|
[1] 中共中央国务院印发《交通强国建设纲要》[N]. 北京:人民日报, 2019. [The central committee of the
communist party of China and the state council "Outline
for building a strong transportation nation"[N]. Beijing:
People's Daily, 2019.]
[2] 戴荣健, 丁川, 鹿应荣, 等. 自动驾驶环境下车辆轨迹及交通信号协同控制[J]. 汽车安全与节能学报, 2019,
10(4): 531-539. [DAI R J, DING C, LU Y R, et al.
Cooperated control of signal and vehicle trajectory under
the autonomous vehicle environment[J]. Journal of
Automotive Safety and Energy Efficiency, 2019, 10(4):
531-539.]
[3] YU C, FENG Y, LIU H X, et al. Integrated optimization
of traffic signals and vehicle trajectories at isolated urban
intersections[J]. Transportation Research Part B:
Methodological, 2018, 112: 89-112.
[4] FENG Y, YU C, LIU H X. Spatiotemporal intersection
control in a connected and automated vehicle
environment[J]. Transportation Research Part C:
Emerging Technologies, 2018, 89: 364-383.
[5] 刘显贵, 王晖年, 洪经纬, 等. 网联环境下信号交叉口车速控制策略及优化[J]. 交通运输系统工程与信息,
2021, 21(2): 82- 90. [LIU X G, WANG H N, HONG J
W, et al. Speed control strategy and optimization of
signalized intersection in network environment[J].
Journal of Transportation Systems Engineering and
Information Technology, 2021, 21(2): 82-90.]
[6] 鹿应荣, 许晓彤, 丁川, 等. 车联网环境下信号交叉口车速控制策略[J]. 交通运输系统工程与信息, 2018, 18
(1): 50-58, 95. [LU Y R, XU X T, DING C, et al. A
speed control strategy at signalized intersection under
connected vehicle environment[J]. Journal of
Transportation Systems Engineering and Information
Technology, 2018, 18(1): 50-58, 95.]
[7] 高志波, 吴志周, 郝威, 等. 智能网联车环境下交叉口车流轨迹优化模型[J]. 交通运输系统工程与信息,
2021, 21(2): 91-97. [GAO Z B, WU Z Z, HAO W, et al.
Vehicle trajectory optimization model for intersection
under the connected and automated vehicles environment
[J]. Journal of Transportation Systems Engineering and
Information Technology, 2021, 21(2): 91-97.]
[8] 刘春禹, 刘永红, 罗霞, 等. 混合交通流环境下单交叉口自动驾驶车辆轨迹优化[J]. 交通运输系统工程与信
息, 2022, 22(2): 154-162. [LIU C Y, LIU Y H, LUO X,
et al. Trajectory optimization of connected vehicles at
isolated intersection in mixed traffic environment[J].
Journal of Transportation Systems Engineering and
Information Technology, 2022, 22(2): 154-162.]
[9] ZHU M, WANG Y, PU Z, et al. Safe, efficient, and
comfortable velocity control based on reinforcement
learning for autonomous driving[J]. Transportation
Research Part C: Emerging Technologies, 2020, 117:
102662.
[10] ZHOU M F, YANG Y, QU X B. Development of an
efficient driving strategy for connected and automated
vehicles at signalized intersections: A reinforcement
learning approach[J]. IEEE Transactions on Intelligent
Transportation Systems, 2019, 21(1): 433-443.
[11] LI J, FOTOUHI A, PAN W, et al. Deep reinforcement
learning-based eco-driving control for connected electric
vehicles at signalized intersections considering traffic
uncertainties[J]. Energy, 2023, 279: 128139.
[12] LIU B, SUN C, WANG B, et al. Adaptive speed planning
of connected and automated vehicles using multi-light
trained deep reinforcement learning[J]. IEEE
Transactions on Vehicular Technology, 2021, 71(4):
3533-3546.
[13] FUJIMOTO S, HOOFH, MEGER D. Addressing function
approximation error in actor-critic methods[C].
Stockholm: International Conference on Machine
Learning, 2018.
[14] KURCZVEIL T, LOPEZ P A, SCHNIEDER E.
Implementation of an energy model and a charging
infrastructure in SUMO[C]. Berlin: Simulation of Urban
Mobility: First International Conference, 2014.
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