Safe and Non-Conservative Trajectory Planning for Autonomous Driving Handling Unanticipated Behaviors of Traffic Participants

TL;DR

This paper introduces a trajectory planning method for autonomous driving that balances efficiency and safety by adapting to traffic participant behaviors, minimizing collision probability, and handling unanticipated actions.

Contribution

It presents a novel control scheme combining SMPC with constraint violation probability minimization, improving safety guarantees in uncertain traffic scenarios.

Findings

Efficient trajectory planning with safety guarantees under uncertainty.

Reduced collision probability compared to previous methods.

Flexible adaptation to minimize harm in potential collisions.

Abstract

Trajectory planning for autonomous driving is challenging because the unknown future motion of traffic participants must be accounted for, yielding large uncertainty. Stochastic Model Predictive Control (SMPC)-based planners provide non-conservative planning, but do not rule out a (small) probability of collision. We propose a control scheme that yields an efficient trajectory based on SMPC when the traffic scenario allows, still avoiding that the vehicle causes collisions with traffic participants if the latter move according to the prediction assumptions. If some traffic participant does not behave as anticipated, no safety guarantee can be given. Then, our approach yields a trajectory which minimizes the probability of collision, using Constraint Violation Probability Minimization techniques. Our algorithm can also be adapted to minimize the anticipated harm caused by a collision. We provide a thorough discussion of the benefits of our novel control scheme and compare it to a previous approach through numerical simulations from the CommonRoad database.