Abstract
For obstacle avoidance against randomly moving traffic participants, stochastic model predictive control is promising. In crowded environments, however, feasible trajectories satisfying chance constraints do not necessarily exist; crowding induces a relaxation of constraints that causes deterioration of safety. To address this issue, we developed a velocity control method that decelerates the ego vehicle to a speed that satisfies the chance constraints on the prediction horizon. We conducted numerical simulations of obstacle avoidance and experiments of moving through a crowd comprising vehicles and pedestrians to evaluate the performance. The results indicate that the designed controller can generate a trajectory that mitigates the relaxation of constraints and adapts to various traffic participants.
