- By:
- Wang, Zejiang ; Zhou, Anye ; Cook, Adian S; Shao, Yunli ; Xu, Guanhao ; Chen, Jianfei
- Page Number:
- 1-7
- Issue Number:
- 1
- Book Title:
- 2023 IEEE International Automated Vehicle Validation Conference (IAVVC)
- Publication Date:
- December 20, 2023
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- IEEE International Automated Vehicle Validation Conference 2023 (IAVVC)
- Conference Location:
- Austin, TX, Texas, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/IAVVC57316.2023.10328114
Abstract
Cooperative highway onramp merging plays an important role in mitigating highway traffic congestion. A crucial component of a cooperative merging system is the merging sequence strategy, which determines each merging participant's order to reach the merging point. Existing merging sequence strategies can be classified into rule-based and optimization-based approaches. The rule-based strategies can be effortlessly implemented with a light online computational burden. However, they may not achieve the optimal energy efficiency. In contrast, the optimization-based strategies can yield the optimal merging sequence to minimize fuel consumption, but typically involve computationally expensive numerical optimization. To leverage the advantages from both sides, we propose a novel merging sequence strategy that can minimize fuel consumption while avoiding online numerical optimization. The key idea is to analytically formulate the expected fuel consumption of each merging participant. Using a realistic highway onramp scenario based on the NGSIM dataset, we validate the performance and the computational efficiency of the proposed merging sequence strategy via SUMO/SIMULINK joint simulation.
