Paper ID: 2310.19113

Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision

Jiayao Tan, Fan Lyu, Linyan Li, Fuyuan Hu, Tingliang Feng, Fenglei Xu, Rui Yao

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.

Submitted: Oct 29, 2023