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An agent-based modelling framework for driving policy learning in connected and autonomous vehicles
conference contribution
posted on 2018-04-24, 13:53 authored by Varuna De-SilvaVaruna De-Silva, Xiongzhao Wang, Ali Aladagli, Ahmet Kondoz, Erhan EkmekciogluDue 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
2Pages
113 - 125Citation
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
SpringerVersion
- AM (Accepted Manuscript)
Rights holder
© Springer Nature Switzerland AGPublisher statement
The final authenticated version is available online at https://doi.org/10.1007/978-3-030-01057-7_10.Acceptance date
2018-01-16Publication date
2018-11-08Copyright date
2019ISBN
9783030010560; 9783030010577Publisher version
Book series
Advances in Intelligent Systems and Computing;869Language
- en
Editor(s)
Kohei Arai, Supriya Kapoor, Rahul BhatiaLocation
LondonAdministrator link
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