Enhanced navigation using computer vision-based steering angle calculation for autonomous vehicles

A key requirement in the development of intelligent and driverless vehicles is steering angle computation for efficient navigation. This paper presents a novel method for computing steering angle for driverless vehicles using computer vision-based techniques of relatively lower computing cost. The proposed system consists of four major stages. The first stage includes dynamic road region extraction using Gaussian mixture model and expectation maximization algorithm. The second stage is to compute the steering angle based on the extracted road region. Subsequently, Kalman filtering technique is used to cancel spurious angle transition noises. In addition, future steering angle is estimated which in turn gives informative feedback for smooth navigation of the vehicle. The proposed algorithm was tested both on a simulator and real-time images and was found to give a good estimation of actual steering angle required for navigation. Further, it was also observed that this works in different lighting conditions as well as for both structured and unstructured road scenarios.