在密集、非结构化的城市交通中，由于道路使用者种类繁多、遮挡频繁、运动模式不规则以及道路布局缺乏标准化，感知任务仍是自动驾驶面临的主要挑战。尽管近期基于LiDAR的3D目标检测器在结构化驾驶场景中展现出较强性能，但多数模型均针对有限视场角（field of view）设置开发与评估，其在全向360度感知下的行为仍缺乏充分理解。本文研究面向自动驾驶的360度LiDAR感知流程，重点关注全景感知、方位角分扇区空间处理，以及复杂城市场景中的变换等变特征提取。论文提出一种实用的360度感知框架，将扇区级全景处理与旋转等变稀疏卷积相结合，并在自建Ouster OS0 LiDAR数据集上进行评估；该数据集采集自多样化的印度城市交通环境。实验结果表明，各类目标检测性能总体稳定：汽车检测精度最高（92.02/90.51），其次为公交车（80.53/76.34）和卡车（78.59/74.16）；而行人（67.45/61.02）、骑行者（73.21/69.54）及摩托车骑手（71.20/68.13）的检测精度较低，反映出在密集城市环境中对体型更小、形态更易变化的道路使用者进行检测的更大难度。

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.