While driving a vehicle, the driver must pay attention to the environment around the vehicle. If the driving period increases, the driver loses his attention that would eventually lead to road accident. There are literatures which address the prevention of road accidents by considering several factors like environmental conditions, traffic density, psychological nature of the driver, etc. Among the factors, the detection of vehicle in front is considered as one of the road safety measures. In this paper, the datasets are collected from GTI vehicle image database and KITTI vision benchmark suite. The algorithm is developed for vehicle classification and the distance estimation by employing a conventional computer vision technique called Histogram of Oriented Gradients (HOG), combined with a machine learning algorithm called Support Vector Machine (SVM). The proposed algorithm could be implemented on autonomous vehicle system to assist the driver effectively and also reduce the vehicle collision. © BEIESP.