6 papers
Spatiotemporal Prediction of Secondary Crashes by Rebalancing Dynamic and Static Data with Generative Adversarial Networks
Junlan Chen, Yiqun Li, Chenyu Ling +2
Data imbalance is a common issue in analyzing and predicting sudden traffic events. Secondary crashes constitute only a small proportion of all crashes. These secondary crashes, tr…
Risk-Informed Diffusion Transformer for Long-Tail Trajectory Prediction in the Crash Scenario
Junlan Chen, Pei Liu, Zihao Zhang +3
Trajectory prediction methods have been widely applied in autonomous driving technologies. Although the overall performance accuracy of trajectory prediction is relatively high, th…
Enhancing Crash Frequency Modeling Based on Augmented Multi-Type Data by Hybrid VAE-Diffusion-Based Generative Neural Networks
Junlan Chen, Qijie He, Pei Liu +3
Crash frequency modelling analyzes the impact of factors like traffic volume, road geometry, and environmental conditions on crash occurrences. Inaccurate predictions can distort o…
Kaninfradet3D:A Road-side Camera-LiDAR Fusion 3D Perception Model based on Nonlinear Feature Extraction and Intrinsic Correlation
Pei Liu, Nanfang Zheng, Yiqun Li +2
With the development of AI-assisted driving, numerous methods have emerged for ego-vehicle 3D perception tasks, but there has been limited research on roadside perception. With its…
Scene-Aware Explainable Multimodal Trajectory Prediction
Pei Liu, Haipeng Liu, Xingyu Liu +4
Advancements in intelligent technologies have significantly improved navigation in complex traffic environments by enhancing environment perception and trajectory prediction for au…
A Generative Deep Learning Approach for Crash Severity Modeling with Imbalanced Data
Junlan Chen, Ziyuan Pu, Nan Zheng +3
Crash data is often greatly imbalanced, with the majority of crashes being non-fatal crashes, and only a small number being fatal crashes due to their rarity. Such data imbalance i…