1 citations · 1 across the 6 of their papers we have counts for
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RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving
Yujin Wang, Quanfeng Liu, Zhengxin Jiang +5
Accurately understanding and deciding high-level meta-actions is essential for ensuring reliable and safe autonomous driving systems. While vision-language models (VLMs) have shown…
MLLM-SUL: Multimodal Large Language Model for Semantic Scene Understanding and Localization in Traffic Scenarios
Jiaqi Fan, Jianhua Wu, Jincheng Gao +4
Multimodal large language models (MLLMs) have shown satisfactory effects in many autonomous driving tasks. In this paper, MLLMs are utilized to solve joint semantic scene understan…
Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios
Jiaqi Fan, Jianhua Wu, Hongqing Chu +2
Large vision-language models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding and generation tasks. However, these models occasionally generate halluci…
RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models
Yujin Wang, Quanfeng Liu, Jiaqi Fan +5
Understanding and addressing corner cases is essential for ensuring the safety and reliability of autonomous driving systems. Vision-language models (VLMs) play a crucial role in e…
Prospective Role of Foundation Models in Advancing Autonomous Vehicles
Jianhua Wu, Bingzhao Gao, Jincheng Gao +8
With the development of artificial intelligence and breakthroughs in deep learning, large-scale Foundation Models (FMs), such as GPT, Sora, etc., have achieved remarkable results i…