Autonomous Vehicle Safety: An Interdisciplinary Challenge

Ensuring the safety of fully autonomous vehicles requires a multi-disciplinary approach across all the levels of functional hierarchy, from hardware fault tolerance, to resilient machine learning, to cooperating with humans driving conventional vehicles, to validating systems for operation in highly...

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Published in:IEEE intelligent transportation systems magazine 2017-01, Vol.9 (1), p.90-96
Main Authors: Koopman, Philip, Wagner, Michael
Format: Article
Language:English
Subjects:
Autonomous vehicles
Fault tolerance
Hardware
ISO Standards
Machine learning
Road traffic
Safety
Training
Vehicle safety
Vehicles
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description Ensuring the safety of fully autonomous vehicles requires a multi-disciplinary approach across all the levels of functional hierarchy, from hardware fault tolerance, to resilient machine learning, to cooperating with humans driving conventional vehicles, to validating systems for operation in highly unstructured environments, to appropriate regulatory approaches. Significant open technical challenges include validating inductive learning in the face of novel environmental inputs and achieving the very high levels of dependability required for full-scale fleet deployment. However, the biggest challenge may be in creating an end-to-end design and deployment process that integrates the safety concerns of a myriad of technical specialties into a unified approach.
doi_str_mv 10.1109/MITS.2016.2583491
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source IEEE Electronic Library (IEL) Journals
subjects Autonomous vehicles
Fault tolerance
Hardware
ISO Standards
Machine learning
Road traffic
Safety
Training
Vehicle safety
Vehicles
title Autonomous Vehicle Safety: An Interdisciplinary Challenge
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