EventDrive：面向视觉-语言驾驶智能的事件相机
EventDrive: Event Cameras for Vision-Language Driving Intelligence
将事件相机异步流与RGB帧通过多尺度事件金字塔和模态路由混合专家融合，为视觉语言模型提供高时序精度和运动感知，可借鉴到需要低延迟、高动态范围的多模态推理任务中。
原文依据 EventDrive-VLM introduces a multi-horizon event pyramid and a modality-routing mixture-of-experts to adaptively encode and fuse asynchronous and frame-based information
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论事件相机通过异步亮度变化感知世界，具有微秒级延迟和高动态范围，其运动保真度远超基于帧的传感器，并能捕捉传统曝光常遗漏的时序结构。这些特性使事件数据成为自动驾驶中RGB的有力补充，尤其在模糊、眩光和快速运动等基于帧的感知可能不可靠的场景下。然而，现有的事件感知视觉-语言模型仍局限于通用感知，未能揭示事件传感如何在整个驾驶闭环中贡献于推理与决策。我们提出EventDrive，一个大规模基准和模型套件，在感知、理解、预测和规划四个核心维度上统一事件流、RGB帧和语言监督，涵盖描述、结构化问答、定位、运动状态识别、轨迹预测和规划任务。在此基础上，EventDrive-VLM引入多尺度事件金字塔和模态路由混合专家，以自适应编码和融合异步与基于帧的信息用于下游推理。跨多样任务的综合评估表明，事件流在时序精度、运动感知和鲁棒性方面带来显著增益，将事件传感推向驾驶智能的中心。
英文原文摘要 1396 字符
Event cameras sense the world through asynchronous brightness changes with microsecond latency and high dynamic range, offering motion fidelity far beyond frame-based sensors and capturing temporal structure that conventional exposures often miss. These properties make events a powerful complement to RGB in autonomous driving, especially under blur, glare, and rapid motion, where frame-based perception can become unreliable. However, existing event-aware vision-language models remain limited to generic perception and do not reveal how event sensing contributes to reasoning and decision-making across the full driving loop. We present EventDrive, a large-scale benchmark and model suite that unifies event streams, RGB frames, and language supervision across four core dimensions: Perception, Understanding, Prediction, and Planning, covering captions, structured QA, grounding, motion-state recognition, trajectory forecasting, and planning tasks. Building on this foundation, EventDrive-VLM introduces a multi-horizon event pyramid and a modality-routing mixture-of-experts to adaptively encode and fuse asynchronous and frame-based information for downstream reasoning. Comprehensive evaluation across diverse tasks shows that event streams provide substantial gains in temporal precision, motion awareness, and robustness, bringing event sensing into the center of driving intelligence.