Eyes All Around: Design and Analysis of 360-Degree LiDAR Perception Using Equivariant Feature Learning in Unstructured Traffic
arXiv:2606.07626v1 Announce Type: cross 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 pi
The paper addresses a critical gap in autonomous driving perception for complex urban environments, pushing the boundaries of existing LiDAR technology to full 360-degree integration.
Improving 360-degree perception directly contributes to the safety and reliability of autonomous vehicles, a prerequisite for widespread adoption and regulatory approval.
This research moves LiDAR-based perception from limited field-of-view scenarios to comprehensive surround sensing, an essential step for fully autonomous operation in diverse traffic conditions.
- · Autonomous vehicle developers
- · LiDAR manufacturers
- · AI algorithm developers
- · Companies relying on less robust perception systems
Enhanced safety and performance of autonomous driving systems in complex urban settings.
Accelerated development and commercial deployment of Level 4 and Level 5 autonomous vehicles.
Increased public trust and societal acceptance of autonomous transport solutions.
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Read at arXiv cs.AI