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

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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 with other vehicles and road infrastructure a CAV will have access to large amounts of useful data. While different control algorithms have been proposed for CAVs, the benefits brought about by connectedness of autonomous vehicles to other vehicles and to the infrastructure, and its implications on policy learning has not been investigated in literature. This paper investigates a data driven driving policy learning framework through an agent-based modelling approaches. The contributions of the paper are twofold. A dynamic programming framework is proposed for in vehicle policy learning with and without connectivity to neighboring vehicles. The simulation results indicate that while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V) communication of information improves this capability. Furthermore, to overcome the limitations of sensing in a CAV, the paper proposes a novel concept for infrastructure-led policy learning and communication with autonomous vehicles. In infrastructure-led policy learning, road-side infrastructure senses and captures successful vehicle maneuvers and learns an optimal policy from those temporal sequences, and when a vehicle approaches the road-side unit, the policy is communicated to the CAV. Deep-imitation learning methodology is proposed to develop such an infrastructure-led policy learning framework.

History

School

  • Loughborough University London

Published in

Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys)

Volume

2

Pages

113 - 125

Citation

DE-SILVA, V. … et al, 2018. An agent-based modelling framework for driving policy learning in connected and autonomous vehicles. IN: Arai, K., Kapoor, S. and Bhatia, R. (eds). Intelligent Systems Conference (IntelliSys 2018), London, UK, 6-7 September 2018, pp.113-125.

Source

2018 Intelligent Systems Conference (IntelliSys 2018)

Publisher

Springer

Version

AM (Accepted Manuscript)

Rights holder

© Springer Nature Switzerland AG

Publisher statement

The final authenticated version is available online at https://doi.org/10.1007/978-3-030-01057-7_10.

Acceptance date

16/01/2018

Publication date

2018-11-08

Copyright date

2019

ISBN

9783030010560; 9783030010577

Book series

Advances in Intelligent Systems and Computing;869

Language

en

Editor(s)

Kohei Arai, Supriya Kapoor, Rahul Bhatia

Location

London