Projects per year
Abstract
This paper addresses the problem of realtime classificationbased machine perception, exemplified by a mobile autonomous system that must continually check that a designated area ahead is free of hazards. Such hazards must be identified within a specified time. In practice, classifiers are imperfect; they exhibit functional uncertainty. In the majority of cases, a given classifier will correctly determine whether there is a hazard or the area ahead is clear. However, in other cases it may produce false positives, i.e. indicate hazard when the area is clear, or false negatives, i.e. indicate clear when there is in fact a hazard. The former are undesirable since they reduce quality of service, whereas the latter are a potential safety concern. A stringent constraint is therefore placed on the maximum permitted probability of false negatives. Since this requirement may not be achievable using a single classifier, one approach is to (logically) OR the outputs of multiple disparate classifiers together, setting the final output to hazard if any of the classifiers indicates hazard. This reduces the probability of false negatives; however, the tradeoff is an inevitably increase in the probability of false positives and an increase in the overall execution time required.
In this paper, we provide optimal algorithms for the scheduling of classifiers that minimize the probability of false positives, while meeting both a latency constraint and a constraint on the maximum acceptable probability of false negatives. The classifiers may have arbitrary statistical dependences between their functional behaviors (probabilities of correct identification of hazards), as well as variability in their execution times, characterized by typical and worstcase values.
In this paper, we provide optimal algorithms for the scheduling of classifiers that minimize the probability of false positives, while meeting both a latency constraint and a constraint on the maximum acceptable probability of false negatives. The classifiers may have arbitrary statistical dependences between their functional behaviors (probabilities of correct identification of hazards), as well as variability in their execution times, characterized by typical and worstcase values.
Original language  English 

Title of host publication  RTNS '23: Proceedings of the 31st International Conference on RealTime Networks and Systems 
Publisher  ACM 
Pages  143154 
Number of pages  12 
ISBN (Print)  9781450399838 
DOIs  
Publication status  Published  6 Jun 2023 
Event  31st International Conference on RealTime Networks and Systems  Duration: 6 Jun 2023 → 8 Jun 2023 
Conference
Conference  31st International Conference on RealTime Networks and Systems 

Abbreviated title  RTNS 2023 
Period  6/06/23 → 8/06/23 
Bibliographical note
© 2023 Copyright held by the owner/author(s).Keywords
 RealTime
 arbitrary dependences
 DNN
 Classifiers
 Optimal Ordering
Projects
 1 Finished

HighIntegrity, Complex, Large, Software and Electronic Systems
Bate, I. J., Kolovos, D. & McDermid, J. A.
1/07/19 → 30/06/23
Project: Research project (funded) › Research