This paper presents a survey, system, and evaluation of video-based lane estimation and tracking for driver assistance. The authors introduce the "video-based lane estimation and tracking" (VioLET) system, which uses steerable filters for robust and accurate lane-marking detection. The system is designed to detect and track lane markings under varying lighting and road conditions, and incorporates visual cues and vehicle-state information to improve curvature detection. The VioLET system is evaluated using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. The authors also present a comprehensive analysis of the current state of the art in lane-detection research, comparing a wide variety of methods and pointing out their similarities and differences. The paper also discusses the importance of road modeling, road-marking extraction, postprocessing, vehicle modeling, and common assumptions in lane-position detection and tracking systems. The VioLET system is designed for driver assistance and is capable of performing well under a wide variety of environments. The system is evaluated using various performance metrics, including mean absolute error, angular-deviation entropy, and angular-deviation histogram fraction. The authors conclude that the VioLET system provides a robust and accurate solution for lane-position detection and tracking in driver-assistance applications.This paper presents a survey, system, and evaluation of video-based lane estimation and tracking for driver assistance. The authors introduce the "video-based lane estimation and tracking" (VioLET) system, which uses steerable filters for robust and accurate lane-marking detection. The system is designed to detect and track lane markings under varying lighting and road conditions, and incorporates visual cues and vehicle-state information to improve curvature detection. The VioLET system is evaluated using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. The authors also present a comprehensive analysis of the current state of the art in lane-detection research, comparing a wide variety of methods and pointing out their similarities and differences. The paper also discusses the importance of road modeling, road-marking extraction, postprocessing, vehicle modeling, and common assumptions in lane-position detection and tracking systems. The VioLET system is designed for driver assistance and is capable of performing well under a wide variety of environments. The system is evaluated using various performance metrics, including mean absolute error, angular-deviation entropy, and angular-deviation histogram fraction. The authors conclude that the VioLET system provides a robust and accurate solution for lane-position detection and tracking in driver-assistance applications.