3 papers
cs.CV2025
Evaluation of Safety Cognition Capability in Vision-Language Models for Autonomous Driving
Enming Zhang, Peizhe Gong, Xingyuan Dai +3
Ensuring the safety of vision-language models (VLMs) in autonomous driving systems is of paramount importance, yet existing research has largely focused on conventional benchmarks…
cs.CV2024
MiniDrive: More Efficient Vision-Language Models with Multi-Level 2D Features as Text Tokens for Autonomous Driving
Enming Zhang, Xingyuan Dai, Min Huang +2
Vision-language models (VLMs) serve as general-purpose end-to-end models in autonomous driving, performing subtasks such as prediction, planning, and perception through question-an…
cs.CV2024
First Multi-Dimensional Evaluation of Flowchart Comprehension for Multimodal Large Language Models
Enming Zhang, Ruobing Yao, Huanyong Liu +2
With the development of Multimodal Large Language Models (MLLMs) technology, its general capabilities are increasingly powerful. To evaluate the various abilities of MLLMs, numerou…