Where to Turn: Road Fork Detection in Sparse 3D Point Cloud

Intersection detection plays a crucial role in localizing and planning the path of autonomous vehicles in urban environments. This paper presents a novel approach, PVWO, for adaptive intersection detection in autonomous vehicles equipped with 3D LiDAR. Firstly, the algorithm removes ground and obstacles effectively, and eliminates noise points. Secondly, a polyarticular viewpoints beam model is applied to detect the type and location of intersections. Thirdly, a linear feature extraction approach in a rolling window is proposed to detect road width and optimize key parameters, enhancing model robustness across different scenes. Compared to parallel intersection detection algorithms, our method exhibits excellent performance under diverse road conditions. Experiments in sparse point clouds show an average precision exceeding 89% and an average processing time of approximately 88 ms/frame. These results demonstrate that our method can accurately detect intersections in real-time, across various road structures.