scenario emulating autonomous highway driving.
