Periodic event-triggered fault detection for safe platooning control of intelligent and connected vehicles

L Wang, M Hu, Y Bian, G Guo, SE Li… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Fault detection is not only a useful approach to guarantee the safety of a vehicle platooning
system but also an indispensable part of functional safety for future connected automated …

VNAGT: Variational non-autoregressive graph transformer network for multi-agent trajectory prediction

X Chen, H Zhang, Y Hu, J Liang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Accurately predicting the trajectory of road agents in complex traffic scenarios is challenging
because the movement patterns of agents are complex and stochastic, not only depending …

Kinematics-aware multigraph attention network with residual learning for heterogeneous trajectory prediction

Z Sheng, Z Huang, S Chen - Journal of Intelligent and …, 2024 - ieeexplore.ieee.org
Trajectory prediction for heterogeneous traffic agents plays a crucial role in ensuring the
safety and efficiency of automated driving in highly interactive traffic environments …

A review of hybrid physics-based machine learning approaches in traffic state estimation

Z Zhang, XT Yang, H Yang - Intelligent Transportation …, 2023 - academic.oup.com
Traffic state estimation (TSE) plays a significant role in traffic control and operations since it
can provide accurate and high-resolution traffic estimations for locations without traffic states …

A Review of Deep Learning-Based Vehicle Motion Prediction for Autonomous Driving

R Huang, G Zhuo, L Xiong, S Lu, W Tian - Sustainability, 2023 - mdpi.com
Autonomous driving vehicles can effectively improve traffic conditions and promote the
development of intelligent transportation systems. An autonomous vehicle can be divided …

Vehicle sideslip trajectory prediction based on time-series analysis and multi-physical model fusion

L Cao, Y Luo, Y Wang, J Chen… - Journal of Intelligent and …, 2023 - ieeexplore.ieee.org
On highways, vehicles that swerve out of their lane due to sideslip can pose a serious threat
to the safety of autonomous vehicles. To ensure their safety, predicting the sideslip …

Vehicle Interactive Dynamic Graph Neural Network Based Trajectory Prediction for Internet of Vehicles

M Yang, H Zhu, T Wang, J Cai, X Weng… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
In the context of the booming Internet of Vehicles, predicting vehicle trajectories is crucial for
intelligent transportation systems. Existing methods, reliant on sensor data and behavior …

Graph representation learning in the ITS: Car-following informed spatiotemporal network for vehicle trajectory predictions

YH Yin, X Lü, SK Li, LX Yang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Multimodal synchronization has become the research highlight of the ITS, where complex
driving scenarios, various types of vehicles and diverse data sources are crucial …

Network macroscopic fundamental diagram-informed graph learning for traffic state imputation

J Xue, E Ka, Y Feng, SV Ukkusuri - Transportation Research Part B …, 2024 - Elsevier
Traffic state imputation refers to the estimation of missing values of traffic variables, such as
flow rate and traffic density, using available data. It furnishes comprehensive traffic context …

Safety aware neural network for connected and automated vehicle operations

H Yao, X Li, Q Li, C Yu - Transportation Research Part E: Logistics and …, 2024 - Elsevier
Contemporary research in connected and automated vehicle (CAV) operations typically
segregates trajectory prediction from planning in two segregated models. Trajectory …