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In this paper, we present a general methodology to estimate safety related parameter values of cooperative cyber-physical system-of- systems. As a case study, we consider a vehicle platoon model equipped with a novel distributed protocol for coordinated emergency braking. The estimation methodology is based on learning-based testing; which is an approach to automated requirements testing that combines machine learning with model checking. Our methodology takes into account vehicle dynamics, control algorithm design, inter-vehicle communication protocols and environmental factors such as message packet loss rates. Empirical measurements from road testing of vehicle-to-vehicle communication in a platoon are modeled and used in our case study. We demonstrate that the minimum global time headway for our platoon model equipped with the CEBP function scales well with respect to platoon size.
safety boundaries, coordinated braking, vehicle platoon, Datavetenskap (datalogi), learning-based testing, Co-CPS, quantitative analysis, Computer Sciences, vehicle platoon, learning-based testing, Co-CPS, safety boundaries, quantitative analysis, coordinated braking
safety boundaries, coordinated braking, vehicle platoon, Datavetenskap (datalogi), learning-based testing, Co-CPS, quantitative analysis, Computer Sciences, vehicle platoon, learning-based testing, Co-CPS, safety boundaries, quantitative analysis, coordinated braking
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 17 | |
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
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