Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/94E61E45-E999-4A4E-92E8-31704ADD8E45 | ||
| Year | 2020 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | M. Karimi, C. Roncoli, C. Alecsandru, and M. Papageorgiou, “Cooperative merging control via trajectory optimization in mixed vehicular traffic,” Transp. Res. Pt. C-Emerg. Technol., vol. 116, Jul. 2020. doi: 10.1016/j.trc.2020.102663 https://doi.org/10.1016/j.trc.2020.102663 | ||
| Appears in Collections |
A major challenging issue related to the emerging mixed traffic vehicular system, composed of connected and automated vehicles (CAVs) together with human-driven vehicles, is the lack of adequate modeling and control framework, especially at traffic bottlenecks such as highway merging areas. A hierarchical control framework for merging areas is first outlined, where we assume that the merging sequence is decided by a higher control level. The focus of this paper is the lower level of the control framework that establishes a set of control algorithms for cooperative CAV trajectory optimization, defined for different merging scenarios in the presence of mixed traffic. To exploit complete cooperation flexibility of the vehicles, we identify six scenarios, consisting of triplets of vehicles, defined based on the different combinations of CAVs and conventional vehicles. For each triplet, different consecutive movement phases along with corresponding desired distance and speed set-points are designed. Through the movement phases, the CAVs engaged in the triplet cooperate to determine their optimal trajectories aiming at facilitating an efficient merging maneuver, while complying with realistic constraints related to safety and comfort of vehicle occupants. Distinct models are considered for each triplet, and a Model Predictive Control scheme is employed to compute the cooperative optimal control inputs, in terms of acceleration of CAVs, accounting also for human-driven vehicles’ uncertainties, such as drivers’ reaction time and desired speed tracing error. Simulation investigations demonstrate that the proposed cooperative merging algorithms ensure efficient and smooth merging maneuvers while satisfying all the prescribed constraints.
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