Generating Comfortable, Safe and Comprehensible Trajectories for Automated Vehicles in Mixed Traffic

TL;DR

This paper presents a novel motion planning approach for automated vehicles in mixed traffic that emphasizes safety, convenience, and comprehensibility, using a probabilistic multi-agent model to achieve near-optimal, human-understandable trajectories.

Contribution

It introduces a probabilistic multi-agent motion planning framework that balances safety, convenience, and comprehensibility, outperforming existing methods in mixed traffic scenarios.

Findings

Provably safe in all situations

Produces human-understandable trajectories

Outperforms existing approaches in simulations

Abstract

While motion planning approaches for automated driving often focus on safety and mathematical optimality with respect to technical parameters, they barely consider convenience, perceived safety for the passenger and comprehensibility for other traffic participants. For automated driving in mixed traffic, however, this is key to reach public acceptance. In this paper, we revise the problem statement of motion planning in mixed traffic: Instead of largely simplifying the motion planning problem to a convex optimization problem, we keep a more complex probabilistic multi agent model and strive for a near optimal solution. We assume cooperation of other traffic participants, yet being aware of violations of this assumption. This approach yields solutions that are provably safe in all situations, and convenient and comprehensible in situations that are also unambiguous for humans. Thus, it outperforms existing approaches in mixed traffic scenarios, as we show in simulation.