10 citations · 52 across the 40 of their papers we have counts for
8 papers · 1 filter
Complementary Learning System Empowers Online Continual Learning of Vehicle Motion Forecasting in Smart Cities
Zirui Li, Yunlong Lin, Guodong Du +5
Artificial intelligence underpins most smart city services, yet deep neural network (DNN) that forecasts vehicle motion still struggle with catastrophic forgetting, the loss of ear…
Exploring Over-stationarization in Deep Learning-based Bus/Tram Arrival Time Prediction: Analysis and Non-stationary Effect Recovery
Zirui Li, Bin Yang, Meng Wang
Arrival time prediction (ATP) of public transport vehicles is essential in improving passenger experience and supporting traffic management. Deep learning has demonstrated outstand…
Escaping Stability-Plasticity Dilemma in Online Continual Learning for Motion Forecasting via Synergetic Memory Rehearsal
Yunlong Lin, Chao Lu, Tongshuai Wu +5
Deep neural networks (DNN) have achieved remarkable success in motion forecasting. However, most DNN-based methods suffer from catastrophic forgetting and fail to maintain their pe…
Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario
Yinsong Chen, Kaifeng Wang, Xiaoqiang Meng +3
Current research on decision-making in safety-critical scenarios often relies on inefficient data-driven scenario generation or specific modeling approaches, which fail to capture…
ArrivalNet: Predicting City-wide Bus/Tram Arrival Time with Two-dimensional Temporal Variation Modeling
Zirui Li, Patrick Wolf, Meng Wang
Accurate arrival time prediction (ATP) of buses and trams plays a crucial role in public transport operations. Current methods focused on modeling one-dimensional temporal informat…
Spatiotemporal-Linear: Towards Universal Multivariate Time Series Forecasting
Aiyinsi Zuo, Haixi Zhang, Zirui Li +1
Within the field of complicated multivariate time series forecasting (TSF), popular techniques frequently rely on intricate deep learning architectures, ranging from transformer-ba…
A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles
Jingyi Xu, Zirui Li, Li Gao +3
The deep reinforcement learning-based energy management strategies (EMS) have become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are changed, the…
Learning and Generalizing Motion Primitives from Driving Data for Path-Tracking Applications
Boyang Wang, Zirui Li, Jianwei Gong +3
Considering the driving habits which are learned from the naturalistic driving data in the path-tracking system can significantly improve the acceptance of intelligent vehicles. Th…