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.
Scenario-based model predictive speed controller considering probabilistic constraint for driving scene with pedestrian
2020-09-20
1079867 byte
Conference paper
Electronic Resource
English
Probabilistic speed–density relationship for pedestrian traffic
Online Contents | 2016
|Research on combined driving assistant system based on pedestrian crossing scene
British Library Conference Proceedings | 2023
|