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How do humanlike behaviors of connected autonomous vehicles affect traffic conditions in mixed traffic?
Citation Link: https://doi.org/10.15480/882.9381
Publikationstyp
Journal Issue
Publikationsdatum
2024-03
Sprache
English
Enthalten in
Volume
16
Issue
6
Start Page
1
End Page
20
Article Number
2402
Citation
Sustainability 16 (6): 2402 (2024-03)
Publisher DOI
Scopus ID
Publisher
MDPI
Peer Reviewed
true
Different methodologies are being used to study the effects of autonomous vehicles (AVs) in mixed traffic to exhibit the interactions between autonomous and human-driven vehicles (HVs). Microscopic simulation tools are popular in such an assessment, as they offer the possibility to experiment in economical, robust, and optimistic ways. A lack of reliable real-world data (also known as natural data) to calibrate and evaluate the connected autonomous vehicle (CAV) simulation model is a major challenge. To deal with this situation, one interesting methodology could be to deal with the CAVs as conventional human-driven vehicles and predict their possible characteristics based on the simulation inputs. The conventional human-driven vehicles from the real world, in this methodology, come to act as a benchmark to offer the measure of effectiveness (MoE) for the calibration and validation. For the three most common driving behaviors, a sensitivity analysis of the behaviors of AVs and an effective assessment of CAVs in a mixed traffic environment were performed to explore the humanlike behaviors of the autonomous technology. The findings show that, up to a certain point, which is directly related to the quantity of interacting vehicles, the impact of CAVs is typically favorable. This study validates the approach and supports past studies by showing that CAVs perform better than AVs in terms of their traffic performance and safety aspects. On top of that, the sensitivity analysis shows that enhancements in the technology are required to obtain the maximum advantages.
Schlagworte
autonomous vehicle
connected autonomous vehicle traffic performance
sensitivity analysis
DDC Class
380: Commerce, Communications, Transport
Projekt(e)
Marie Skłodowska Curie grant
European Union’s Horizon
More Funding Information
This research received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement No 754462.
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