发表机构
School of Intelligent Systems Engineering, Sun Yat-sen University; School of Intelligence Science and Engineering, Harbin Institute of Technology（中山大学智能系统工程学院; 哈尔滨工业大学智能科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高速公路夜间低光条件下RGB感知性能差的问题，提出JEAT框架，通过自适应扩展卡尔曼滤波器动态融合事件流与RGB帧，还创建SEHN数据集，为多模态融合研究提供支持。
AI 中文摘要
部署在高速公路上的智能交通系统主要依靠传统RGB相机进行交通感知和车辆跟踪。然而，高速公路环境存在独特挑战，夜间场景会出现运动模糊、曝光不足和信噪比低等问题，严重损害基于RGB传感系统的可靠性。为此提出联合事件-RGB自适应跟踪（JEAT）框架，它将异步事件流和RGB帧合并进行联合数据关联优化，采用自适应扩展卡尔曼滤波器动态加权融合两种模式。此外，鉴于缺乏适合不同环境条件的基于事件的高速公路感知公开数据集，提出了SEHN数据集。该数据集包含不同环境条件和交通密度，提供同步RGB图像和事件流以促进多模态融合研究。代码和数据集将在指定网址提供。
英文摘要
Intelligent Transportation Systems deployed on highways predominantly rely on conventional RGB cameras for traffic perception and vehicle tracking. However, highway environments present unique challenges: the absence of artificial lighting infrastructure, combined with high vehicle velocities, results in severely degraded perception performance under low-light conditions. Specifically, nighttime scenarios suffer from motion blur, insufficient exposure, and poor signal-to-noise ratios, which catastrophically impair the reliability of RGB-based sensing systems. To address these limitations, we propose a novel Joint Event-RGB Adaptive Tracking (JEAT) framework. Unlike existing multi-sensor trackers constrained by rigid, hard-coded prioritization, JEAT merges asynchronous event streams and RGB frames into a unified joint data association optimization. By employing an Adaptive Extended Kalman Filter to continuously estimate measurement noise via NIS statistics, the framework dynamically weights and fuses both modalities, optimally harnessing event streams during dark or high-speed motion while leveraging RGB frames under bright or static conditions. Furthermore, given the absence of publicly available datasets tailored for event-based highway perception with diverse environmental conditions, we present SEHN, a large-scale synthetic dataset generated using the CARLA simulator. Our dataset encompasses diverse environmental conditions (daytime, nighttime, nighttime with out artificial lighting) and varying traffic densities, providing synchronized RGB imagery and event streams to facilitate multi-modal fusion research. Our code and datasets will be available at https://github.com/haidongwang96/SEHN.
