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Game-Theoretic Lane-Changing Decision Making and Payoff Learning for Autonomous Vehicles.
- Source :
- IEEE Transactions on Vehicular Technology; Apr2022, Vol. 71 Issue 4, p3609-3620, 12p
- Publication Year :
- 2022
-
Abstract
- In this paper, the problem of decision making for autonomous vehicles changing lanes is addressed by formulating multiple games in normal form for pairs of agents. This formulation generates the optimal action for the Ego vehicle at a given state and does not consider global optimality for all agents. The payoff matrices of the games are designed based on a user-defined set of rules. The constant parameters of these payoffs are then adjusted using neural learning to generate optimal behavior among the vehicles. An algorithm integrating deep reinforcement learning and game theory, regarded as Nash Q-learning, is included in the decision-making scheme. The applicability of the proposed method in a lane-changing scenario is tested via simulation. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 71
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 156718608
- Full Text :
- https://doi.org/10.1109/TVT.2022.3148972