78 citations · 84 across the 29 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…