1 citations · 1 across the 4 of their papers we have counts for
4 papers
A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving
Heye Huang, Jingguang Li, Zhiyuan Zhou +4
Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic safety. These scenarios are…
CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention
Jiacheng Tang, Zhiyuan Zhou, Zhuolin He +3
Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relationships. This vulnerability leads…
REACT: Runtime-Enabled Active Collision-avoidance Technique for Autonomous Driving
Heye Huang, Hao Cheng, Zhiyuan Zhou +3
Achieving rapid and effective active collision avoidance in dynamic interactive traffic remains a core challenge for autonomous driving. This paper proposes REACT (Runtime-Enabled…
SafeDrive: Knowledge- and Data-Driven Risk-Sensitive Decision-Making for Autonomous Vehicles with Large Language Models
Zhiyuan Zhou, Heye Huang, Boqi Li +3
Recent advancements in autonomous vehicles (AVs) use Large Language Models (LLMs) to perform well in normal driving scenarios. However, ensuring safety in dynamic, high-risk enviro…