most citedDECODE: Domain-aware Continual Domain Expansion for Motion Prediction

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Percept-WAM: Perception-Enhanced World-Awareness-Action Model for Robust End-to-End Autonomous Driving

Jianhua Han, Meng Tian, Jiangtong Zhu +16

Autonomous driving heavily relies on accurate and robust spatial perception. Many failures arise from inaccuracies and instability, especially in long-tail scenarios and complex in…

cs.RO2025

Improving Traffic Signal Data Quality for the Waymo Open Motion Dataset

Xintao Yan, Erdao Liang, Jiawei Wang +2

Datasets pertaining to autonomous vehicles (AVs) hold significant promise for a range of research fields, including artificial intelligence (AI), autonomous driving, and transporta…

cs.RO2025

Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles

Henry X. Liu, Xintao Yan, Haowei Sun +7

Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespread adoption. Traditional fu…

cs.RO2025

TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation

Haowei Sun, Xintao Yan, Zhijie Qiao +14

Traffic simulation is essential for autonomous vehicle (AV) development, enabling comprehensive safety evaluation across diverse driving conditions. However, traditional rule-based…

cs.CV2024★ 1 cited

DECODE: Domain-aware Continual Domain Expansion for Motion Prediction

Boqi Li, Haojie Zhu, Henry X. Liu

Motion prediction is critical for autonomous vehicles to effectively navigate complex environments and accurately anticipate the behaviors of other traffic participants. As autonom…