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  4. The Path to Safe Machine Learning for Automotive Applications
 
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2023
Report
Title

The Path to Safe Machine Learning for Automotive Applications

Abstract
Recent rapid advancement in machine learning (ML) technologies have unlocked the potential for realizing advanced vehicle functions that were previously not feasible using traditional approaches to software development. One prominent example is the area of automated driving. However, there is much discussion regarding whether ML-based vehicle functions can be engineered to be acceptably safe, with concerns related to the inherent difficulty and ambiguity of the tasks to which the technology is applied. This leads to challenges in defining adequately safe responses for all possible situations and an acceptable level of residual risk, which is then compounded by the reliance on training data. The Path to Safe Machine Learning for Automotive Applications discusses the challenges involved in the application of ML to safety-critical vehicle functions and provides a set of recommendations within the context of current and upcoming safety standards. In summary, the potential of ML will only be unlocked for safety-related functions if the inevitable uncertainties associated with both the specification and performance of the trained models can be sufficiently well understood and controlled within the application-specific context.
Author(s)
Burton, Simon  
Fraunhofer-Institut für Kognitive Systeme IKS  
Person Involved
Barbier, Eric
Wayve Technologies
Heyl, Andreas
Robert-Bosch-GmbH
Kuwajima, Hiroshi
DENSO
Mohr, Russel
Qualcomm Technologies
Pitale, Mandar
NVIDIA
Ramesh, S.
General Motors Company  
Savage, Lisa
Aptiv
Stauner, Thomas
BMW Group  
Xiao, Jing
Continental Autonomous Mobility Germany
Publisher
SAE International  
Link
Link
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IUK-Technologie  
Keyword(s)
  • machine learning

  • ML

  • automated vehicle

  • automated driving system

  • safety

  • safety-critical

  • safety-critical system

  • high driving automation

  • level 4

  • conditional driving automation

  • level 3

  • full driving automation

  • level 5

  • automotive

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