most citedAdaptive Evolution Factor Risk Ellipse Framework for Reliable and Safe Autonomous Driving

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2025

Attention and Risk-Aware Decision Framework for Safe Autonomous Driving

Zhen Tian, Fujiang Yuan, Yangfan He +7

Autonomous driving has attracted great interest due to its potential capability in full-unsupervised driving. Model-based and learning-based methods are widely used in autonomous d…

cs.RO20251 cited

Adaptive Evolution Factor Risk Ellipse Framework for Reliable and Safe Autonomous Driving

Fujiang Yuan, Zhen Tian, Yangfan He +4

In recent years, ensuring safety, efficiency, and comfort in interactive autonomous driving has become a critical challenge. Traditional model-based techniques, such as game-theore…

cs.RO2025

Enhanced Mean Field Game for Interactive Decision-Making with Varied Stylish Multi-Vehicles

Liancheng Zheng, Zhen Tian, Yangfan He +4

This paper presents an MFG-based decision-making framework for autonomous driving in heterogeneous traffic. To capture diverse human behaviors, we propose a quantitative driving st…

cs.CV2025

Optimizing Multi-Round Enhanced Training in Diffusion Models for Improved Preference Understanding

Kun Li, Jianhui Wang, Yangfan He +10

Generative AI has significantly changed industries by enabling text-driven image generation, yet challenges remain in achieving high-resolution outputs that align with fine-grained…

cs.CV2025

MaRI: Material Retrieval Integration across Domains

Jianhui Wang, Zhifei Yang, Yangfan He +3

Accurate material retrieval is critical for creating realistic 3D assets. Existing methods rely on datasets that capture shape-invariant and lighting-varied representations of mate…

cs.RO2024

FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback

Kangan Qian, Zhikun Ma, Yangfan He +13

Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving systems. While end-to-end models trained on large-scale datasets excel in common driv…