Abstract
Traffic accidents due to falling asleep at the wheel are a longstanding problem in many countries. This paper presents a novel solution based on fuzzy-logic decision fusion that prevents accidents by detecting driver fatigue or drowsiness early. The proposed method is based on analyzing and inferring about certain biological and behavioral measurements that enable detection of reduced alertness preceding driver-sleep onset. Because wakeful or sleep activity is reflected in several physiological conditions in human beings, such as cardiac, breathing, movement, and skin galvanic conductance, captured bioelectric signal features were extracted and fuzzy decision-fusion logic was tuned to make inferences about oncoming driver fatigue or drowsiness. The proposed method improves the performance by applying the fuzzy logic inference to fuse decisions from independent modules that infer about features measured on the sensed physiologic and/or behavioral information. The method reduces the complexity of the signal processing and of the pattern matching model. Tests have been executed on clinical and in field physiologic and behavioral data. A prototype based on a 32 bit microcontroller and a highly integrated analog front-end has been developed to support the in field tests.
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Malcangi, M. (2014). Fuzzy-Logic Decision Fusion for Nonintrusive Early Detection of Driver Fatigue or Drowsiness. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_6
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DOI: https://doi.org/10.1007/978-3-319-11071-4_6
Publisher Name: Springer, Cham
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