Performance validation of vehicle platooning through intelligible analytics

Mongelli, Maurizio; Ferrari, Enrico; Muselli, Marco; Fermi, Alessandro

doi:10.1049/iet-cps.2018.5055

Cited by 16 publications

(23 citation statements)

References 27 publications

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“…Safety regions research is a well-known task for machine learning [11], [12], [13] and the main focus is to avoid false negatives, i.e., including in the safe region unsafe points. In this section, two methods for the research of zero FNR regions are proposed: the first one is based simply on the reduction of the SVDD radius until only safe points are enclosed in the SVDD shape, the second one instead performs successive iterations of the SVDD on the safe region until there are no more negative points.…”

Section: Zero Fnr Regions With Svddmentioning

confidence: 99%

“…We now consider how to make the SVDD explainable in order to explicit the inherent logic and use the extracted rules for further safety envelope tuning as in [12].…”

Section: Rules Extractionmentioning

confidence: 99%

“…As in [12] we applied these rules with the goal of maximizing the number of safe points (that is the number of points in the target class) while keeping FNR at zero. This is possible by performing rule tuning as in [12] but SVDD allows for much more flexibility. .…”

Section: Rules Extraction From Svddmentioning

confidence: 99%

“…The concept of Region of Attraction (ROA) is fundamental in the stability analysis of dynamical systems [20], [35] and it is topical when safety of cyber physical system should be preserved with zero (probabilistic) error [12], [13].…”

Section: Roa Inferencementioning

confidence: 99%

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Reliable AI through SVDD and rule extraction

Carlevaro¹

2021

Preprint

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<div><div><div><p>The proposed paper addresses how Support Vector Data Description (SVDD) can be used to detect safety regions with zero statistical error. It provides a detailed methodology for the applicability of SVDD in real-life applications, such as Vehicle Platooning, by addressing common machine learning problems such as parameter tuning and handling large data sets. Also, intelligible analytics for knowledge extraction with rules is presented: it is targeted to understand safety regions of system parameters. Results are shown by feeding data through simulation to the train of different rule extraction mechanisms.</p></div></div></div>

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Section: Zero Fnr Regions With Svddmentioning

confidence: 99%

“…We now consider how to make the SVDD explainable in order to explicit the inherent logic and use the extracted rules for further safety envelope tuning as in [12].…”

Section: Rules Extractionmentioning

confidence: 99%

Section: Rules Extraction From Svddmentioning

confidence: 99%

Section: Roa Inferencementioning

confidence: 99%

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Reliable AI through SVDD and rule extraction

Carlevaro¹

2021

Preprint

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“…Ongoing research addresses formal verification to extract evidence of safety conditions [ 68 ]. Authors in [ 69 ] apply machine learning for sensitivity analysis of safety conditions in platooning, under the constraint of no false negative, i.e., avoiding to predict safety (no collision) while collision happens in reality.…”

Section: Safety and Security For Safecop Co-cpssmentioning

confidence: 99%

Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems

Balador

Kouba

Cassioli

et al. 2018

Sensors

Self Cite

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Cooperative Cyber-Physical Systems (Co-CPSs) can be enabled using wireless communication technologies, which in principle should address reliability and safety challenges. Safety for Co-CPS enabled by wireless communication technologies is a crucial aspect and requires new dedicated design approaches. In this paper, we provide an overview of five Co-CPS use cases, as introduced in our SafeCOP EU project, and analyze their safety design requirements. Next, we provide a comprehensive analysis of the main existing wireless communication technologies giving details about the protocols developed within particular standardization bodies. We also investigate to what extent they address the non-functional requirements in terms of safety, security and real time, in the different application domains of each use case. Finally, we discuss general recommendations about the use of different wireless communication technologies showing their potentials in the selected real-world use cases. The discussion is provided under consideration in the 5G standardization process within 3GPP, whose current efforts are inline to current gaps in wireless communications protocols for Co-CPSs including many future use cases.

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Reliable AI Through SVDD and Rule Extraction

Carlevaro

Mongelli

2021

Lecture Notes in Computer Science

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Performance validation of vehicle platooning through intelligible analytics

Cited by 16 publications

References 27 publications

Reliable AI through SVDD and rule extraction

Reliable AI through SVDD and rule extraction

Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems

Reliable AI Through SVDD and Rule Extraction

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