2 citations · 4 across the 13 of their papers we have counts for
14 papers
FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning for Autonomous Driving
Bonan Wang, Letian Tao, Bin Shuai +7
Deep reinforcement learning is pivotal for closed-loop autonomous driving yet remains constrained by severe bottlenecks in sampling efficiency. Standard parallel sampling mitigates…
SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving
Chengyue Wang, Bin Rao, Haicheng Liao +3
Accurate trajectory prediction in autonomous driving hinges on modeling dynamic and context-dependent interactions among traffic agents. However, most existing approaches are purel…
Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles
Haicheng Liao, Huanming Shen, Bonan Wang +8
Interpreting natural-language commands to localize target objects is critical for autonomous driving (AD). Existing visual grounding (VG) methods for autonomous vehicles (AVs) typi…
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…
CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting
Haicheng Liao, Hanlin Kong, Bonan Wang +5
Accurate motion forecasting is crucial for safe autonomous driving (AD). This study proposes CoT-Drive, a novel approach that enhances motion forecasting by leveraging large langua…
NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving
Chengyue Wang, Haicheng Liao, Bonan Wang +6
Accurate trajectory prediction is essential for the safety and efficiency of autonomous driving. Traditional models often struggle with real-time processing, capturing non-linearit…
Characterized Diffusion and Spatial-Temporal Interaction Network for Trajectory Prediction in Autonomous Driving
Haicheng Liao, Xuelin Li, Yongkang Li +7
Trajectory prediction is a cornerstone in autonomous driving (AD), playing a critical role in enabling vehicles to navigate safely and efficiently in dynamic environments. To addre…
MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving
Haicheng Liao, Zhenning Li, Chengyue Wang +5
This paper introduces a trajectory prediction model tailored for autonomous driving, focusing on capturing complex interactions in dynamic traffic scenarios without reliance on hig…
A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environment
Haicheng Liao, Zhenning Li, Chengyue Wang +6
As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive i…