An Accurate and Computational Efficient System for Detecting and Classifying Ego and Sides Lanes Using LiDAR

this work, we are proposing a computationally efficient LiDAR based lane detection system that detects both ego and side lanes using 3D LiDARs. Our solution relies on the construction of local gird map around the ego vehicle using the infrared reflectance of combination of LiDARs. To fuse the information of the LiDARs into a map, the vehicle ego-motion is taken into account. The system is built using image processing by binarizing the map to extract the lane markers. The evaluation of computational performance of the final solution is realized on a single ARM core of the NVIDIA Drive PX2 without the need for the GPUs, and achieved a frame rate of 40 Hz. In the absence of a publicly available annotated dataset for LiDAR based lane detection, we evaluate the proposed solution against our proprietary camera based lane detection system. We observed a good correlation between the two in terms of Jaccard and Dice Coefficients.