🤖 AI Summary
This work addresses the challenges posed by unstructured urban traffic—such as heterogeneous road users, frequent occlusions, complex motion patterns, and non-standardized road layouts—by proposing a 360-degree LiDAR-based panoramic perception framework. The method integrates azimuth-aware sectorization with rotation-equivariant sparse convolutions to achieve robust 3D object detection in complex urban environments. As the first 360-degree perception system validated on real-world unstructured urban traffic data from India, it demonstrates strong performance, achieving AP scores of 92.02 and 90.51 for cars. While detection performance for pedestrians and cyclists is comparatively lower due to their small scale and high shape variability, the results remain practically viable for real-world deployment.
📝 Abstract
Perception in dense, unstructured urban traffic remains a major challenge for autonomous driving because of the wide variety of road users, frequent occlusions, irregular motion patterns, and the lack of standardized road layouts. Although recent LiDAR based 3D object detectors have shown strong performance in structured driving scenarios, most are developed and evaluated for limited field of view settings, and their behavior under full surround 360-degree sensing is still not well understood. This paper studies a 360-degree LiDAR perception pipeline for autonomous driving, with particular attention to panoramic sensing, azimuthal sector wise spatial processing, and transformation equivariant feature extraction in complex urban scenes. The paper presents a practical 360-degree perception framework that combines sector wise panoramic processing with rotation equivariant sparse convolutions and evaluates its behavior on a custom Ouster OS0 LiDAR dataset collected across diverse Indian urban traffic conditions. The results show generally stable detection across several object classes, with the strongest performance for cars at 92.02/90.51, buses at 80.53/76.34, and trucks at 78.59/74.16, while lower scores for pedestrians at 67.45/61.02, cyclists at 73.21/69.54, and motorcyclists at 71.20/68.13 reflect the greater difficulty of detecting smaller and more variable road users in dense urban scenes.