A new method for detection of on-road vehicles based on color intensity segregation is proposed. This method has two steps. Firstly, details such as pavements or lanes in the image frame are utilized to extract the region of interest. Secondly, a new filter is proposed that utilizes the intensity information to filter the illumination variations, shadows and cluttered backgrounds from the extracted region of interest and detect the vehicles subsequently. The proposed method is evaluated on videos from KITTI vision benchmark suite and on our videos recorded during cloudy and rainy days with variable resolution. The experimental results demonstrate the effectiveness of the proposed vehicle detection method by achieving 90% detection rate and also reduced computational load on hardware, making it suitable for real-time applications.