Geometrically Sound LANE

Geometrically sound lane detection and modeling are crucial for autonomous driving and traffic simulation, focusing on accurately representing vehicle paths within lanes, even under challenging conditions like sparse markings or complex lane geometries. Current research employs diverse approaches, including graph-based search algorithms with geometric constraints, neural networks (e.g., Graph Attention Networks, curved guide line networks), and stochastic models to capture vehicle lateral movement. These advancements improve the accuracy and efficiency of lane detection and trajectory prediction, ultimately enhancing the safety and reliability of autonomous vehicles and traffic management systems.