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.

翻译：车联网（V2X）感知是一项创新技术，可提升车辆感知精度，从而增强自动驾驶系统的安全性与可靠性。然而，现有V2X感知方法主要基于车辆视觉聚焦于静态场景，这受到传感器能力与通信负载的限制。为使V2X感知模型适应动态场景，我们提出从道路到车辆视觉构建V2X感知，并引入自适应路车协同感知（AR2VP）方法。在AR2VP中，我们利用路侧单元提供稳定、广域感知能力，并作为通信中枢。AR2VP旨在同时应对场景内与场景间变化。针对场景内变化，我们构建动态感知表示模块，高效集成车辆感知信息，使车辆能够捕获场景内更全面的动态因素；同时引入路车感知补偿模块，旨在场景内变化时保留路侧单元最大化的感知信息。针对场景间变化，我们利用路侧单元的存储能力实现经验回放机制，保留历史场景数据子集，以维持模型对场景间偏移的鲁棒性。我们在3D目标检测与分割任务上开展感知实验，结果表明AR2VP在性能-带宽权衡及动态环境适应性方面均表现优异。