An agent-based modelling framework for driving policy learning in connected and autonomous vehicles

Due to the complexity of the natural world, a programmer cannot foresee all possible situations a connected and autonomous vehicle (CAV) will face during its operation, and hence, CAVs will need to learn to make decisions autonomously. Due to the sensing of its surroundings and information exchanged...

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Bibliographic Details
Main Authors: Varuna De-Silva, Xiongzhao Wang, Ali Aladagli, Ahmet Kondoz, Erhan Ekmekcioglu
Format: Default Conference proceeding
Published: 2018
Subjects:
Agent-based learning
Reinforcement learning
Driving policy
Data driven control
Imitation learning
Online Access:https://hdl.handle.net/2134/32720
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https://hdl.handle.net/2134/32720

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