Evaluation of MHC in CACC Platooning under Disturbance Scenarios

Master Thesis (2024)
Author(s)

X. Dong (TU Delft - Civil Engineering & Geosciences)

Contributor(s)

Simeon C. Calvert – Mentor (TU Delft - Transport and Planning)

A. Zgonnikov – Graduation committee member (TU Delft - Human-Robot Interaction)

L.E. Suryana – Mentor (TU Delft - Transport and Planning)

Faculty
Civil Engineering & Geosciences
More Info
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Publication Year
2024
Language
English
Graduation Date
05-07-2024
Awarding Institution
Delft University of Technology
Programme
['Civil Engineering | Transport and Planning']
Faculty
Civil Engineering & Geosciences
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Abstract

Platooning has become a useful area for better transportation efficiency on highway driving. As Cooperative and Automated Vehicles continue to evolve and integrate , it is important to have insights into their implications, emphasizing the need for rigorous real-world assessments. In general, platoon formation is monitored by Cooperative Adaptive Cruise Control (CACC), which uses real-time vehicle-to-vehicle (V2V) communication to exchange vehicle status information, improving the control reaction as platoon members adjust to their surroundings. Automated systems can normally drive vehicles to perform planned behaviors based on the pre-setting by humans, but if the platoon encounters disturbances, the extent to which the automated system can still follow human intentions is still unknown. This research uses field operational test (FOT) data from the CACC platoon on an arterial corridor to assess the platoon's performance when disrupted during the test. This research applies the concept of meaningful human control (MHC) with focus on tracking condition. Additionally, this study will focus on human 'reasons', both distal and proximal. An evaluation framework for platoons is created by categorizing 'Tracking' into three main metrics: comfort, safety, and local stability. Furthermore, this study demonstrates that disturbance has variable degrees of detrimental impact on the platoon's tracking state, and that these effects may be recovered when the disturbance has concluded; however, different disturbance situations indicate different recoveries. The evaluation methodology of this paper provides insight into the tracking performance of CAVs, which can help road authorities build infrastructure for their wider deployment of CAVs. Last but not least, this study may provide guidance to automation technology organizations and automobile manufacturers on how to develop vehicles so that they follow human reasons more closely.

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