Evaluation of Connected Vehicle Identification-Aware Mixed Traffic Freeway Cooperative Merging
<|reference_start|>Evaluation of Connected Vehicle Identification-Aware Mixed Traffic Freeway Cooperative Merging: Cooperative on-ramp merging control for connected automated vehicles (CAVs) has been extensively investigated. However, they did neglect the connected vehicle identification process, which is a must for CAV cooperations. In this paper, we introduced a connected vehicle identification system (VIS) into the on-ramp merging control process for the first time and proposed an evaluation framework to assess the impacts of VIS on on-ramp merging performance. First, the mixed-traffic cooperative merging problem was formulated. Then, a real-world merging trajectory dataset was processed to generate dangerous merging scenarios. Aiming at resolving the potential collision risks in mixed traffic where CAVs and traditional human-driven vehicles (THVs) coexist, we proposed on-ramp merging strategies for CAVs in different mixed traffic situations considering the connected vehicle identification process. The performances were evaluated via simulations. Results indicated that while safety was assured for all cases with CAVs, the cases with VIS had delayed initiation of cooperation, limiting the range of cooperative merging and leading to increased fuel consumption and acceleration variations.<|reference_end|>
@article{liu2024evaluation,
    title={Evaluation of Connected Vehicle Identification-Aware Mixed Traffic
  Freeway Cooperative Merging},
    author={Haoji Liu, Fatemeh Jahedinia, Zeyu Mu, B. Brian Park},
    journal={2024 IEEE Intelligent Vehicles Symposium (IV)},
    year={2024},
    doi={10.1109/IV55156.2024.10588618},
    archivePrefix={arXiv},
    eprint={2405.12464},
    primaryClass={eess.SY cs.SY}
}