Scenario-based model predictive speed controller considering probabilistic constraint for driving scene with pedestrian

One of the difficult tasks associated with driving in suburban or urban roads is the interactions with pedestrians. One often finds it hard to judge the chances of a pedestrian or bicycle suddenly turning onto the driving path. These leads to a natural slow down response by the drivers. Since these responses are based on the risk feeling of driver, they are probabilistic in nature. This study shares a scenario-based model predictive control algorithm considering probabilistic constraint (SMPC-P) to handle such pedestrian interactions. An Interacting Multiple-Model Kalman Filter (IMM-KF) is used to predict the pedestrian path as multiple trajectories of independent probabilities. The task is formulated into a nonlinear MPC problem. We use a non-linear optimization solver named Interior Point OPTimizer(IPOPT). We introduce a modified form of inverse square root unit function to represent the collision probability into a deterministic function that is compatible with IPOPT. Having simulated it in MATLAB, the controller gives a very natural control behaviour for shared road driving compared to single scenario deterministic MPC.