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.
Drivers’ Knowledge of and Preferences for Connected and Automated Vehicles
Connected and automated vehicles (CAVs) offer many potential advantages, including improved traffic flow, reduction of traffic accidents, and increased freedom for adolescents and adults with restricted mobility. However, successful implementation of CAVs depends on several factors, especially acceptance and preferences by people. Specifically, during the earlier stage of deployment, CAVs will have to share the roads with human-driven vehicles (HDVs), which requires communication between CAVs and HDVs regarding their intentions and future actions. Therefore, as a first step in our research program, we conducted a survey of 182 U.S. drivers to assess their knowledge of CAVs and their thoughts about implementation. We report the survey results, accompanied by our interpretations.
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- Award ID(s):
- 2121967
- NSF-PAR ID:
- 10443144
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume:
- 66
- Issue:
- 1
- ISSN:
- 2169-5067
- Page Range / eLocation ID:
- 1457 to 1461
- 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
- Journal Article:
- https://doi.org/10.1177/1071181322661285
