Sujay Yadawadkar, Brian Mayer, Sanket Lokegaonkar, Mohammad Raihanul Islam, Naren Ramakrishnan
Driver fatigue and distraction remain significant safety issues for drivers. Despite substantial developments in driver state detection technology, a reliable system has yet to emerge. Existing systems tend to suffer from reliance on a single metric such as PERCLOS estimated from single expensive in-vehicle cameras and/or a poorly designed and tuned algorithm resulting in lack of effectiveness (high false positive rates). It is not likely that any single, real-time measure of driver drowsiness will be obtainable all of the time from the entire driver population. Therefore, a multi-variable algorithm based on sensors/variables that can be reliably obtained in real time on modern vehicles is essential. In this work, several algorithms for multivariate time-series analysis are tested on the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset, including a statistical feature extraction method, deep learning-based long short-term memory, and video classification using convolutional neural networks. Given the amount of training and test data currently available, traditional statistical feature extraction methods outperformed the deep learning methods tested.
Sujay Yadawadkar, Brian Mayer , Sanket Lokegaonkar, Mohammad Raihanul Islam, Naren Ramakrishnan, Miao Song, Michael Mollenhauer: Identifying Distracted and Drowsy Drivers Using Naturalistic Driving Data. IEEE BigData 2018: 2019-2026
- Date of publication:
- January 24, 2019
- IEEE Big Data
- Page number(s):