Lane change strategies for autonomous vehicles: A deep reinforcement learning approach based on transformer

G Li, Y Qiu, Y Yang, Z Li, S Li, W Chu… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
End-to-end approaches are one of the most promising solutions for autonomous vehicles
(AVs) decision-making. However, the deployment of these technologies is usually …

LLM-based operating systems for automated vehicles: A new perspective

J Ge, C Chang, J Zhang, L Li, X Na… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
The deployment of large language models (LLMs) brings challenges to intelligent systems
because its capability of integrating large-scale training data facilitates contextual reasoning …

Collaborative overtaking strategy for enhancing overall effectiveness of mixed connected and connectionless vehicles

H Qian, L Zhao, A Hawbani, Z Liu, K Yu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Intelligent Transportation Systems (ITS) aim to enhance traffic management by improving
connectivity and data sharing among vehicles and road infrastructure. In a Mixed Connected …

[HTML][HTML] Modeling coupled driving behavior during lane change: A multi-agent Transformer reinforcement learning approach

H Guo, M Keyvan-Ekbatani, K **e - Transportation Research Part C …, 2024 - Elsevier
In a lane change (LC) scenario, the lane change vehicle interacts with surrounding vehicles.
The interactions not only affect their driving behaviors but also influence the traffic flow. This …