Driver Estimation in Non-Linear Autoregressive Models

Tour, Tom Duprela; Grenier, Yves; Gramfort, Alexandre

doi:10.1109/icassp.2018.8462268

Cited by 1 publication

(1 citation statement)

References 17 publications

(22 reference statements)

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“…In this paper, we address the learning task of estimating driver drowsiness [1,2], a cause of numerous severe accidents. Such driver monitoring scenarios are typical embodiments of signal processing leveraging machine learning technologies [3,4,5]. We take a light-weight and non-intrusive approach that does not require any image processing or invasive sensors and instead only requires acceleration sensors that capture anomalous acceleration, braking, and steering data reflecting the drowsiness of the driver.…”

Section: Introductionmentioning

confidence: 99%

Learning to Estimate Driver Drowsiness from Car Acceleration Sensors Using Weakly Labeled Data

Katsuki

Zhao

Yoshizumi

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper addresses the learning task of estimating driver drowsiness from the signals of car acceleration sensors. Since even drivers themselves cannot perceive their own drowsiness in a timely manner unless they use burdensome invasive sensors, obtaining labeled training data for each timestamp is not a realistic goal. To deal with this difficulty, we formulate the task as a weakly supervised learning. We only need to add labels for each complete trip, not for every timestamp independently. By assuming that some aspects of driver drowsiness increase over time due to tiredness, we formulate an algorithm that can learn from such weakly labeled data. We derive a scalable stochastic optimization method as a way of implementing the algorithm. Numerical experiments on real driving datasets demonstrate the advantages of our algorithm against baseline methods.

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Section: Introductionmentioning

confidence: 99%