Interaction-aware Model Predictive Control for Autonomous Driving

We propose an interaction-aware stochastic model predictive control (MPC) strategy for lane merging tasks in automated driving. The MPC strategy is integrated with an online learning framework, which models a given driver’s cooperation level as an unknown parameter in a state-dependent probability distribution. The online learning framework adaptively estimates the surrounding vehicle’s cooperation level with the vehicle’s past state trajectory and combines this with a kinematic vehicle model to predict the distribution of a multimodal future state trajectory. Learning is conducted using logistic regression, enabling fast online computations. The multi-future prediction is used in the MPC algorithm to compute the optimal control input while satisfying safety constraints. We demonstrate our algorithm in an interactive lane changing scenario with drivers in different randomly selected cooperation levels.