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This nal report describes the application of computer vision techniques to the lateral and longitudinal control of an autonomous highway vehicle. In the part of the project we focused on an analysis of the vehicle's lateral dynamics and the design of an appropriate controller for lateral control and investigated various static feedback strategies where the measurements obtained from vision, namely o set from the centerline and angle between the road tangent and the orientation of the vehicle at some look-ahead distance, are directly used for control. The role of the look-ahead, its relation to the vision processing delay, longitudinal velocity and road geometry was crucial on the design of the control and their experimental evaluation. We carried out a thorough analysis of the e ects of changing various important system parameters like the vehicle velocity, thelookahead range of the vision sensor and the processing delay associated with the perception and control systems. We also present the results of a series of experiments that were designed to provide a systematic comparison of a number of control strategies. The control strategies that were explored include a lead-lag control law, a full- state linear controller and input-output linearizing control law. Each of these control strategies was implemented and tested at highway speeds on our experimental vehicle platform, a Honda Accord LX sedan. For the longitudinal control problem, we investigated the possibility of using stereo vision to provide the range information, in conjunction with a scanning laser radar sensor. The vision based tracking system utilizes a layered architecture wherein the bottom layer computes motion in both images using a simple correlation algorithm, and the upper level performs stereo xation and reconstruction using an algorithm designed for active vision systems. We present some initial results comparing the quality of range measurements provided by a vision system with the laser radar system. We report the results from the experimental demonstration of the system as part of the National Automated Highway Systems Consortium (NAHSC) Demonstration which took place in August 1997 in San Diego. The overall system was demonstrated as a part of the main highway scenario as well as part of a small public demonstration of the vision based lateral control on a highly curved test track.

This report describes progress in the application of computer vision techniques to the lateral and longitudinal control of an autonomous highway vehicle. A vehicle's lateral dynamics and the design of an appropriate controller for lateral control are investigated. Stereo vision, in conjunction with a scanning laser radar sensor, are studied for providing range information applicable to the longitudinal control problem. The results from the experimental demonstration of this system are reported as part of the National Automated Highway Systems Consortium (NAHSC) Demonstration that took place in San Diego in August 1997.

This correspondence presents a new technique for calibrating a camera mounted on a controllable head/eye platform. It uses the trajectories of an arbitrary number of tracked corner features to improve the calibration parameter estimates over time, utilizing a novel variable state dimension form of recursive filter. No special visual stimuli are required and no assumptions are made about the structure of the scene, other than that it is stationary relative to the head. The algorithm runs at 4 frames per second on a single Inmos T805 transputer, and is fully integrated into a real-time active vision system. Updated calibration parameters are regularly passed to the vision modules that require them. Although the algorithm requires an initial estimate of camera focal length, results are presented from real experiments demonstrating that convergence is achieved for initial errors up to 50%. Index Terms - Camera calibration, active vision, real-time vision, recursive filter.