Stochastic MPC with Dual Control for Autonomous Driving with Multi-Modal Interaction-Aware Predictions

2022/08/06 by Nair, Siddharth H., Govindarajan, Vijay, Lin, Theresa +3
#FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2208.03525

Abstract

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.

Related

• Predictive Control for Autonomous Driving with Uncertain, Multi-modal Predictions
• Stochastic MPC with Multi-modal Predictions for Traffic Intersections
• Interaction-aware Model Predictive Control for Autonomous Driving
• Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions
• Collision Avoidance with Stochastic Model Predictive Control for Systems\n with a Twofold Uncertainty Structure