Laser-scanner-based object state estimation and tracking control algorithms of autonomous truck using single-wheel driving module

Abstract

This paper describes state estimation and tracking control algorithms for use in an autonomous truck using a single-wheel driving module and simulation-based performance evaluation with actual data from a one-layer laser scanner. In order to construct the tracking control algorithm, the one-layer laser scanner has been used to detect the object as two-dimensional point cloud data. The tracking control algorithm consists of three routines—perception, decision, and control. In the perception routine, the coordinate transformation, downsizing, clustering, and state estimation have been conducted for calculation of the object states, such as position and velocity, using point data from the one-layer laser scanner. The velocity components of the object have been estimated based on the extended Kalman filter, and the desired straight path has been derived using the estimated velocity of the object. To track the object reasonably, lateral error and yaw angle error have been defined with respect to the desired straight path. The error dynamics has been derived using a single-wheel driving module, equipped with a planar truck model based on the vehicle dynamics. Using the derived planar truck model, the optimal steering input for tracking the object has been computed based on the linear quadratic regulator. The actual point cloud data from the one-layer laser scanner has been used to conduct reasonable performance evaluation of the tracking control algorithm proposed in this study. The actual data-based performance evaluation of the algorithm has been conducted in the MATLAB/SIMULINK environment with various scenarios. The performance evaluation results show that the tracking control algorithm proposed in this study can manipulate the single-wheel driving module of the autonomous truck to track the object soundly, based on the state estimation algorithm.