  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.
