Driver’s workload detection for advanced driving assistance system

Abstract

Advanced Driving Assistant System (ADAS) was developed to reduce hazard on road, as drivers tend to get distracted from non-driving tasks. Researchers widely acknowledge that machine learning should be applied in ADAS, so that system can recognize driver’s state and adapt accordingly. Applying machine learning to physiological signals to learn psychological model is a common research topic. Yet, little work has considered the challenges in implementation, which is different from other machine learning domains. Usual approach is to collect many signals and go through tedious signal processing to output feature vectors. Machine learning plays its part only after features are available, which is costly and unlikely to be feasible in real world situation. We propose new machine learning based methods that only require simple single signal combined with manifold learning algorithms. Our methods are robust in that they require only one signal but don’t compromise the performance. The first contribution of the work is that we collected data from partly automated vehicle’s simulation, where machine learning has seldom been applied. The second contribution is that we proposed new feature extraction methods which only exploit ECG signal. Our methods do not require domain specific knowledge or tedious signal processing procedure. As long as intervals between R peaks are available (which can be measured easily with cheap commercial equipment), our manifold learning based feature extractor will provide reliable features. As this implies no pre-processing, it is more beneficial for implementation.