GPU-Parallelized Iterative LQR with Input Constraints for Fast Collision Avoidance of Autonomous Vehicles

Abstract

Collision avoidance in emergency situations is a crucial and challenging task in motion planning for autonomous vehicles. Especially in the field of optimization-based planning using nonlinear model predictive control, many efforts to achieve real-time performance are still ongoing. Among various approaches, the iterative linear quadratic regulator (iLQR) is known as an efficient means of nonlinear optimization. Additionally, parallel computing architectures, such as GPUs, are more widely applied in autonomous vehicles. In this paper, we propose 1) a parallel computing framework for iLQR with input constraints considering the characteristics of the problem and 2) a proper environmental formulation that can be covered with single-precision floating-point computation of the GPU. The GPU-accelerated framework was tested on a real-time simulation-in-the-loop system using CarMaker and ROS at a 20 Hz sampling rate on a low-performance mobile computer and was compared against the same framework realized with a CPU.