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/car1online.
Montiel-Marín et al. (Fri,) studied this question.