In the realm of autonomous driving, accurately detecting surrounding obstacles is crucial for effective decision-making. Traditional methods primarily rely on 3D bounding boxes to represent these obstacles, which often fail to capture the complexity of irregularly shaped, real-world objects. To overcome these limitations, we present GUIDE, a novel framework that utilizes 3D Gaussians for instance detection and occupancy prediction. Unlike conventional occupancy prediction methods, GUIDE also offers robust tracking capabilities. Our framework employs a sparse representation strategy, using Gaussian-to-Voxel Splatting to provide fine-grained, instance-level occupancy data without the computational demands associated with dense voxel grids. Experimental validation on the nuScenes dataset demonstrates GUIDE's performance, with an instance occupancy mAP of 21.61, marking a 50\% improvement over existing methods, alongside competitive tracking capabilities. GUIDE establishes a new benchmark in autonomous perception systems, effectively combining precision with computational efficiency to better address the complexities of real-world driving environments.

翻译：在自动驾驶领域，准确检测周围障碍物对于有效决策至关重要。传统方法主要依赖三维边界框来表示这些障碍物，但往往难以捕捉现实中不规则形状物体的复杂性。为克服这些局限性，我们提出了GUIDE，一种利用三维高斯分布进行实例检测与占据预测的新型框架。与传统的占据预测方法不同，GUIDE还具备鲁棒的跟踪能力。该框架采用稀疏表示策略，通过高斯-体素投射技术提供细粒度的实例级占据数据，避免了密集体素网格带来的计算负担。在nuScenes数据集上的实验验证表明，GUIDE实现了21.61的实例占据平均精度均值（mAP），较现有方法提升50%，同时展现出具有竞争力的跟踪性能。GUIDE为自动驾驶感知系统设立了新基准，有效融合了精度与计算效率，以更好地应对真实驾驶环境的复杂性。