ElasticLaneNet: An Efficient Geometry-Flexible Lane Detection Framework

The task of lane detection involves identifying the boundaries of driving areas in real-time. Recognizing lanes with variable and complex geometric structures remains a challenge. In this paper, we explore a novel and flexible way of implicit lanes representation named Elastic Lane Map (ELM), and introduce an efficient physics-informed end-to-end lane detection framework, namely, ElasticLaneNet (Elastic interaction energy-informed Lane detection Network). The approach considers predicted lanes as moving zero-contours on the flexibly shaped ELM that are attracted to the ground truth guided by an elastic interaction energy-loss function (EIE loss). Our framework well integrates the global information and low-level features. The method performs well in complex lane scenarios, including those with large curvature turns, intersections, various crossing lanes, Y-shapes lanes, dense lanes, etc. We apply our approach on three datasets: SDLane, TuSimple and CULane. The results demonstrate exceptional performance of our method, with the state-of-the-art results on the structurally diverse SDLane, achieving F1-score of 89.51, Recall of 87.50, and Precision of 91.61 with fast inference speed.