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2016 | OriginalPaper | Chapter

38. Driver Intent Inference and Risk Assessment

Authors : Martin Liebner, Felix Klanner

Published in: Handbook of Driver Assistance Systems

Publisher: Springer International Publishing

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Abstract

Both for driver assistance systems and highly automated driving, the in-depth understanding of traffic situations becomes more and more important. From the viewpoint of a warning driver assistance system, the authors analyze the requirements and challenges of risk assessment and driver intent inference in complex urban scenarios and provide a systematic overview of existing approaches. Furthermore, the ability of each approach to deal with more than two alternative maneuvers, partially observable feature sets, and potential interaction between traffic participants is evaluated. It is found that generative approaches and Bayesian networks in particular show great potential for driver intent inference, but it is also argued that more effort should be put into modeling the driver’s situation awareness. Based on four concrete examples, the benefits of awareness-based situation analysis are demonstrated with respect to the avoidance of unnecessary warnings, the detection of occluded traffic participants, further improvement of the driver intent inference itself, as well as the prediction of the future trajectories of relevant traffic participants.

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Title: Driver Intent Inference and Risk Assessment
Authors: Martin Liebner
Felix Klanner
Publisher: Springer International Publishing
Book: Handbook of Driver Assistance Systems
Print ISBN: 978-3-319-12351-6

Electronic ISBN: 978-3-319-12352-3

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-12352-3_39

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