Problem
Research questions and friction points this paper is trying to address.
V2X
closed-loop evaluation
language-grounded supervision
end-to-end cooperative driving
visual-language models
Roadside-Cooperative Autonomous Driving: From Data Platform to Vision-Language End-to-End Reasoning
🤖 AI Summary
为解决V2X合作驾驶中闭环保证和语言监督不足的问题，本文通过构建V2XBench平台及AURORA框架，利用跨视图查询对齐与融合技术提升车辆在遮挡环境下的行驶性能。
📝 Abstract
Vehicle-to-Everything (V2X) cooperation enables beyond-line-of-sight perception, mitigating occlusions in single-vehicle sensing. However, existing V2X benchmarks provide limited support for closed-loop evaluation and language-grounded supervision, hindering the development of vision-language models (VLMs) for end-to-end cooperative driving. To address these limitations, we introduce V2XBench, a simulation platform featuring synchronized ego--roadside sensing and closed-loop evaluation, together with Chat-V2XBench, a progressively structured VQA dataset for cooperative reasoning. Building upon this benchmark infrastructure, we propose AURORA, an end-to-end cooperative driving framework. Equipped with a dual-view perception architecture, AURORA mitigates spatial and semantic discrepancies across ego and roadside viewpoints through a query-level Cross-View Query Alignment and Fusion (CQAF) module. Leveraging the resulting unified tokens, a LoRA-adapted VLM bridges semantic reasoning and generative trajectory planning. Extensive closed-loop evaluations on V2XBench demonstrate that AURORA achieves state-of-the-art performance in heavily occluded scenarios, with a Route Completion rate of 98.21% and a Driving Score of 76.02, while requiring low roadside communication bandwidth. Ultimately, this work pioneers an extensible V2X--VLM paradigm, paving the way for next-generation cooperative autonomous driving.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cross-View Query Alignment and Fusion (CQAF)
V2XBench
end-to-end cooperative driving
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