Given the rapid expansion of car ownership worldwide, vehicle safety is an increasingly critical issue in the automobile industry. The reduced cost of cameras and optical devices has made it economically feasible to deploy front-mounted intelligent systems for visual-based event detection. Prior to vehicle event detection, detecting vehicles robustly in real time is challenging, especially conducting detection process in images captured by a dynamic camera. Therefore, in this paper, a robust vehicle detector is developed. Our contribution is three-fold. Road modeling is first proposed to confine detection area for maintaining low computation complexity and reducing false alarms as well. Haar-like features and eigencolors are then employed for the vehicle detector. To tackle the occlusion problem, chamfer distance is used to estimate the probability of each individual vehicle. AdaBoost algorithm is used to select critical features from a combined high dimensional feature set. Experiments on an extensive dataset show that our proposed system can effectively detect vehicles under different lighting and traffic conditions, and thus demonstrates its feasibility in real-world environments.