A Roadside Decision-Making Methodology Based on Deep Reinforcement Learning to Simultaneously Improve the Safety and Efficiency of Merging Zone.

MLA

Hu, Jinchao, et al. “A Roadside Decision-Making Methodology Based on Deep Reinforcement Learning to Simultaneously Improve the Safety and Efficiency of Merging Zone.” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, Oct. 2022, pp. 18620–31. EBSCOhost, https://doi.org/10.1109/TITS.2022.3157910.

APA

Hu, J., Li, X., Cen, Y., Xu, Q., Zhu, X., & Hu, W. (2022). A Roadside Decision-Making Methodology Based on Deep Reinforcement Learning to Simultaneously Improve the Safety and Efficiency of Merging Zone. IEEE Transactions on Intelligent Transportation Systems, 23(10), 18620–18631. https://doi.org/10.1109/TITS.2022.3157910

Chicago

Hu, Jinchao, Xu Li, Yanqing Cen, Qimin Xu, Xuefen Zhu, and Weiming Hu. 2022. “A Roadside Decision-Making Methodology Based on Deep Reinforcement Learning to Simultaneously Improve the Safety and Efficiency of Merging Zone.” IEEE Transactions on Intelligent Transportation Systems 23 (10): 18620–31. doi:10.1109/TITS.2022.3157910.