Deep reinforcement learning based lane detection and localization

Recently, deep-learning based lane detection methods effectively boost the development of Advanced Driver Assistance Systems (ADAS) and Self-Driving Systems. However, these methods only detect lane lines with sketchy bounding boxes while ignore the shape of specific curved lanes. To address the above problems, this paper introduces deep reinforcement learning into cursory lane detection models for accurate lane detection and localization. This model consists of two stages, namely the bounding box detector and landmark point localizer. To be specific, a bounding box level convolution neural network lane detector outputs the preliminary location of lanes in the form of bounding boxes. Then, a reinforcement based Deep Q-Learning Localizer (DQLL) accurately localizes the lanes as a group of landmarks to achieve better representation of curved lanes. Moreover, a pixel-level lane detection dataset named NWPU Lanes Dataset is constructed and released. It contains a variety of real traffic scenes and accurate masks of the lane lines. This approach achieves competitive performance in the released dataset and TuSimple Lane dataset. Furthermore, the codes and dataset will be released on https://github.com/tuzixini/DQLL.