RLM: A Vision-Language Model Approach for Radar Scene Understanding

Abstract

Radar sensors provide reliable perception across adverse weather, lighting, and long-range conditions, yet existing machine learning approaches remain fragmented and task-specific, with each downstream task employing distinct architectures and training objectives. We present RadarVLM, a vision-language framework that learns unified scene-level representations through structured spatial language supervision. Leveraging the CARLA simulator with a realistic radar model, we collect over 800k radar-caption pairs across 110+ hours of simulated driving in diverse scenarios. We make two key contributions: (1) a structured caption framework encoding vehicle distributions in the radar's native coordinate system, and (2) Spatially-Grounded CLIP (SG-CLIP) objective that replaces binary matching with continuous scene similarity, enabling fine-grained spatial reasoning. We further propose localization-aware evaluation metrics that directly assess spatial accuracy beyond traditional linguistic similarity measures. Validated on generative captioning and vehicle segmentation, SG-CLIP achieves up to 50% relative F1-score improvement over vanilla CLIP and a 21% AP gain on segmentation, demonstrating that language grounding produces spatially structured representations.