37 citations · 57 across the 3 of their papers we have counts for
6 papers
Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review
Talha Azfar, Jinlong Li, Hongkai Yu +3
Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. Whil…
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values
Zhiyong Cui, Ruimin Ke, Ziyuan Pu +1
Short-term traffic forecasting based on deep learning methods, especially recurrent neural networks (RNN), has received much attention in recent years. However, the potential of RN…
A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices
Ruimin Ke, Yifan Zhuang, Ziyuan Pu +1
Cloud computing has been a main-stream computing service for years. Recently, with the rapid development in urbanization, massive video surveillance data are produced at an unprece…
Two-Stream Multi-Channel Convolutional Neural Network (TM-CNN) for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact
Ruimin Ke, Wan Li, Zhiyong Cui +1
Traffic speed prediction is a critically important component of intelligent transportation systems (ITS). Recently, with the rapid development of deep learning and transportation d…
Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving
Meixin Zhu, Yinhai Wang, Ziyuan Pu +3
A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function re…
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
Zhiyong Cui, Kristian Henrickson, Ruimin Ke +2
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on ro…