Efficient Lateral Lane Position Sensing using Active Contour Modeling

Abstract

As research into autonomous vehicles and Advanced Driver Assistance Systems (ADAS) has grown, research into computer vision techniques to detect objects and lane lines within images has also grown. The heavier computational load of modern techniques involving neural net- works and machine learning limits the ability to downscale to cheaper, less computationally- capable platforms when needed. The goal of the project is to develop a robust and computationally efficient method to estimate vehicle position within a lane. A clothoid lane line model based in real-world coor- dinates is projected into the image pixel-space where a novel approach to image segmentation and active contour modeling is performed. Another novel approach presented is the use of velocity as an input from a source outside the algorithm into the process to predict the initial conditions of the model in the next frame, rather than using the algorithm to produce an estimate of the velocity as an output to other systems. Validation is performed using the TuSimple dataset using both ideal and realistic scenarios to evaluate the performance of the various aspects of the algorithm against the current state-of-the-art methods.