Roadside-Cooperative Autonomous Driving: From Data Platform to Vision-Language End-to-End Reasoning

• URL: http://arxiv.org/abs/2608.21032v1
• Date: Fri, 21 Aug 2026 12:29:27 GMT
• Title: Roadside-Cooperative Autonomous Driving: From Data Platform to Vision-Language End-to-End Reasoning
• Abstract summary: AURORA is an end-to-end cooperative driving framework.<n>AURORA mitigates spatial and semantic discrepancies across ego and roadside viewpoints.<n>LoRA-adapted VLM bridges semantic reasoning and generative trajectory planning.
• Score: 19.188066393763407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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