1 citations · 3 across the 17 of their papers we have counts for
18 papers
Planning-Oriented End-to-End Autonomous Driving: Architectures, Evaluation, and Emerging Paradigms
Yanchen Guan, Xingcheng Liu, Bin Rao +7
End-to-end autonomous driving has evolved from camera-to-control regression toward planning-oriented systems that use structured representations, trajectory-level outputs, and incr…
TRACER: Training-Free Closed-Loop Structured Inference for Traffic Accident Reconstruction
Yanchen Guan, Chengyue Wang, Bin Rao +5
Traffic accident reconstruction is a forensic inverse problem that requires recovering physically consistent motion from sparse and heterogeneous evidence. Existing learning-based…
Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation
Yanchen Guan, Haicheng Liao, Chengyue Wang +4
Anticipating traffic accidents is a critical yet unresolved problem for autonomous driving, hindered by the inherent complexity of modeling interactions between road users and the…
Learning physically grounded traffic accident reconstruction from public accident reports
Yanchen Guan, Haicheng Liao, Chengyue Wang +1
Traffic accidents are routinely documented in textual reports, yet physically grounded accident reconstruction remains difficult because detailed scene measurements and expert reco…
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…
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…
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…
When, Where, and What? A Novel Benchmark for Accident Anticipation and Localization with Large Language Models
Haicheng Liao, Yongkang Li, Chengyue Wang +6
As autonomous driving systems increasingly become part of daily transportation, the ability to accurately anticipate and mitigate potential traffic accidents is paramount. Traditio…
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…
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…
World Models for Autonomous Driving: An Initial Survey
Yanchen Guan, Haicheng Liao, Zhenning Li +5
In the rapidly evolving landscape of autonomous driving, the capability to accurately predict future events and assess their implications is paramount for both safety and efficienc…