1 citations · 2 across the 11 of their papers we have counts for
4 papers · 2 filters
ReWAM: Reciprocal World Action Models for Interactive Autonomous Driving
Benshan Ma, Pei Liu, Ruiguo Zhong +4
In interactive scenarios, an autonomous driving system is required to generate ego actions under the influence of other agents' behaviors. Existing World Action Models (WAMs) typic…
DA-WAM: Decision-Aligned Future Latents for Driving World Models
Ruiguo Zhong, Benshan Ma, Xiaolong Chen +5
World models for autonomous driving should anticipate the consequences that matter for driving decisions rather than reconstruct every detail of future scenes. However, predictive…
LUNA-AD: Lightweight Uncertainty-Aware Language Model with Lifelong Learning for Autonomous Driving
Ruoyu Yao, Pei Liu, Ruiguo Zhong +3
While large language models (LLMs) offer promising reasoning capabilities, their integration into safety-critical driving systems is hindered by limited reasoning diversity, high c…
Decision-Making with Lightweight Confidence-Aware Language Model for Autonomous Driving
Ruoyu Yao, Ruiguo Zhong, Pei Liu +3
Large Language Models (LLMs) and Multimodal LLMs (MLLMs) have demonstrated immense potential in autonomous driving (AD) by offering human-like reasoning and open-world generalizati…