2 citations · 3 across the 6 of their papers we have counts for
10 papers
Addressing Corner Cases in Autonomous Driving: A World Model-based Approach with Mixture of Experts and LLMs
Haicheng Liao, Bonan Wang, Junxian Yang +5
Accurate and reliable motion forecasting is essential for the safe deployment of autonomous vehicles (AVs), particularly in rare but safety-critical scenarios known as corner cases…
Incorporating Legal Logic into Deep Learning: An Intelligent Approach to Probation Prediction
Qinghua Wang, Xu Zhang, Lingyan Yang +4
Probation is a crucial institution in modern criminal law, embodying the principles of fairness and justice while contributing to the harmonious development of society. Despite its…
Domain-Enhanced Dual-Branch Model for Efficient and Interpretable Accident Anticipation
Yanchen Guan, Haicheng Liao, Chengyue Wang +4
Developing precise and computationally efficient traffic accident anticipation system is crucial for contemporary autonomous driving technologies, enabling timely intervention and…
AMD: Adaptive Momentum and Decoupled Contrastive Learning Framework for Robust Long-Tail Trajectory Prediction
Bin Rao, Haicheng Liao, Yanchen Guan +4
Accurately predicting the future trajectories of traffic agents is essential in autonomous driving. However, due to the inherent imbalance in trajectory distributions, tail data in…
Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction
Bonan Wang, Haicheng Liao, Chengyue Wang +7
Accurate trajectory prediction has long been a major challenge for autonomous driving (AD). Traditional data-driven models predominantly rely on statistical correlations, often ove…
SafeCast: Risk-Responsive Motion Forecasting for Autonomous Vehicles
Haicheng Liao, Hanlin Kong, Bin Rao +7
Accurate motion forecasting is essential for the safety and reliability of autonomous driving (AD) systems. While existing methods have made significant progress, they often overlo…