GUIDE: Gaussian Unified Instance Detection for Enhanced Obstacle Perception in Autonomous Driving
Chunyong Hu, Qi Luo, Jianyun Xu, Song Wang, Qiang Li, Sheng Yang
Abstract
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
BibTeX
@inproceedings{aaai2026_guidegaussianuni,
title = {GUIDE: Gaussian Unified Instance Detection for Enhanced Obstacle Perception in Autonomous Driving},
author = {Chunyong Hu and Qi Luo and Jianyun Xu and Song Wang and Qiang Li and Sheng Yang},
booktitle = {AAAI 2026},
year = {2026}
}