Stereo Vision Based Speed Estimation for Autonomous Driving

Human errors are considered to be one of the most common reasons for road accidents. Autonomous driving technology has the potential to reduce the number of accidents to a large extent by eliminating this error. Estimating the speed of vehicles is a capability of utmost importance for a self driving vehicle for accident free autonomous navigation. Hence speed estimation is an area which has been extensively researched in the recent years. This paper presents a novel, stereo vision based algorithm for the speed estimation problem. This algorithm involves detecting various obstacles on the road for two consecutive frames using YOLO followed by matching the features of the detected objects using Scale Invariant Feature Transform (SIFT). This algorithm also leverages the MC CNN architecture for finding the disparity map of the frames. The algorithm developed is applied on the KITTI dataset. Once calculated, the position and speed of the obstacles can then be fed to the path planning and control sub system for the decision making process. The estimated speed values are validated using the three dimensional object location data in the camera coordinates available in the KITTI dataset.