4 papers
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…
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…
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…
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…