2 papers
cs.CV2025
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…
cs.RO2024
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…