标题： 环境不确定性下的轨迹规划与有限样本安全保证 显示英文标题

标题： Trajectory Planning Under Environmental Uncertainty With Finite-Sample Safety Guarantees

摘要： 我们解决了一个由一组障碍物组成的环境中轨迹规划的问题，这些障碍物的位置具有不确定性和时变性。 不确定性使用广泛接受的高斯分布进行建模，从而得到一个概率约束程序。 然而与之前的方法不同，我们不假设对分布的矩有完全的了解，而是通过来自传感器或历史数据的有限样本对其进行估计。 我们推导了这些估计误差的紧密集中界限，以充分收紧概率约束程序。 因此，我们提供了对对应于名义但未知的矩的概率约束的可证明保证。 我们通过两个自主车辆轨迹规划的案例研究来说明我们的结果。 显示英文摘要

摘要： We tackle the problem of trajectory planning in an environment comprised of a set of obstacles with uncertain time-varying locations. The uncertainties are modeled using widely accepted Gaussian distributions, resulting in a chance-constrained program. Contrary to previous approaches however, we do not assume perfect knowledge of the moments of the distribution, and instead estimate them through finite samples available from either sensors or past data. We derive tight concentration bounds on the error of these estimates to sufficiently tighten the chance-constraint program. As such, we provide provable guarantees on satisfaction of the chance-constraints corresponding to the nominal yet unknown moments. We illustrate our results with two autonomous vehicle trajectory planning case studies.