Reinforcement learning-based traffic signal control systems (RLTSC) can enhance dynamic adaptability, save vehicle travelling time and promote intersection capacity. However, the existing RLTSC methods do not consider the driver's response time requirement, so the systems often face efficiency limitations and implementation difficulties. We propose the advance decision-making reinforcement learning traffic signal control (AD-RLTSC) algorithm to improve traffic efficiency while ensuring safety in mixed traffic environment. First, the relationship between the intersection perception range and the signal control period is established and the trust region state (TRS) is proposed. Then, the scalable state matrix is dynamically adjusted to decide the future signal light status. The decision will be displayed to the human-driven vehicles (HDVs) through the bi-countdown timer mechanism and sent to the nearby connected automated vehicles (CAVs) using the wireless network rather than be executed immediately. HDVs and CAVs optimize the driving speed based on the remaining green (or red) time. Besides, the Double Dueling Deep Q-learning Network algorithm is used for reinforcement learning training; a standardized reward is proposed to enhance the performance of intersection control and prioritized experience replay is adopted to improve sample utilization. The experimental results on vehicle micro-behaviour and traffic macro-efficiency showed that the proposed AD-RLTSC algorithm can simultaneously improve both traffic efficiency and traffic flow stability.
Connected autonomous intelligent agents (AIA) can improve intersection performance and resilience for the transportation infrastructure. An agent is an autonomous decision maker whose decision making is determined internally but may be altered by interactions with the environment or with other agents. Implementing agent-based modeling techniques to advance communication for more appropriate decision making can benefit autonomous vehicle technology.
This research examines vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and infrastructure to infrastructure (I2I) communication strategies that use gathered data to ensure these agents make appropriate decisions under operational circumstances. These vehicles and signals are modeled to adapt to the common traffic flow of the intersection to ultimately find an traffic flow that will minimizes average vehicle transit time to improve intersection efficiency. By considering each light and vehicle as an agent and providing for communication between agents, additional decision-making data can be transmitted. Improving agent based I2I communication and decision making will provide performance benefits to traffic flow capacities.
more » « less- Award ID(s):
- 1633608
- PAR ID:
- 10341166
- Date Published:
- Journal Name:
- c. ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DETC2020-22568
