Probabilistische Vorhersage von Fahrstreifenwechseln für hochautomatisiertes Fahren auf Autobahnen

Probabilistische Vorhersage von Fahrstreifenwechseln für hochautomatisiertes Fahren auf Autobahnen

A concept for time-related forecasts of lane change maneuvers in highway scenarios is presented within the present work. Automated driving systems rely on understanding the driving environment to fulfill their driving task transparently and safely. This involves the perception of the driving environ...

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Bibliographic Details
Main Author: Weiser, Andreas (auth)
Format: Electronic Book Chapter
Published: KIT Scientific Publishing 2019
Series:Schriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie
Subjects:
Technology: general issues
Fahrstreifenwechsel
Automatisches Fahren
Maschinelles Lernen
dynamic Bayesian networks
Dynamische Bayes'sche Netzwerke
lane change
machine learning
automated driving
Online Access:DOAB: download the publication
DOAB: description of the publication
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Summary:A concept for time-related forecasts of lane change maneuvers in highway scenarios is presented within the present work. Automated driving systems rely on understanding the driving environment to fulfill their driving task transparently and safely. This involves the perception of the driving environment as well as its interpretation to detect and predict driving maneuvers of road users.
Physical Description:1 electronic resource (XI, 232 p. p.)
ISBN:KSP/1000082533
9783731507949
Access:Open Access

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