Multimodal Information Retrieval for Autonomous Driving Scene Understanding with Cross-Modal Semantic Ranking
Keywords:
multimodal information retrieval; autonomous driving; cross-modal semantic ranking; scene understanding; edge computing; AI governance; robustness; fairness; vehicular networks; retrieval-augmented systemsAbstract
Autonomous driving scene understanding increasingly depends not only on real-time perception but also on the capacity to retrieve, align, and rank heterogeneous information across cameras, lidar, radar, maps, vehicle telemetry, and external infrastructure. This paper examines multimodal information retrieval as a system-level capability for autonomous driving, with particular attention to cross-modal semantic ranking as a mechanism for converting noisy candidate scenes, objects, events, and contextual records into decision-relevant evidence. The discussion positions retrieval as a socio-technical infrastructure rather than a standalone machine learning module. It analyzes architectural layers spanning sensing, embedding, indexing, candidate generation, reranking, and downstream planning interfaces. The paper then considers how cross-modal semantic ranking can improve robustness under occlusion, adverse weather, and sensor degradation while introducing new dependencies related to latency, bandwidth, model maintenance, and auditability. Deployment trade-offs across edge, vehicle, and cloud environments are examined through the lenses of sustainability, resilience, privacy, and fleet-scale governance. The paper also explores fairness and policy implications, including representational bias, liability allocation, certification, and public accountability for retrieval-supported driving decisions. Evaluation challenges are discussed, including benchmark fragmentation, missing modality scenarios, ranking stability, and the need for interpretable evidence chains. The central argument is that multimodal retrieval for autonomous driving must be designed as a governed, resource-aware, and safety-critical information system, not merely as a performance-oriented vision-language pipeline. Cross-modal semantic ranking offers a promising path toward contextual scene understanding, but its benefits depend on disciplined architecture, transparent governance, and robust operational practices.
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