The Path To Safe Machine Learning For Automotive Applications
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The Path to Safe Machine Learning for Automotive Applications
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. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2023023
Navigating the Evolving Landscape of Safety Standards for Machine Learning-based Road Vehicle Functions
ML approaches to solving some of the key perception and decision challenges in automated vehicle functions are maturing at an incredible rate. However, the setbacks experienced during initial attempts at widespread deployment have highlighted the need for a careful consideration of safety during the development and deployment of these functions. To better control the risk associated with this storm of complex functionality, open operating environments, and cutting-edge technology, there is a need for industry consensus on best practices for achieving an acceptable level of safety. Navigating the Evolving Landscape of Safety Standards for Machine Learning-based Road Vehicle Functions provides an overview of standards relevant to the safety of ML-based vehicle functions and serves as guidance for technology providers—including those new to the automotive sector—on how to interpret the evolving standardization landscape. The report also contains practical guidance, along with an example from the perspective of a developer of an ML-based perception function on how to interpret the requirements of these standards. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2024017
Computational Intelligence in Automotive Applications
Author: Danil Prokhorov
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-05-30
This edited volume is the first of its kind and provides a representative sample of contemporary computational intelligence (CI) activities in the area of automotive technology. All chapters contain overviews of the state-of-the-art.