Visual Blind-spot Detection for Lane Change Assistance

• Main Authors: Yen-Hsiang Lin, 林彥翔
• Other Authors: Din-Chang Tseng
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/97510955129464197258

碩士 === 國立中央大學 === 資訊工程研究所 === 97 === Developing a real-time automotive driver assistant system for safety has emerged wide attention in recent years. When driving on the road, the fields of view beside the host vehicle for drivers are limited. Therefore, we utilize cameras mounted under side-view mirrors of a vehicle to monitor the circumstance in the blind-spot areas for drivers to avoid possible collision when changing lane. The proposed visual blind-spot detection system includes near lane mark detection, classification of solid/dashed lane mark, far lane mark estimation, side vehicle detection, distance estimation of side vehicles, vehicle tracking, and object-based motion analysis. In the proposed system, the lane mark at the near side of the host vehicle is detected by searching the optimal parameters of a defined lane model on the images, and the lane mark at the far side is estimated from the relative position of near lane mark by inverse perspective transform. Thus the proposed system is able to extract the region of the adjacent lane and detect the approaching vehicles. Side vehicles are detected by underneath shadow and left/right borders, and verified by the ratio of vehicle width and road width, symmetry, and gray-level variance of the vehicle region. We track the detected vehicles in consecutive images to acquire their relative positions between frames and compute their motion vectors. We analyze the motion vectors to judge if the vehicle is approaching the host vehicle, and the system will warn the driver if there is a vehicle approaching during the driver changes lane. In the experiments, we evaluate the proposed system in different weather conditions, such as cloudy day, sunny day, dusky day, and rainy day, and in different driving environments, such as highway, expressway, and urban roads. The average detection rate of vehicles in sunny day and cloudy day is about 92%, while the detection rate in rainy day is about 75%. The performance of the vehicle detection is not robust enough in bad weather condition, so finding other vehicle detecting method or fusing different sensor data is our future work.