Model Predictive Control for Autonomous Driving considering Actuator Dynamics

TL;DR

This paper introduces a novel MPC approach for autonomous driving that incorporates actuator dynamics and uses an alternating minimization method to improve computational efficiency and safety during complex maneuvers.

Contribution

It presents a new MPC formulation with alternating minimization and explicit actuator dynamics integration, enhancing predictive accuracy and safety in autonomous driving.

Findings

Reduced computation time due to alternating minimization.

Improved inter-vehicle distance during maneuvers.

Enhanced trajectory smoothness and reduced velocity overshoot.

Abstract

In this paper, we propose a new model predictive control (MPC) formulation for autonomous driving. The novelty of our MPC stems from the following results. Firstly, we adopt an alternating minimization approach wherein linear velocities and angular accelerations are alternately optimized. We show that in contrast to the joint optimization, the alternating minimization exploits the structure of the problem better, which in turn translates to reduction in computation time. Secondly, our MPC explicitly incorporates the time dependent non-linear actuator dynamics that captures the transient response of the vehicle for a given commanded velocity. This added complexity improves the predictive component of MPC resulting in improved margin of inter-vehicle distance during maneuvers like overtaking, lane-change, etc. Although, past works have also incorporated actuator dynamics within MPC, there has been very few attempts towards coupling actuator dynamics to collision avoidance constraints through the non-holonomic motion model of the vehicle and analyzing the resulting behavior. We use a high fidelity simulator to benchmark our actuator dynamics augmented MPC with other related approaches in terms of metrics like inter-vehicle distance, trajectory smoothness, and velocity overshoot.