469 citations · 594 across the 8 of their papers we have counts for
8 papers
Traffic Performance GPT (TP-GPT): Real-Time Data Informed Intelligent ChatBot for Transportation Surveillance and Management
Bingzhang Wang, Zhiyu Cai, Muhammad Monjurul Karim +2
The digitization of traffic sensing infrastructure has significantly accumulated an extensive traffic data warehouse, which presents unprecedented challenges for transportation ana…
Fusion-GRU: A Deep Learning Model for Future Bounding Box Prediction of Traffic Agents in Risky Driving Videos
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 sur…
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…
Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting
Meng-Ju Tsai, Zhiyong Cui, Hao Yang +3
With accurate and timely traffic forecasting, the impacted traffic conditions can be predicted in advance to guide agencies and residents to respond to changes in traffic patterns…
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…
Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection
Yifan Zhuang, Ziyuan Pu, Jia Hu +1
Accurate and efficient pedestrian detection is crucial for the intelligent transportation system regarding pedestrian safety and mobility, e.g., Advanced Driver Assistance Systems,…
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…