CaR1: A Multi-Modal Baseline for BEV Vehicle Segmentation via Camera-Radar Fusion

Camera-radar fusion offers a robust and cost-effective alternative to LiDAR-based autonomous driving systems by combining complementary sensing capabilities: cameras provide rich semantic cues but unreliable depth, while radar delivers sparse yet reliable position and motion information. We introduce CaR1, a novel camera-radar fusion architecture for BEV vehicle segmentation. Built upon BEVFusion, our approach incorporates a grid-wise radar encoding that discretizes point clouds into structured BEV features and an adaptive fusion mechanism that dynamically balances sensor contributions. Experiments on nuScenes demonstrate competitive segmentation performance (57.6 IoU), on par with state-of-the-art methods. Code is publicly available {https://www.github.com/santimontiel/car1}{online}.