D2-V2X: Depth-Driven Cooperative V2X Reasoning for Autonomous Driving
Quick Take
D2-V2X introduces a cooperative reasoning benchmark for V2X systems, enhancing occlusion handling in autonomous driving.
Key Points
- Features 8,500 triplets from multimodal sensors.
- Achieves 24.4% recall in identifying occluded hazards.
- Establishes a new baseline for VLM architectures.
Article Content
From source RSS / original summaryarXiv:2605. 24098v1 Announce Type: new Abstract: Single-vehicle Vision-Language Models (VLMs) are fundamentally constrained by sensor occlusions. While Vehicle-to-Everything (V2X) systems mitigate this, current benchmarks lack the cooperative reasoning required for resolving ambiguities in complex environments. We introduce D2-V2X, a spatially-aware Question-Rationale-Answer (QRA) benchmark featuring 8,500 triplets derived from multimodal vehicle and infrastructure sensors.
We additionally establish a baseline that aligns 3D LiDAR features with the VLM's latent space. By enforcing natural language Chain-of-Thought rationales prior to structured JSON outputs, our model is forced to explicitly articulate spatial relations. Our experiments demonstrate that grounding VLMs in cooperative LiDAR achieves 24. 4% recall in identifying occluded hazards compared to near-zero in zero-shot models and reduces spatial estimation error for visible objects by 77% compared to the zero-shot baseline.
While the model achieves a functional decision-making F1-score of 53. 5, we identify 3D-to-2D projection as a fundamental bottleneck in current VLM architectures, establishing a new baseline for future innovation. Data, code, and trained models available at https://github. com/KevinRichard1/D2-V2X
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