Combating Stop-and-Go Wave Problem at a Ring Road Using Deep Reinforcement Learning Based Autonomous Vehicles 


Vol. 46,  No. 10, pp. 1667-1682, Oct.  2021
10.7840/kics.2021.46.10.1667


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  Abstract

With the rapid development of artificial intelligence, autonomous driving has recently attracted considerable attention. This paper aims to use an autonomous vehicle to improve road flow by solving the stop-and-go-wave problem on a ring road. We design a special model of Markov decision process model to solve stop-and-go-wave and use three deep reinforcement learning algorithms to train autonomous vehicles: Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3). We then compare their driving patterns and performances. We confirmed that an autonomous vehicle on the ring road could control the flow of multiple non-autonomous vehicles with an extensive simulation study, thus successfully solving the stop-and-go wave problem.

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  Cite this article

[IEEE Style]

D. Lee and M. Kwon, "Combating Stop-and-Go Wave Problem at a Ring Road Using Deep Reinforcement Learning Based Autonomous Vehicles," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 10, pp. 1667-1682, 2021. DOI: 10.7840/kics.2021.46.10.1667.

[ACM Style]

Dongsu Lee and Minhae Kwon. 2021. Combating Stop-and-Go Wave Problem at a Ring Road Using Deep Reinforcement Learning Based Autonomous Vehicles. The Journal of Korean Institute of Communications and Information Sciences, 46, 10, (2021), 1667-1682. DOI: 10.7840/kics.2021.46.10.1667.

[KICS Style]

Dongsu Lee and Minhae Kwon, "Combating Stop-and-Go Wave Problem at a Ring Road Using Deep Reinforcement Learning Based Autonomous Vehicles," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 10, pp. 1667-1682, 10. 2021. (https://doi.org/10.7840/kics.2021.46.10.1667)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880