Enhancing Traffic Flow and Safety in Mixed Vehicle Fleets: Mitigating the Influence of Non-Cooperative Vehicles on Autonomous Intersection Management Systems
(2023) 31st International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2023- Abstract
- With the rapid advancement of autonomous vehicle technology, integrating mixed autonomous and non-autonomous vehicles that are not cooperative in vehicular network has become a significant challenge. This paper presents an innovative Autonomous Intersection Management (AIM) system designed to optimize traffic flow and enhance intersection safety in such mixed traffic scenarios. By utilizing vehicle-to-infrastructure (V2I) communication and advanced intersection control algorithms, the AIM system showcases the potential of next-generation vehicular network technologies in revolutionizing intersection management. To evaluate the performance of the AIM system and the impact of non-cooperative vehicles, extensive simulations were conducted... (More)
- With the rapid advancement of autonomous vehicle technology, integrating mixed autonomous and non-autonomous vehicles that are not cooperative in vehicular network has become a significant challenge. This paper presents an innovative Autonomous Intersection Management (AIM) system designed to optimize traffic flow and enhance intersection safety in such mixed traffic scenarios. By utilizing vehicle-to-infrastructure (V2I) communication and advanced intersection control algorithms, the AIM system showcases the potential of next-generation vehicular network technologies in revolutionizing intersection management. To evaluate the performance of the AIM system and the impact of non-cooperative vehicles, extensive simulations were conducted using realistic traffic scenarios and a mixed traffic model. The results demonstrate that the proposed system effectively enhances intersection throughput, and ensures safe and efficient operations, particularly in situations involving a high proportion of autonomous vehicles. Additionally, the system’s robustness is demonstrated by evaluating its performance under various traffic flow rates and considering imperfect wireless communication conditions. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/38069a3b-4c7b-4dc8-8d4a-be5bae28f586
- author
- Chamideh, Seyedezahra LU ; Tärneberg, William LU and Kihl, Maria LU
- organization
- publishing date
- 2023-10
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2023 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 31st International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2023
- conference location
- Split, Croatia
- conference dates
- 2023-09-21 - 2023-09-23
- external identifiers
-
- scopus:85174531038
- ISBN
- 979-8-3503-0107-6
- DOI
- 10.23919/SoftCOM58365.2023.10271599
- language
- English
- LU publication?
- yes
- id
- 38069a3b-4c7b-4dc8-8d4a-be5bae28f586
- date added to LUP
- 2023-10-11 09:57:43
- date last changed
- 2024-02-13 01:59:16
@inproceedings{38069a3b-4c7b-4dc8-8d4a-be5bae28f586, abstract = {{With the rapid advancement of autonomous vehicle technology, integrating mixed autonomous and non-autonomous vehicles that are not cooperative in vehicular network has become a significant challenge. This paper presents an innovative Autonomous Intersection Management (AIM) system designed to optimize traffic flow and enhance intersection safety in such mixed traffic scenarios. By utilizing vehicle-to-infrastructure (V2I) communication and advanced intersection control algorithms, the AIM system showcases the potential of next-generation vehicular network technologies in revolutionizing intersection management. To evaluate the performance of the AIM system and the impact of non-cooperative vehicles, extensive simulations were conducted using realistic traffic scenarios and a mixed traffic model. The results demonstrate that the proposed system effectively enhances intersection throughput, and ensures safe and efficient operations, particularly in situations involving a high proportion of autonomous vehicles. Additionally, the system’s robustness is demonstrated by evaluating its performance under various traffic flow rates and considering imperfect wireless communication conditions.}}, author = {{Chamideh, Seyedezahra and Tärneberg, William and Kihl, Maria}}, booktitle = {{2023 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)}}, isbn = {{979-8-3503-0107-6}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Enhancing Traffic Flow and Safety in Mixed Vehicle Fleets: Mitigating the Influence of Non-Cooperative Vehicles on Autonomous Intersection Management Systems}}, url = {{http://dx.doi.org/10.23919/SoftCOM58365.2023.10271599}}, doi = {{10.23919/SoftCOM58365.2023.10271599}}, year = {{2023}}, }