Real-Time Lane Estimation Using Deep Features and Extra Trees Regression

In this paper, we present a robust real-time lane estimation algorithm by adopting a learning framework using the convolutional neural network and extra trees. By utilising the learning framework, the proposed algorithm predicts the ego-lane location in the given image even under conditions of lane marker occlusion or absence. In the algorithm, the convolutional neural network is trained to extract robust features from the road images. While the extra trees regression model is trained to predict the ego-lane location from the extracted road features. The extra trees are trained with input-output pairs of road features and ego-lane image points. The ego-lane image points correspond to Bezier spline control points used to define the left and right lane markers of the ego-lane. We validate our proposed algorithm using the publicly available Caltech dataset and an acquired dataset. A comparative analysis with a baseline algorithms, shows that our algorithm reports better lane estimation accuracy, besides being robust to the occlusion and absence of lane markers. We report a computational time of 45 ms per frame. Finally, we report a detailed parameter analysis of our proposed algorithm.