Experimental Studies on Dynamics Performance of Lateral and Longitudinal Control for Autonomous Vehicle Using Image Processing Khalid Isa Department of Computer Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia P.O Box 101, 86400 Parit Raja, Batu Pahat, Johor, Malaysia halid@uthm.edu.my Abstract This paper presents a simulation of vehicle driving control system in terms of lateral and longitudinal control using image processing. The main contribution of this study is it contributes an algorithm of vehicle lane detection and tracking which based on colour cue segmentation, Canny edge detection and Hough transform. The algorithm gave good result in detecting straight and smooth curvature lane on highway even the lane was affected by shadow. Then by combining and processing the result of lane detection process with vehicle dynamics model, this system will produce the dynamics performance of vehicle driving control. This simulation system was divided into four subsystems: sensor, image processing, controller and vehicle. All the methods have been tested on video data and the experimental results have demonstrated a fast and robust system. 1. Introduction The main objective of this system is to develop the simulation for analysing dynamics performance of lateral and longitudinal control for autonomous vehicle using image processing. Therefore, the simulation determines the steering command for the vehicle lateral control by processing, analysing, and detecting the lane on highway. This means, the lane detection process will produce the lane angle, and this angle was directly used as steering command. Then, by combining the steering command and others vehicle dynamics parameters such as the vehicle mass, and vehicle velocity, the vehicle’s dynamics performance can be determined by this system. Previously, many lanes or road boundary detection algorithms have been developed. LOIS [1] system used a deformable template approach to find the best fit of road model whether it straight or curve. The research groups of the University Der Bundeswehr [2] and Daimler-Benz [3] base their road detection functionality on a specific road model: lane markings are modelled as clothoids. This model has the advantage that the knowledge of only two parameters allows the full localization of lane markings and the computation of other parameters like the lateral offset within the lane, the lateral speed with respect to the lane and the steering angle. The approach in [4] is an evolutionary approach of lane markings detection. It used collaborative autonomous agents to identify the lane markings in road images. Several aspects of designing control system for a vehicle have been examined extensively in the past, both in the physics literature [5] as well as in control theoretic studies. The control problem in a dynamic setting, using measurement ahead of the vehicle, has been explored by [6], who proposed a constant control law proportional to the offset from the centreline at a look-ahead distance. Ackermann et al [7], proposed a linear and non-linear controller design for robust steering. Taylor et al [8] considered the problem of controlling a motor vehicle based on the information obtained from conventional cameras mounted onboard. Ma, Kosecka and Sastry [9] looked at the problem of guiding a nonholonomic robot along a path based on visual input. 2. Problem Formulation The following subsection presents the basic system design and the techniques. 2.1 System Design System design for autonomous vehicle is depending on number of tasks that can be performed by the vehicle. Since the experiment only using one video camera as a sensor, so this paper only presented the lane detection task along with dynamics and control of IEEE 8th International Conference on Computer and Information Technology Workshops 978-0-7695-3242-4/08 $25.00 © 2008 IEEE DOI 10.1109/CIT.2008.Workshops.89 411