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Research . 2019
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PEGASUS METHOD

Authors: Mazzega, Jens; Lipinski, Daniel; Eberle, Ulrich; Schittenhelm, Helmut; Wachenfeld, Walther;
Abstract

PEGASUS Method: The dream of many car drivers appears like this: while driving, one simply switches to the autopilot, sits back, reads, … However, until these automation systems can actually be used on the roads, a million times, there are still many questions that have to be clarified. In particular: what are the requirements for self-driving vehicles? How can the safety and reliability of these systems be proven? And not to forget: as nice as the future vision of self-driving cars might be – without people behind the wheel, it will not work. In particular, the transfer of responsibility from the driver to the automated system comes with high demands, since the humans no longer have to continuously monitor their driving task and can devote themselves to other activities. But what role will the human factor play in the future? What does technology have to guarantee? And how can optimally shape the interplay between humans and technology? In such situations, there is an enormous demand for research, when it comes to bringing highly-automated vehicles, quickly and safely on the market. In order for such functions to be approved, new and standardized quality standards and methods must be developed through the close cooperation between the research and industry fields. This is what the PEGASUS joint project stands for: project for the establishment of generally accepted quality criteria, tools and methods as well as scenarios and situations for the release of highly-automated driving functions. The objective is to develop a procedure for the testing of automated driving functions, in order to facilitate the rapid implementation of automated driving into practice.

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Keywords

Database, Automated Driving, Scenario-based Development, Autonomous Vehicles, Scenario-based Validation, Simulation

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selected citations
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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).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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