Abstract
Since the 80s, the building of learn and test data bases for learning-based systems (i.e. neural networks) had to cope with problems of picking representative examples and measuring the generalization/the score of the system. And of course, real open world applications cannot be fully tested. It seems that artificial vision-based ADAS now discover the same question, and then, may use the same solutions, involving the same methodology (A.G.E.N.D.A.), using design of experiments and data analysis tools.
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- ADAS:
-
Advanced Driver Assistance Systems
- s/n ratio:
-
signal/noise ratio
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Yahiaoui, G., Da Silva Dias, P. (2016). Methodology for ADAS Validation: Potential Contribution of Other Scientific Fields Which Have Already Answered the Same Questions. In: Langheim, J. (eds) Energy Consumption and Autonomous Driving. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-319-19818-7_14
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DOI: https://doi.org/10.1007/978-3-319-19818-7_14
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