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Future of Smart Parking: Automated Valet Parking Using Deep Q-Learning

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1004))

Abstract

Population growth and increasing the number of vehicles are causing many different economic and environmental problems. One of the crucial ones is finding a parking space. In order to deal with this issue, we can construct new parking lots or optimizing the old ones. In fact, building new parking costs a lot and will destroy nature. Most of the time there is not enough space to build a new one in cities. Thanks to the evolution of IoT, the implementation of smart parking based on IoT is possible. In this paper, We are focusing on an eco-friendly system called Automated Valet Parking which uses hybrid robotic valets in smart parking and helps optimizing parking space usage with Deep Q-Learning which is a reinforcement learning method, In order to achieve high performance. Because reinforcement learning is a goal absorbing algorithm and by well-defining the objective function (The goal), We can achieve optimum policy.

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Correspondence to Nastaran Shoeibi or Niloufar Shoeibi .

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Shoeibi, N., Shoeibi, N. (2020). Future of Smart Parking: Automated Valet Parking Using Deep Q-Learning. In: Herrera-Viedma, E., Vale, Z., Nielsen, P., Martin Del Rey, A., Casado Vara , R. (eds) Distributed Computing and Artificial Intelligence, 16th International Conference, Special Sessions. DCAI 2019. Advances in Intelligent Systems and Computing, vol 1004. Springer, Cham. https://doi.org/10.1007/978-3-030-23946-6_20

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