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Lane change strategies for autonomous vehicles: A deep reinforcement learning approach based on transformer
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 …
(AVs) decision-making. However, the deployment of these technologies is usually …
LLM-based operating systems for automated vehicles: A new perspective
The deployment of large language models (LLMs) brings challenges to intelligent systems
because its capability of integrating large-scale training data facilitates contextual reasoning …
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
Intelligent Transportation Systems (ITS) aim to enhance traffic management by improving
connectivity and data sharing among vehicles and road infrastructure. In a Mixed Connected …
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
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 …
The interactions not only affect their driving behaviors but also influence the traffic flow. This …