A REAL-TIME ROAD LANE DETECTION SYSTEM FOR AUTONOMOUS VEHICLES USING COMPUTER VISION TECHNIQUES

Engineering notes

Autonomous vehicles have emerged as one of the most significant advancements in intelligent transportation systems.Among the various perception tasks required for autonomous navigation, road lane detection plays a critical role in ensuring vehicle stability, lane keeping, path planning, and safe driving operations.Reliable lane detection allows a vehicle to understand road geometry and maintain its position within designated lane boundaries.This project presents a real-time road lane detection system developed using computer vision techniques and implemented using Python and OpenCV.The proposed system processes road video frames captured from a forward-facing camera and applies a sequence of image processing operations including perspective transformation, color thresholding, morphological filtering, sliding-window lane search, polynomial curve fitting, lane region extraction, curvature estimation, vehicle offset measurement, and steering angle prediction.The developed system is capable of detecting lane boundaries under straight and moderately curved road conditions.A bird's-eye perspective transformation is used to simplify lane tracking, while adaptive lane fitting techniques provide stable lane estimation across successive frames.The detected lane region is projected back onto the original image and displayed in real time.Additional driving parameters such as lane curvature, vehicle offset from lane center, and steering guidance angle are computed to support autonomous driving applications.Experimental results demonstrate successful lane detection and tracking across multiple road scenarios.The system achieves stable performance in day light driving conditions and provides a practical foundation for Advanced Driver Assistance Systems (ADAS) and autonomous vehicle navigation.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Autonomous vehicles have emerged as one of the most significant advancements in intelligent transportation systems.Among the various perception tasks required for autonomous navigation, road lane detection plays a critical role in ensuring vehicle stability, lane keeping, path planning, and safe driving operations.Reliable lane detection allows a vehicle to understand road geometry and maintain its position within designated lane boundaries.This project presents a real-time road lane detection system developed using computer vision techniques and implemented using Python and OpenCV.The proposed system processes road video frames captured from a forward-facing camera and applies a sequence of image processing operations including perspective transformation, color thresholding, morphological filtering, sliding-window lane search, polynomial curve fitting, lane region extraction, curvature estimation, vehicle offset measurement, and steering angle prediction.The developed system is capable of detecting lane boundaries under straight and moderately curved road conditions.A bird's-eye perspective transformation is used to simplify lane tracking, while adaptive lane fitting techniques provide stable lane estimation across successive frames.The detected lane region is projected back onto the original image and displayed in real time.Additional driving parameters such as lane curvature, vehicle offset from lane center, and steering guidance angle are computed to support autonomous driving applications.Experimental results demonstrate successful lane detection and tracking across multiple road scenarios.The system achieves stable performance in day light driving conditions and provides a practical foundation for Advanced Driver Assistance Systems (ADAS) and autonomous vehicle navigation.

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