Improved Virtual Vehicles Design for On-Ramp Cooperative Merging
Virtual vehicle design has received a lot of attention recently and is commonly used to assist connected and autonomous vehicles (CAVs) in achieving cooperative merging. However, very few virtual vehicles have been designed to assist connected and human-driven vehicles (CHVs) in achieving cooperative control. This paper proposes an improved virtual vehicle design methodology that extends the application of the virtual vehicle concept to CHVs by considering driver compliance to ensure that CHVs and CAVs achieve cooperative merging. This method mainly includes two parts: the computation of vehicle cooperative state based on car-following model and the redesign of virtual vehicles. This paper also analyzes the stability of the mixed platoon composed of CHV and CAV, and obtains the conditions for the string stability of the mixed platoon. Simulation experiments demonstrate that the proposed method can overcome the problems caused by driver compliance in the collaborative process of the on-ramp area. Additionally, the proposed method has the advantages in reducing fuel consumptions and improving the comfort of drivers and passengers. The experiments based on SUMO platform show that the method can reduce traffic density and increase average speed, and that the improvement is more significant at lower CAV penetration rates. It will meet the real-time requirements of daily traffic by analyzing the computation time.
Authors
- Dihua Sun (ORCID: https://orcid.org/0000-0001-6559-1495)
- Liuping Wang (ORCID: https://orcid.org/0000-0002-7604-1322)
- Min Zhao (ORCID: https://orcid.org/0000-0003-1648-679X)
- Shihui Wang (ORCID: https://orcid.org/0000-0002-4978-5756)
Institutions
- Chongqing University (CN)
Publication Details
- Journal
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2025-04-08
- DOI
- https://doi.org/10.1109/tits.2025.3554556
- Citations
- 2
- Primary Topic
- Transportation and Mobility Innovations
- Type
- article
- Field-Weighted Citation Impact
- 1.37
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities