The DOI matches its Crossref record; 111 of the 111 references it cites were found in a registry or an index.
Energy saving potentials of connected and automated vehicles
Abstract
Abstract Connected and automated vehicles (CAV) are marketed for their increased safety, driving comfort, and time saving potential. With much easier access to information, increased processing power, and precision control, they also offer unprecedented opportunities for energy efficient driving. This paper is an attempt to highlight the energy saving potential of connected and automated vehicles based on first principles of motion, optimal control theory, and a review of the vast but scattered eco-driving literature. We explain that connectivity to other vehicles and infrastructure allows better anticipation of upcoming events, such as hills, curves, slow traffic, state of traffic signals, and movement of neighboring vehicles. Automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and save energy. Opportunities for cooperative driving could further increase energy efficiency of a group of vehicles by allowing them to move in a coordinated manner. Energy efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles.
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Citations
Cites
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Influence of connected and autonomous vehicles on traffic flow stability and throughput
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Cited by
Impacts of Autonomous Vehicles on Greenhouse Gas Emissions—Positive or Negative?
Massar, M., Reza, I., Rahman, S. M., Abdullah, S. M. H., Jamal, A., & Al-Ismail, F. S. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18115567 · Cited by 146
The impacts of connected autonomous vehicles on mixed traffic flow: A comprehensive review
Pan, Y., Wu, Y., Xu, L., Xia, C., & Olson, D. L. (2024) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.129454 · Cited by 102
Air quality and greenhouse gas implications of autonomous vehicles in Vancouver Canada
Le Hong, Z., & Zimmerman, N. (2021) · Transportation Research Part D: Transport and Environment · doi:10.1016/j.trd.2020.102676 · Cited by 54
Kim, D., Eo, J. S., & Kim, K.-K. K. (2021) · IEEE Access · doi:10.1109/access.2021.3098807 · Cited by 29
Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic
Zhang, K., Chen, K., Li, Z., Chen, J., & Zheng, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3329484 · Cited by 24
Metadata
- Authors
- Vahidi, A., & Sciarretta, A.
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 95
- Pages
- 822-843
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2891048072
- Cited by
- 537
- References
- 111
- FWCI
- 26.99
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
