Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach

References

• Ahn, K., Rakha, H. A., & Park, S. (2013). Ecodrive application: Algorithmic development and preliminary testing. Transportation Research Record: Journal of the Transportation Research Board, 2341(1), 1–11. https://doi.org/10.3141/2341-01
   Google Scholar
• Cao, Z., Jiang, S., Zhang, J., & Guo, H. (2017). A unified framework for vehicle rerouting and traffic light control to reduce traffic congestion. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1958–1973. https://doi.org/10.1109/TITS.2016.2613997
   Web of Science ®Google Scholar
• Cheng, Y., Hu, X., Tang, Q., Qi, H., & Yang, H. (2020). Monte Carlo tree search-based mixed traffic flow control algorithm for arterial intersections. Transportation Research Record, 0(0), 0361198120919746.
   Google Scholar
• Chowdhury, F. R., Wang, P., & Li, P. (2021). Congestion-aware heterogeneous connected automated vehicles cooperative scheduling problems at intersections. Journal of Intelligent Transportation Systems, 1–16. https://doi.org/10.1080/15472450.2021.1990053
   Google Scholar
• Dayi, Q., Yanfeng, J., Tao, W., Bin, L., & Lewei, H. (2021). Research on coordinated control of vehicle’s speed in new mixed traffic flow. Journal of Intelligent Transportation Systems, 1–13. https://doi.org/10.1080/15472450.2021.1973897
   Google Scholar
• Du, Z., HomChaudhuri, B., & Pisu, P. (2018). Hierarchical distributed coordination strategy of connected and automated vehicles at multiple intersections. Journal of Intelligent Transportation Systems, 22(2), 144–158. https://doi.org/10.1080/15472450.2017.1407930
   Web of Science ®Google Scholar
• Eskandarian, A., Wu, C., & Sun, C. (2019). Research advances and challenges of autonomous and connected ground vehicles. IEEE Transactions on Intelligent Transportation Systems, 22(2), 1–29.
   Web of Science ®Google Scholar
• Ghiasi, A., Li, X., & Ma, J. (2019). A mixed traffic speed harmonization model with connected autonomous vehicles. Transportation Research Part C: Emerging Technologies, 104, 210–233. https://doi.org/10.1016/j.trc.2019.05.005
   Web of Science ®Google Scholar
• Guanetti, J., Kim, Y., & Borrelli, F. (2018). Control of connected and automated vehicles: State of the art and future challenges. Annual Reviews in Control, 45, 18–40. https://doi.org/10.1016/j.arcontrol.2018.04.011
   Web of Science ®Google Scholar
• Guo, Y., & Ma, J. (2021). SCoPTO: signalized corridor management with vehicle platooning and trajectory control under connected and automated traffic environment. Transportmetrica B: Transport Dynamics, 9(1), 673–692. https://doi.org/10.1080/21680566.2021.1938282
   Web of Science ®Google Scholar
• He, X., Liu, H. X., & Liu, X. (2015). Optimal vehicle speed trajectory on a signalized arterial with consideration of queue. Transportation Research Part C: Emerging Technologies, 61, 106–120. https://doi.org/10.1016/j.trc.2015.11.001
   Web of Science ®Google Scholar
• Jiang, H., Hu, J., An, S., Wang, M., & Park, B. B. (2017). Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment. Transportation Research Part C: Emerging Technologies, 79, 290–307. https://doi.org/10.1016/j.trc.2017.04.001
   Web of Science ®Google Scholar
• Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA.
   Google Scholar
• Lee, J., & Park, B. (2012). Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment. IEEE Transactions on Intelligent Transportation Systems, 13(1), 81–90. https://doi.org/10.1109/TITS.2011.2178836
   Web of Science ®Google Scholar
• Liang, X., Guler, S. I., & Gayah, V. V. (2019). Joint optimization of signal phasing and timing and vehicle speed guidance in a connected and autonomous vehicle environment. Transportation Research Record, 2673(4), 70–83. https://doi.org/10.1177/0361198119841285
   Web of Science ®Google Scholar
• Liang, X., Guler, S. I., & Gayah, V. V. (2020). An equitable traffic signal control scheme at isolated signalized intersections using Connected Vehicle technology. Transportation Research Part C: Emerging Technologies, 110, 81–97. https://doi.org/10.1016/j.trc.2019.11.005
   Web of Science ®Google Scholar
• Liang, X., Guler, S. I., & Gayah, V. V. (2020). Traffic signal control optimization in a connected vehicle environment considering pedestrians. Transportation Research Record, 2674(10), 499–511. https://doi.org/10.1177/0361198120936268
   Web of Science ®Google Scholar
• Ma, J., Li, X., Zhou, F., Hu, J., & Park, B. B. (2017). Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: Computational issues and optimization. Transportation Research Part B: Methodological, 95, 421–441. https://doi.org/10.1016/j.trb.2016.06.010
   Web of Science ®Google Scholar
• Mirheli, A., Hajibabai, L., & Hajbabaie, A. (2018). Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment. Transportation Research Part C: Emerging Technologies, 92, 412–425. https://doi.org/10.1016/j.trc.2018.04.026
   Web of Science ®Google Scholar
• Mirheli, A., Tajalli, M., Hajibabai, L., & Hajbabaie, A. (2019). A consensus-based distributed trajectory control in a signal-free intersection. Transportation Research Part C: Emerging Technologies, 100, 161–176. https://doi.org/10.1016/j.trc.2019.01.004
   Web of Science ®Google Scholar
• Walraven, E., Spaan, M. T. J., & Bakker, B. (2016). Traffic flow optimization: A reinforcement learning approach. Engineering Applications of Artificial Intelligence, 52, 203–212. https://doi.org/10.1016/j.engappai.2016.01.001
   Web of Science ®Google Scholar
• Wang, M., Daamen, W., Hoogendoorn, S. P., & van Arem, B. (2014). Rolling horizon control framework for driver assistance systems. Part I: Mathematical formulation and non-cooperative systems. Transportation Research Part C: Emerging Technologies, 40, 271–289. https://doi.org/10.1016/j.trc.2013.11.023
   Web of Science ®Google Scholar
• Wu, Z., Gao, Z., Hao, W., & Ma, J. (2020). An optimal longitudinal control strategy of platoons using improved particle swarm optimization. Journal of Advanced Transportation, 2020, 8822117.
   Web of Science ®Google Scholar
• Wu, X., He, X., Yu, G., Harmandayan, A., & Wang, Y. (2015). Energy-optimal speed control for electric vehicles on signalized arterials. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2786–2796. https://doi.org/10.1109/TITS.2015.2422778
   Web of Science ®Google Scholar
• Yang, K., Guler, S. I., & Menendez, M. (2016). Isolated intersection control for various levels of vehicle technology: Conventional, connected, and automated vehicles. Transportation Research Part C: Emerging Technologies, 72, 109–129. https://doi.org/10.1016/j.trc.2016.08.009
   Web of Science ®Google Scholar
• Yao, H., Cui, J., Li, X., Wang, Y., & An, S. (2018). A trajectory smoothing method at signalized intersection based on individualized variable speed limits with location optimization. Transportation Research Part D: Transport and Environment, 62, 456–473. https://doi.org/10.1016/j.trd.2018.03.010
   Web of Science ®Google Scholar
• Zhou, F., Li, X., & Ma, J. (2017). Parsimonious shooting heuristic for trajectory design of connected automated traffic part I: Theoretical analysis with generalized time geography. Transportation Research Part B: Methodological, 95, 394–420. https://doi.org/10.1016/j.trb.2016.05.007
   Web of Science ®Google Scholar
• Zhou, M., Yu, Y., & Qu, X. (2020). Development of an efficient driving strategy for connected and automated vehicles at signalized intersections: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems, 21(1), 433–443. https://doi.org/10.1109/TITS.2019.2942014
   Web of Science ®Google Scholar