469 citations · 644 across the 15 of their papers we have counts for
15 papers
Data-driven Traffic Simulation: A Comprehensive Review
Di Chen, Meixin Zhu, Hao Yang +2
Autonomous vehicles (AVs) have the potential to significantly revolutionize society by providing a secure and efficient mode of transportation. Recent years have witnessed notable…
EnsembleFollower: A Hybrid Car-Following Framework Based On Reinforcement Learning and Hierarchical Planning
Xu Han, Xianda Chen, Meixin Zhu +3
Car-following models have made significant contributions to our understanding of longitudinal driving behavior. However, they often exhibit limited accuracy and flexibility, as the…
EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction
Kehua Chen, Xianda Chen, Zihan Yu +2
Accurate trajectory prediction is crucial for the safe and efficient operation of autonomous vehicles. The growing popularity of deep learning has led to the development of numerou…
FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling
Xianda Chen, Meixin Zhu, Kehua Chen +5
Car-following is a control process in which a following vehicle (FV) adjusts its acceleration to keep a safe distance from the lead vehicle (LV). Recently, there has been a booming…
Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning
Pengqin Wang, Meixin Zhu, Shaojie Shen
Interacting with the actual environment to acquire data is often costly and time-consuming in robotic tasks. Model-based offline reinforcement learning (RL) provides a feasible sol…
Catch Planner: Catching High-Speed Targets in the Flight
Huan Yu, Pengqin Wang, Jin Wang +8
Catching high-speed targets in the flight is a complex and typical highly dynamic task. In this paper, we propose Catch Planner, a planning-with-decision scheme for catching. For s…
TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer
Meixin Zhu, Simon S. Du, Xuesong Wang +4
Car-following refers to a control process in which the following vehicle (FV) tries to keep a safe distance between itself and the lead vehicle (LV) by adjusting its acceleration i…
Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications
Ruimin Ke, Zhiyong Cui, Yanlong Chen +3
Traffic near-crash events serve as critical data sources for various smart transportation applications, such as being surrogate safety measures for traffic safety research and corn…
Traffic Performance Score for Measuring the Impact of COVID-19 on Urban Mobility
Zhiyong Cui, Meixin Zhu, Shuo Wang +5
Measuring traffic performance is critical for public agencies who manage traffic and individuals who plan trips, especially when special events happen. The COVID-19 pandemic has si…
Mining Public Transit Ridership Flow and Origin-Destination Information from Wi-Fi and Bluetooth Sensing Data
Ziyuan Pu, Meixin Zhu, Zhiyong Cui +1
Transit ridership flow and origin-destination (O-D) information is essential for enhancing transit network design, optimizing transit route and improving service. The effectiveness…
Traffic Sign Detection and Recognition for Autonomous Driving in Virtual Simulation Environment
Meixin Zhu, Jingyun Hu, Ziyuan Pu +3
This study developed a traffic sign detection and recognition algorithm based on the RetinaNet. Two main aspects were revised to improve the detection of traffic signs: image cropp…
Personalized Context-Aware Multi-Modal Transportation Recommendation
Meixin Zhu, Jingyun Hu, Hao +3
This study proposes to find the most appropriate transport modes with awareness of user preferences (e.g., costs, times) and trip characteristics (e.g., purpose, distance). The wor…
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…
Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning
Meixin Zhu, Xuesong Wang, Yinhai Wang
This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation e…
Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study
Meixin Zhu, Xuesong Wang, Andrew P. Tarko +1
Five car-following models were calibrated, validated and cross-compared. The intelligent driver model performed best among the evaluated models. Considerable behavioral differences…