2 citations · 3 across the 4 of their papers we have counts for
5 papers
CrossRay3D: Geometry and Distribution Guidance for Efficient Multimodal 3D Detection
Huiming Yang, Wenzhuo Liu, Yicheng Qiao +8
The sparse cross-modality detector offers more advantages than its counterpart, the Bird's-Eye-View (BEV) detector, particularly in terms of adaptability for downstream tasks and c…
TEM^3-Learning: Time-Efficient Multimodal Multi-Task Learning for Advanced Assistive Driving
Wenzhuo Liu, Yicheng Qiao, Zhen Wang +8
Multi-task learning (MTL) can advance assistive driving by exploring inter-task correlations through shared representations. However, existing methods face two critical limitations…
MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception
Wenzhuo Liu, Wenshuo Wang, Yicheng Qiao +9
Advanced driver assistance systems require a comprehensive understanding of the driver's mental/physical state and traffic context but existing works often neglect the potential be…
MIPD: A Multi-sensory Interactive Perception Dataset for Embodied Intelligent Driving
Zhiwei Li, Tingzhen Zhang, Meihua Zhou +7
During the process of driving, humans usually rely on multiple senses to gather information and make decisions. Analogously, in order to achieve embodied intelligence in autonomous…
Unified End-to-End V2X Cooperative Autonomous Driving
Zhiwei Li, Bozhen Zhang, Lei Yang +6
V2X cooperation, through the integration of sensor data from both vehicles and infrastructure, is considered a pivotal approach to advancing autonomous driving technology. Current…