Robust and accurate perception of dynamic objects and map elements is crucial for autonomous vehicles performing safe navigation in complex traffic scenarios. While vision-only methods have become the de facto standard due to their technical advances, they can benefit from effective and cost-efficient fusion with radar measurements. In this work, we advance fusion methods by repurposing Gaussian Splatting as an efficient universal view transformer that bridges the view disparity gap, mapping both image pixels and radar points into a common Bird's-Eye View (BEV) representation. Our main contribution is GaussianCaR, an end-to-end network for BEV segmentation that, unlike prior BEV fusion methods, leverages Gaussian Splatting to map raw sensor information into latent features for efficient camera-radar fusion. Our architecture combines multi-scale fusion with a transformer decoder to efficiently extract BEV features. Experimental results demonstrate that our approach achieves performance on par with, or even surpassing, the state of the art on BEV segmentation tasks (57.3%, 82.9%, and 50.1% IoU for vehicles, roads, and lane dividers) on the nuScenes dataset, while maintaining a 3.2x faster inference runtime. Code and project page are available online.

翻译：在复杂交通场景中，自动驾驶车辆需通过动态目标与地图元素的鲁棒精准感知来实现安全导航。尽管纯视觉方法因技术进步已成为实际标准，但通过与雷达测量值进行高效且低成本的融合仍可进一步提升性能。本研究通过将高斯泼溅（Gaussian Splatting）重构为高效的通用视角转换器来推进融合方法，该转换器能弥合视角差异，将图像像素与雷达点统一映射至共同的鸟瞰图（BEV）表征空间。我们的核心贡献是GaussianCaR——一种端到端的BEV分割网络，与现有BEV融合方法不同，该网络利用高斯泼溅将原始传感器信息映射至隐特征空间，从而实现高效的相机-雷达融合。该架构结合多尺度融合与Transformer解码器，可高效提取BEV特征。实验结果表明，在nuScenes数据集上，我们的方法在BEV分割任务中（车辆、道路与车道分隔线的交并比分别为57.3%、82.9%与50.1%）达到甚至超越了当前最优性能，同时推理速度提升3.2倍。代码与项目页面已公开。