DG-SSR: Dual-Granularity Structured Scene Retrieval for Autonomous Driving

Abstract

To address the inherent limitations of Vision-Language Models in long-tail object retrieval for autonomous driving, this paper proposes a Dual-Granularity Structured Scene Retrieval (DG-SSR) architecture. By decoupling text queries and visual features, we introduce a parameter-free mechanism that fuses local semantic scores with macro global context. An Adaptive Negative Injection (ANI) strategy and a Soft-NCE loss further enforce fine-grained alignment and mitigate color bias. Evaluated on a curated nuScenes dataset comprising 8,500 homogeneous street views and 1,128 combinatorial queries, our method achieves an mP@5 of 33.06% with a single-query latency of 0.812 ms, outperforming CLIP (16.48%) and BLIP (23.88%) by significant margins. Extensive ablation analysis demonstrates that optimal retrieval in complex scenes is achieved through a local-dominated architecture supplemented by minimal global context.