6 papers
Traffic flow forecasting, STL decomposition, Hybrid model, LSTM, ARIMA, XGBoost, Intelligent transportation systems
Fujiang Yuan, Yangrui Fan, Xiaohuan Bing +3
Accurate traffic flow forecasting is essential for intelligent transportation systems and urban traffic management. However, single model approaches often fail to capture the compl…
Mean Field Game-Based Interactive Trajectory Planning Using Physics-Inspired Unified Potential Fields
Zhen Tian, Fujiang Yuan, Chunhong Yuan +1
Interactive trajectory planning in autonomous driving must balance safety, efficiency, and scalability under heterogeneous driving behaviors. Existing methods often face high compu…
Attention and Risk-Aware Decision Framework for Safe Autonomous Driving
Zhen Tian, Fujiang Yuan, Yangfan He +7
Autonomous driving has attracted great interest due to its potential capability in full-unsupervised driving. Model-based and learning-based methods are widely used in autonomous d…
Adaptive Evolution Factor Risk Ellipse Framework for Reliable and Safe Autonomous Driving
Fujiang Yuan, Zhen Tian, Yangfan He +4
In recent years, ensuring safety, efficiency, and comfort in interactive autonomous driving has become a critical challenge. Traditional model-based techniques, such as game-theore…
Enhanced Mean Field Game for Interactive Decision-Making with Varied Stylish Multi-Vehicles
Liancheng Zheng, Zhen Tian, Yangfan He +4
This paper presents an MFG-based decision-making framework for autonomous driving in heterogeneous traffic. To capture diverse human behaviors, we propose a quantitative driving st…
Safe and Efficient Lane-Changing for Autonomous Vehicles: An Improved Double Quintic Polynomial Approach with Time-to-Collision Evaluation
Rui Bai, Rui Xu, Teng Rui +5
Autonomous driving technology has made significant advancements in recent years, yet challenges remain in ensuring safe and comfortable interactions with human-driven vehicles (HDV…