We propose a Stochastic MPC (SMPC) approach for autonomous driving which incorporates multi-modal, interaction-aware predictions of surrounding vehicles. For each mode, vehicle motion predictions are obtained by a control model described using a basis of fixed features with unknown weights. The proposed SMPC formulation finds optimal controls which serves two purposes: 1) reducing conservatism of the SMPC by optimizing over parameterized control laws and 2) prediction and estimation of feature weights used in interaction-aware modeling using Kalman filtering. The proposed approach is demonstrated on a longitudinal control example, with uncertainties in predictions of the autonomous and surrounding vehicles.
@article{arxiv.2208.03525,
title = {Stochastic MPC with Dual Control for Autonomous Driving with Multi-Modal Interaction-Aware Predictions},
author = {Siddharth H. Nair and Vijay Govindarajan and Theresa Lin and Yan Wang and Eric H. Tseng and Francesco Borrelli},
journal= {arXiv preprint arXiv:2208.03525},
year = {2022}
}