Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving (PDF)
(Sprache: Englisch)
Approximately 3700 people die in traffic accidents each day. The mostfrequent cause of accidents is human error. Autonomous driving can significantly reduce thenumber of traffic accidents. To prepare autonomous vehicles for road traffic, the software...
sofort als Download lieferbar
eBook (pdf)
17.90 €
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
Produktdetails
Produktinformationen zu „Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving (PDF)“
Approximately 3700 people die in traffic accidents each day. The mostfrequent cause of accidents is human error. Autonomous driving can significantly reduce thenumber of traffic accidents. To prepare autonomous vehicles for road traffic, the software andsystem components must be thoroughly validated and tested. However, due to their criticality, thereis only a limited amount of data for safety-critical driving scenarios. Such driving scenarios canbe represented in the form of time series. These represent the corresponding kinematic vehiclemovements by including vectors of time, position coordinates, velocities, and accelerations. Thereare several ways to provide such data. For example, this can be done in the form of a kinematicmodel. Alternatively, methods of artificial intelligence or machine learning can be used. These arealready being widely used in the development of autonomous vehicles. For example, generativealgorithms can be used to generate safety-critical driving data. A novel taxonomy for the generationof time series and suitable generative algorithms will be described in this paper. In addition, agenerative algorithm will be recommended and used to demonstrate the generation of time seriesassociated with a typical example of a driving-critical scenario.
Bibliographische Angaben
- 2021, 30 Seiten, Englisch
- Verlag: Cuvillier Verlag
- ISBN-10: 3736964536
- ISBN-13: 9783736964532
- Erscheinungsdatum: 21.06.2021
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Größe: 2.74 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
Family Sharing
eBooks und Audiobooks (Hörbuch-Downloads) mit der Familie teilen und gemeinsam genießen. Mehr Infos hier.
Kommentar zu "Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving"
0 Gebrauchte Artikel zu „Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving“
Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
---|
Schreiben Sie einen Kommentar zu "Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving".
Kommentar verfassen