most citedReceive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles

9 citations · 17 across the 10 of their papers we have counts for

collaborators

10 papers

cs.AI20241 cited

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…

cs.CV2024

Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture

Juanwu Lu, Can Cui, Yunsheng Ma +2

Safety and robustness are crucial factors in developing trustworthy autonomous vehicles. One essential aspect of addressing these factors is to equip vehicles with the capability t…

cs.CV2023

MACP: Efficient Model Adaptation for Cooperative Perception

Yunsheng Ma, Juanwu Lu, Can Cui +4

Vehicle-to-vehicle (V2V) communications have greatly enhanced the perception capabilities of connected and automated vehicles (CAVs) by enabling information sharing to "see through…

cs.HC20239 cited

Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles

Can Cui, Yunsheng Ma, Xu Cao +2

The fusion of human-centric design and artificial intelligence (AI) capabilities has opened up new possibilities for next-generation autonomous vehicles that go beyond transportati…

cs.HC20233 cited

Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles

Can Cui, Yunsheng Ma, Xu Cao +2

The future of autonomous vehicles lies in the convergence of human-centric design and advanced AI capabilities. Autonomous vehicles of the future will not only transport passengers…

cs.LG20231 cited

Mitigating Transformer Overconfidence via Lipschitz Regularization

Wenqian Ye, Yunsheng Ma, Xu Cao +1

Though Transformers have achieved promising results in many computer vision tasks, they tend to be over-confident in predictions, as the standard Dot Product Self-Attention (DPSA)…