Risk-Aware Fast Trajectory Planner for Uncertain Environments Based on Probabilistic Surrogate Reliability and Risk Contours

This paper presents the risk-aware fast trajectory planner (RAFTER) for autonomous vehicles in dynamic uncer- tain environments, which is based on the probabilistic surrogate reliability of other traffic participants and risk contours. In contrast to the conventional risk metric, RAFTER not only provides the upper bound of the probability of constraint violation but deduces an infimum on the risk which the controlled plant can stand by the probabilistic reliable surrogate model. Such a risk-aware algorithm is capable of perceiving uncertainty and handling robustness. A series of covering disks are constructed utilizing a concise geometric configuration for a lower conservatism representation of the vehicle profile, which attains a desirable tradeoff between the quantity and area of occupation. Safe travel corridors are built on the dilated map via covering disks, significantly reducing the computational burden of a reliability-based optimization procedure for optimal trajectory planning. The effectiveness of the proposed method is confirmed by two numerical simulations derived from real scenarios.