78 citations · 88 across the 39 of their papers we have counts for
18 papers · 1 filter
E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving
Yihong Tang, Haicheng Liao, Tong Nie +7
End-to-end autonomous driving (AD) systems increasingly adopt vision-language-action (VLA) models, yet they typically ignore the passenger's emotional state, which is central to co…
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…
Cooperative Safety Intelligence over V2X Networks: A Survey
Jiaxun Zhang, Qian Xu, Zhenning Li +3
Vehicle-to-Everything (V2X) cooperation is reshaping traffic safety from an ego-centric sensing problem into a networked intelligence problem involving distributed sensing, coopera…
Map-World: Masked Action planning and Path-Integral World Model for Autonomous Driving
Bin Hu, Zijian Lu, Haicheng Liao +7
Motion planning for autonomous driving must handle multiple plausible futures while remaining computationally efficient. Recent end-to-end systems and world-model-based planners pr…
Predict and Resist: Long-Term Accident Anticipation under Sensor Noise
Xingcheng Liu, Bin Rao, Yanchen Guan +6
Accident anticipation is essential for proactive and safe autonomous driving, where even a brief advance warning can enable critical evasive actions. However, two key challenges hi…
Differentiable Semantic Meta-Learning Framework for Long-Tail Motion Forecasting in Autonomous Driving
Bin Rao, Chengyue Wang, Haicheng Liao +7
Long-tail motion forecasting is a core challenge for autonomous driving, where rare yet safety-critical events-such as abrupt maneuvers and dense multi-agent interactions-dominate…
ROAR: Robust Accident Recognition and Anticipation for Autonomous Driving
Xingcheng Liu, Yanchen Guan, Haicheng Liao +2
Accurate accident anticipation is essential for enhancing the safety of autonomous vehicles (AVs). However, existing methods often assume ideal conditions, overlooking challenges s…
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…
World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving
Yanchen Guan, Haicheng Liao, Chengyue Wang +3
Reliable anticipation of traffic accidents is essential for advancing autonomous driving systems. However, this objective is limited by two fundamental challenges: the scarcity of…
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…
Eyes on the Road, Mind Beyond Vision: Context-Aware Multi-modal Enhanced Risk Anticipation
Jiaxun Zhang, Haicheng Liao, Yumu Xie +4
Accurate accident anticipation remains challenging when driver cognition and dynamic road conditions are underrepresented in predictive models. In this paper, we propose CAMERA (Co…
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…
Towards Human-Like Trajectory Prediction for Autonomous Driving: A Behavior-Centric Approach
Haicheng Liao, Zhenning Li, Guohui Zhang +2
Predicting the trajectories of vehicles is crucial for the development of autonomous driving (AD) systems, particularly in complex and dynamic traffic environments. In this study,…
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…
LATTE: Lightweight Attention-based Traffic Accident Anticipation Engine
Jiaxun Zhang, Yanchen Guan, Chengyue Wang +3
Accurately predicting traffic accidents in real-time is a critical challenge in autonomous driving, particularly in resource-constrained environments. Existing solutions often suff…
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…
Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights
Haicheng Liao, Chengyue Wang, Kaiqun Zhu +5
In mixed autonomous driving environments, accurately predicting the future trajectories of surrounding vehicles is crucial for the safe operation of autonomous vehicles (AVs). In d…