Work-in-Progress: Achieving Fast Lane Detection of Autonomous Driving by CNN Based Differentiation

Lane detection is a challenging task in autonomous driving with strong requirements on real-time and high accuracy. Based on the theory of differential calculus, we propose a fast lane detection method for autonomous driving systems to achieve high accuracy and satisfy real-time performance. We formulate the lane differentiation to represent the lanes with a series of endpoints of line segments. A convolutional neural network is presented to predict the heatmap and the embedding vector simultaneously. The heatmap is used to represent the endpoints in lane differentiation while the embedding vector is leveraged to group endpoints into lanes. Then we implement a non-maximum suppression algorithm to combine the heatmap with the embedding vector to obtain the final lanes. Preliminary experimental results demonstrate the efficiency of our approach, which can achieve up to 43.1 times faster than traditional segmentation methods.