DeepGuard: a framework for safeguarding autonomous driving systems from inconsistent behaviour
Abstract
The deep neural networks (DNNs)-based autonomous driving systems (ADSs) are expected to reduce road accidents and improve safety in the transportation domain as it removes the factor of human error from driving tasks. The DNN-based ADS sometimes may exhibit erroneous or unexpected behaviours due to unexpected driving conditions which may cause accidents. Therefore, safety assurance is vital to the ADS. However, DNN-based ADS is a highly complex...
Paper Details
Title
DeepGuard: a framework for safeguarding autonomous driving systems from inconsistent behaviour
Published Date
Nov 23, 2021
Volume
29
Issue
1
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