A Study on an Approach to Driver Distraction Detection Based on Predicting the Distribution of Categorical Response Variable with Denoising Diffusion Probabilistic Models

Abstract

Abstract: As per a World Health Organization (WHO) report, inattentive driving is the 8th greatest reason for traffic fatalities. A lot of work has been done to combat this problem including research into advanced driver-assistance systems (ADAS). This work includes the employment of distracted driver detection systems and algorithms to serve as a warning system. Recent work has shown how deep learning techniques can offer higher accuracy in classifying distracted driver. Earlier work has also shown how machine learning and later deep learning can be used for this task. At its core the task can be broken down to learning features in an image to predict the state .ML and DL algorithms were employed in this research to classify diverted driving using images achieving state of the art results in many occurrences. However, as the research has progressed and models have gotten bigger. How these predictions are made has not been explored. The black box problem is now an area of research and quantifying the uncertainty of the module may help shed light on my predictions were made. We suggest using a new approach to distracted driver detection using denoising diffusion probabilistic models for this task. Performance is enhanced by reconfiguring the CARD model for the task. Our motivation is not to achieve state-of- the-art performance in terms of mean accuracy on the State farm distracted driver dataset, which is strongly related to network architecture design. Our goal is to perform classification via a generative model emphasize the model’s capability to improve the performance of a base classifier with deterministic outputs in terms of accuracy.