Highway Autopilot Using Deep Reinforcement Learning and Graph Neural Networks
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Sähkötekniikan korkeakoulu |
Master's thesis
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Authors
Date
2023-12-11
Department
Major/Subject
Autonomous Systems
Mcode
ELEC3055
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
46+1
Series
Abstract
Autonomous vehicles hold promise for enhancing road traffic safety by minimizing human errors through optimal decision-making approaches. The development of decision-making algorithms for complex traffic scenarios is crucial for the successful implementation and wider adoption of autonomous vehicles. In the context of a multi-laned highway traffic scenario, lane changing is a challenging coordination problem due to high speeds and the variation in the number of surrounding road users. Further, a robust input representation of the highway traffic is required for the autonomous vehicle to make optimal decisions for safe navigation. Learning-based or data-driven methods offer a promising solution for developing these robust decision-making algorithms. In this thesis, the feasibility of developing a low-level controller model based on deep reinforcement learning and graph neural networks which can be used to safely change lanes in a highway with traffic in a simulated environment is investigated. The dependence on the input graph traffic configuration on the model’s performance is shown for a given simulation environment configuration. Further, the usefulness of the graph attention mechanism for understanding the decision-making of the trained policy is explored.Description
Supervisor
Kyrki, VilleThesis advisor
Laine, LeoKeywords
deep reinforcement learning, graph neural networks, soft actor-critic, graph attention network, multi-laned Highway