An Efficient Risk-aware Branch MPC for Automated Driving that is Robust to Uncertain Vehicle Behaviors
<|reference_start|>An Efficient Risk-aware Branch MPC for Automated Driving that is Robust to Uncertain Vehicle Behaviors: One of the critical challenges in automated driving is ensuring safety of automated vehicles despite the unknown behavior of the other vehicles. Although motion prediction modules are able to generate a probability distribution associated with various behavior modes, their probabilistic estimates are often inaccurate, thus leading to a possibly unsafe trajectory. To overcome this challenge, we propose a risk-aware motion planning framework that appropriately accounts for the ambiguity in the estimated probability distribution. We formulate the risk-aware motion planning problem as a min-max optimization problem and develop an efficient iterative method by incorporating a regularization term in the probability update step. Via extensive numerical studies, we validate the convergence of our method and demonstrate its advantages compared to the state-of-the-art approaches.<|reference_end|>
@article{zhang2024an,
    title={An Efficient Risk-aware Branch MPC for Automated Driving that is Robust
  to Uncertain Vehicle Behaviors},
    author={Luyao Zhang, George Pantazis, Shaohang Han and Sergio Grammatico},
    journal={arXiv preprint arXiv:2403.18695},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18695},
    primaryClass={eess.SY cs.RO cs.SY}
}