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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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| Main Authors: | , , , , |
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| Format: | Default Conference proceeding |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/2134/32720 |
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| _version_ | 1818167874843312128 |
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| author | Varuna De-Silva Xiongzhao Wang Ali Aladagli Ahmet Kondoz Erhan Ekmekcioglu |
| author_facet | Varuna De-Silva Xiongzhao Wang Ali Aladagli Ahmet Kondoz Erhan Ekmekcioglu |
| author_sort | Varuna De-Silva (2603902) |
| collection | Figshare |
| description | 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. |
| format | Default Conference proceeding |
| id | rr-article-9464456 |
| institution | Loughborough University |
| publishDate | 2018 |
| record_format | Figshare |
| spelling | rr-article-94644562018-11-08T00:00:00Z An agent-based modelling framework for driving policy learning in connected and autonomous vehicles Varuna De-Silva (2603902) Xiongzhao Wang (8118827) Ali Aladagli (2567308) Ahmet Kondoz (1384131) Erhan Ekmekcioglu (1383846) Agent-based learning Reinforcement learning Driving policy Data driven control Imitation learning 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. 2018-11-08T00:00:00Z Text Conference contribution 2134/32720 https://figshare.com/articles/conference_contribution/An_agent-based_modelling_framework_for_driving_policy_learning_in_connected_and_autonomous_vehicles/9464456 CC BY-NC-ND 4.0 |
| spellingShingle | Agent-based learning Reinforcement learning Driving policy Data driven control Imitation learning Varuna De-Silva Xiongzhao Wang Ali Aladagli Ahmet Kondoz Erhan Ekmekcioglu An agent-based modelling framework for driving policy learning in connected and autonomous vehicles |
| title | An agent-based modelling framework for driving policy learning in connected and autonomous vehicles |
| title_full | An agent-based modelling framework for driving policy learning in connected and autonomous vehicles |
| title_fullStr | An agent-based modelling framework for driving policy learning in connected and autonomous vehicles |
| title_full_unstemmed | An agent-based modelling framework for driving policy learning in connected and autonomous vehicles |
| title_short | An agent-based modelling framework for driving policy learning in connected and autonomous vehicles |
| title_sort | agent-based modelling framework for driving policy learning in connected and autonomous vehicles |
| topic | Agent-based learning Reinforcement learning Driving policy Data driven control Imitation learning |
| url | https://hdl.handle.net/2134/32720 |