The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach
Open accessAbstract
The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle's driving behaviour will no longer be solely based on the driver's limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications.
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Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas
Zhu, J., Tasic, I., & Qu, X. (2022) · Multimodal Transportation · doi:10.1016/j.multra.2022.100005 · Cited by 88
Self-citationHua, M., Qi, X., Chen, D., Jiang, K., Liu, Z. E., Sun, H., Zhou, Q., & Xu, H. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3574280 · Cited by 88
“We then delve into various prominent MARL algorithms, establishing the context to enhance the understanding of our review.”
Network traffic instability with automated driving and cooperative merging
Gu, Z., Wang, Z., Liu, Z., & Saberi, M. (2022) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2022.103626 · Cited by 57
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Metadata
- Authors
- Zhou, M., Yu, Y., & Qu, X.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2020-01
- Volume
- 21
- Issue
- 1
- Pages
- 433-443
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2976036462
- Cited by
- 299
- References
- 48
- FWCI
- 17.76
- Type
- journal article
- Language
- en
- Open access
- green
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- 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. https://doi.org/10.1109/tits.2019.2942014
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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