  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 \textit{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 \textit{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, weak
geometry features at intersections, complicated cross lanes, Y-shapes lanes,
dense lanes, etc. We apply our approach on three datasets: SDLane, CULane, and
TuSimple. 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 rate of 87.50, and Precision of 91.61 with fast
inference speed.
