9 citations · 11 across the 6 of their papers we have counts for
6 papers
OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving
Juntong Peng, Juanwu Lu, Yupeng Zhou +3
We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving. The model directly interprets independent context sequences comprising multi-…
ViLaD: A Large Vision Language Diffusion Framework for End-to-End Autonomous Driving
Can Cui, Yupeng Zhou, Juntong Peng +6
End-to-end autonomous driving systems built on Vision Language Models (VLMs) have shown significant promise, yet their reliance on autoregressive architectures introduces some limi…
A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
Yupeng Zhou, Can Cui, Juntong Peng +5
Vision-Language Models (VLMs) have demonstrated notable promise in autonomous driving by offering the potential for multimodal reasoning through pretraining on extensive image-text…
On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation
Can Cui, Zichong Yang, Yupeng Zhou +11
Personalized driving refers to an autonomous vehicle's ability to adapt its driving behavior or control strategies to match individual users' preferences and driving styles while m…
LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends
Can Cui, Yunsheng Ma, Sung-Yeon Park +14
With the broader adoption and highly successful development of Large Language Models (LLMs), there has been growing interest and demand for applying LLMs to autonomous driving tech…
Personalized Autonomous Driving with Large Language Models: Field Experiments
Can Cui, Zichong Yang, Yupeng Zhou +6
Integrating large language models (LLMs) in autonomous vehicles enables conversation with AI systems to drive the vehicle. However, it also emphasizes the requirement for such syst…