Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision

TL;DR

This paper introduces AR2VP, a novel V2X perception framework utilizing roadside units for dynamic scene understanding, improving autonomous perception accuracy and robustness in changing environments.

Contribution

The paper proposes AR2VP, a new adaptive perception method using roadside units to handle intra- and inter-scene changes in dynamic V2X perception.

Findings

AR2VP outperforms existing methods in dynamic perception tasks.

It achieves better performance-bandwidth trade-offs.

The approach enhances robustness in changing environments.

Abstract

Vehicle-to-everything (V2X) perception is an innovative technology that enhances vehicle perception accuracy, thereby elevating the security and reliability of autonomous systems. However, existing V2X perception methods focus on static scenes from mainly vehicle-based vision, which is constrained by sensor capabilities and communication loads. To adapt V2X perception models to dynamic scenes, we propose to build V2X perception from road-to-vehicle vision and present Adaptive Road-to-Vehicle Perception (AR2VP) method. In AR2VP,we leverage roadside units to offer stable, wide-range sensing capabilities and serve as communication hubs. AR2VP is devised to tackle both intra-scene and inter-scene changes. For the former, we construct a dynamic perception representing module, which efficiently integrates vehicle perceptions, enabling vehicles to capture a more comprehensive range of dynamic factors within the scene.Moreover, we introduce a road-to-vehicle perception compensating module, aimed at preserving the maximized roadside unit perception information in the presence of intra-scene changes.For inter-scene changes, we implement an experience replay mechanism leveraging the roadside unit's storage capacity to retain a subset of historical scene data, maintaining model robustness in response to inter-scene shifts. We conduct perception experiment on 3D object detection and segmentation, and the results show that AR2VP excels in both performance-bandwidth trade-offs and adaptability within dynamic environments.