Real-time Model-based lane and vehicle detection

• Main Authors: Hsu-Jen Liu, 劉旭仁
• Other Authors: Din-Chang Tseng
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/80617901646049945738

碩士 === 國立中央大學 === 資訊工程研究所 === 92 === Abstract People pay attention on the safe driving more and more. The research on intelligent transportation systems (ITS) is quickly developed in recent years. The safe driving is one of the important subjects in the ITS. In this thesis, we propose a real-time model-based method for lane and vehicle detection for safe driving system. Our goal is to detect lane markings and front vehicle, and then provide lane departure warning based on the road images efficiently and effectively. In the lane detection, we exploit the property of human vision to enhance the difference map’s information such that the result of the lane detection is more effectively, and then propose a method for reduction of searching space in order to improve the detection efficiency. Moreover, we propose a multi-lane detection method. In the front vehicle detection, we exploit lane’s location as a searching region and define two adaptive threshold values to detect the front vehicle. Finally, we also exploit lane’s location and camera optical direction to estimate lateral offset of the vehicle with respect to the detected lane markers. Then the lane departure alarm is triggered by the decision of the estimation algorithm. In experiments, six-thousand images were processed to evaluate the system performance. The images were captured in variant weather conditions and with various driving situations. The rate of lane detection is over 98% and the processing time is about 0.033 seconds on average.