no code implementations • 27 Mar 2024 • Mingxing Peng, Xusen Guo, Xianda Chen, Meixin Zhu, Kehua Chen, Hao, Yang, Xuesong Wang, Yinhai Wang
To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior.
no code implementations • 20 Dec 2023 • Lening Wang, Yilong Ren, Han Jiang, Pinlong Cai, Daocheng Fu, Tianqi Wang, Zhiyong Cui, Haiyang Yu, Xuesong Wang, Hanchu Zhou, Helai Huang, Yinhai Wang
For human-driven vehicles, we offer proactive long-range safety warnings and blind-spot alerts while also providing safety driving recommendations and behavioral norms through human-machine dialogue and interaction.
no code implementations • 24 Oct 2023 • Di Chen, Meixin Zhu, Hao Yang, Xuesong Wang, Yinhai Wang
The primary objective of this paper is to review current research efforts and provide a futuristic perspective that will benefit future developments in the field.
no code implementations • 12 Aug 2023 • Muhammad Monjurul Karim, Ruwen Qin, Yinhai Wang
To ensure the safe and efficient navigation of autonomous vehicles and advanced driving assistance systems in complex traffic scenarios, predicting the future bounding boxes of surrounding traffic agents is crucial.
1 code implementation • 28 Jun 2023 • Lei Tong, Adam Corrigan, Navin Rathna Kumar, Kerry Hallbrook, Jonathan Orme, Yinhai Wang, Huiyu Zhou
To address this challenge, we introduce CLANet, a pioneering framework for cross-batch cell line identification using brightfield images, specifically designed to tackle three distinct batch effects.
1 code implementation • 25 May 2023 • Xianda Chen, Meixin Zhu, Kehua Chen, Pengqin Wang, Hongliang Lu, Hui Zhong, Xu Han, Yinhai Wang
To address this gap and promote the development of microscopic traffic flow modeling, we establish a public benchmark dataset for car-following behavior modeling.
1 code implementation • 15 Mar 2023 • Jan Oscar Cross-Zamirski, Praveen Anand, Guy Williams, Elizabeth Mouchet, Yinhai Wang, Carola-Bibiane Schönlieb
Image-to-image reconstruction problems with free or inexpensive metadata in the form of class labels appear often in biological and medical image domains.
1 code implementation • 16 Sep 2022 • Jan Oscar Cross-Zamirski, Guy Williams, Elizabeth Mouchet, Carola-Bibiane Schönlieb, Riku Turkki, Yinhai Wang
We propose WS-DINO as a novel framework to use weak label information in learning phenotypic representations from high-content fluorescent images of cells.
no code implementations • 19 Jun 2022 • Meng-Ju Tsai, Zhiyong Cui, Hao Yang, Cole Kopca, Sophie Tien, Yinhai Wang
To better manage future roadway capacity and accommodate social and human impacts, it is crucial to propose a flexible and comprehensive framework to predict physical-aware long-term traffic conditions for public users and transportation agencies.
no code implementations • 4 Feb 2022 • Meixin Zhu, Simon S. Du, Xuesong Wang, Hao, Yang, Ziyuan Pu, Yinhai Wang
Through cross-attention between encoder and decoder, the decoder learns to build a connection between historical driving and future LV speed, based on which a prediction of future FV speed can be obtained.
no code implementations • 9 Dec 2021 • Yifan Zhuang, Ziyuan Pu, Jia Hu, Yinhai Wang
Besides, the quantized IT-MN achieves an inference time of 0. 21 seconds per image pair on the edge device, which also demonstrates the potentiality of deploying the proposed model on edge devices as a highly efficient pedestrian detection algorithm.
no code implementations • 2 Aug 2020 • Ruimin Ke, Zhiyong Cui, Yanlong Chen, Meixin Zhu, Hao Yang, Yinhai Wang
It is among the first efforts in applying edge computing for real-time traffic video analytics and is expected to benefit multiple sub-fields in smart transportation research and applications.
1 code implementation • 15 Jul 2020 • Zhihua Liu, Lei Tong, Long Chen, Feixiang Zhou, Zheheng Jiang, Qianni Zhang, Yinhai Wang, Caifeng Shan, Ling Li, Huiyu Zhou
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning.
no code implementations • 24 May 2020 • Zhiyong Cui, Ruimin Ke, Ziyuan Pu, Yinhai Wang
Further, comprehensive comparison results show that the proposed data imputation mechanism in the RNN-based models can achieve outstanding prediction performance when the model's input data contains different patterns of missing values.
2 code implementations • 10 Dec 2019 • Zhiyong Cui, Longfei Lin, Ziyuan Pu, Yinhai Wang
Although missing values can be imputed, existing data imputation methods normally need long-term historical traffic state data.
no code implementations • 1 Nov 2019 • Ziyuan Pu, Shuo Wang, Chenglong Liu, Zhiyong Cui, Yinhai Wang
A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity.
no code implementations • 1 Nov 2019 • Ziyuan Pu, Zhiyong Cui, Shuo Wang, Qianmu Li, Yinhai Wang
The findings can help improve the prediction accuracy and efficiency of forecasting road surface friction using historical data sets with missing values, therefore mitigating the impact of wet or icy road conditions on traffic safety.
no code implementations • 27 Oct 2019 • Meixin Zhu, Jingyun Hu, Ziyuan Pu, Zhiyong Cui, Liangwu Yan, Yinhai Wang
This study developed a traffic sign detection and recognition algorithm based on the RetinaNet.
no code implementations • 13 Oct 2019 • Meixin Zhu, Jingyun Hu, Hao, Yang, Ziyuan Pu, Yinhai Wang
Also, results of the multinomial logit model show that (1) an increase in travel cost would decrease the utility of all the transportation modes; (2) people are less sensitive to the travel distance for the metro mode or a multi-modal option that containing metro, i. e., compared to other modes, people would be more willing to tolerate long-distance metro trips.
1 code implementation • 2 Jun 2019 • Lei Tong, Zhihua Liu, Zheheng Jiang, Feixiang Zhou, Long Chen, Jialin Lyu, Xiangrong Zhang, Qianni Zhang, Abdul Sadka Senior, Yinhai Wang, Ling Li, Huiyu Zhou
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year.
no code implementations • 15 Mar 2019 • Philip T. Jackson, Yinhai Wang, Sinead Knight, Hongming Chen, Thierry Dorval, Martin Brown, Claus Bendtsen, Boguslaw Obara
While deep learning has seen many recent applications to drug discovery, most have focused on predicting activity or toxicity directly from chemical structure.
no code implementations • 5 Mar 2019 • Ruimin Ke, Wan Li, Zhiyong Cui, Yinhai Wang
In this model, we first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial-temporal multi-channel matrices.
1 code implementation • 29 Jan 2019 • Meixin Zhu, Yinhai Wang, Ziyuan Pu, Jingyun Hu, Xuesong Wang, Ruimin Ke
A model used for velocity control during car following was proposed based on deep reinforcement learning (RL).
no code implementations • 3 Jan 2019 • Meixin Zhu, Xuesong Wang, Yinhai Wang
This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.
no code implementations • 24 Oct 2018 • Zhengchao Zhang, Meng Li, Xi Lin, Yinhai Wang, Fang He
Multistep traffic forecasting on road networks is a crucial task in successful intelligent transportation system applications.
2 code implementations • 20 Feb 2018 • Zhiyong Cui, Kristian Henrickson, Ruimin Ke, Ziyuan Pu, Yinhai Wang
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks.
1 code implementation • 7 Jan 2018 • Zhiyong Cui, Ruimin Ke, Ziyuan Pu, Yinhai Wang
In this paper, a deep stacked bidirectional and unidirectional LSTM (SBU- LSTM) neural network architecture is proposed, which considers both forward and backward dependencies in time series data, to predict network-wide traffic speed.
no code implementations • 5 Jan 2018 • Yunyi Liang, Zhiyong Cui, Yu Tian, Huimiao Chen, Yinhai Wang
The GAA is able to combine traffic flow theory with neural networks and thus improve the accuracy of traffic state estimation.