6 papers
KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving System
Zhongyu Xia, Wenhao Chen, Yongtao Wang +1
Visual-language reasoning, driving knowledge, and value alignment are essential for advanced autonomous driving systems. However, existing approaches largely rely on data-driven le…
HENet++: Hybrid Encoding and Multi-task Learning for 3D Perception and End-to-end Autonomous Driving
Zhongyu Xia, Zhiwei Lin, Yongtao Wang +1
Three-dimensional feature extraction is a critical component of autonomous driving systems, where perception tasks such as 3D object detection, bird's-eye-view (BEV) semantic segme…
EA3D: Online Open-World 3D Object Extraction from Streaming Videos
Xiaoyu Zhou, Jingqi Wang, Yuang Jia +3
Current 3D scene understanding methods are limited by offline-collected multi-view data or pre-constructed 3D geometry. In this paper, we present ExtractAnything3D (EA3D), a unifie…
DrivingGaussian++: Towards Realistic Reconstruction and Editable Simulation for Surrounding Dynamic Driving Scenes
Yajiao Xiong, Xiaoyu Zhou, Yongtao Wan +2
We present DrivingGaussian++, an efficient and effective framework for realistic reconstructing and controllable editing of surrounding dynamic autonomous driving scenes. DrivingGa…
AutoOcc: Automatic Open-Ended Semantic Occupancy Annotation via Vision-Language Guided Gaussian Splatting
Xiaoyu Zhou, Jingqi Wang, Yongtao Wang +3
Obtaining high-quality 3D semantic occupancy from raw sensor data remains an essential yet challenging task, often requiring extensive manual labeling. In this work, we propose Aut…
OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
Zhongyu Xia, Jishuo Li, Zhiwei Lin +3
Open-world perception aims to develop a model adaptable to novel domains and various sensor configurations and can understand uncommon objects and corner cases. However, current re…