  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.
