Driver Cognitive Distraction Classification While Using Eco-driving Applications

Abstract

Onboard eco-driving systems that provide speed guidance and encourage fuel and emission reduction have become increasingly popular. However, such systems may cause driver distraction, highlighting the need for cognitive attention monitoring capabilities. This study investigates how to accurately detect cognitive distraction when drivers interact with an eco-driving system in both acceleration and deceleration scenarios. Using the random forest algorithm, driving and glance features were extracted to classify drivers’ cognitive attentional states. Results showed that the glance feature was the most effective factor for detecting cognitive distraction, achieving 90.8% accuracy in the acceleration scenario. This study contributes to the design of effective eco-driving systems that can accurately monitor drivers' cognitive attention and enhance safety.1.BackgroundWith the advancement of connected-vehicle technologies, the onboard eco-driving systems may provide drivers with real-time information about their driving behavior and traffic conditions, encouraging them to optimize their driving speed and thus reduce fuel consumption and greenhouse gas emissions. A growing body of research has examined the impact of these systems and discovered that implementing these systems could result in an average of 6.6% reduction in fuel consumption (ranging from 1% to 30%), making eco-driving an appealing option to protect the environment. In our study, we concentrate on examining drivers' cognitive distractions while interacting with eco-driving systems and identifying effective methods to detect and mitigate such distractions, with a specific focus on scenarios involving both acceleration and deceleration.2.Method Twenty-one drivers (15 males, 6 females), between 18 and 48 years of age (Mean = 26.11, SD =9.11), were recruited for a driving simulator experiment. All participants wore masks throughout the experiments for COVID-19 protection. All of them had normal or corrected-to-normal vision (using contact lenses). One female participant did not finish the experiment due to motion sickness. Her data were excluded from further analysis. Therefore, 20 valid users’ data remained for the rest of the analysis. They had an average of 8.5 years of driving experience (SD = 9.7). Pupil-Labs eye tracker was used to record participant’s eye movements during the experiment. The device captures video and audio streams, detects pupils, tracks gaze, tracks surface tracker, and records the data in an open format.3.Results and FindingsWe utilized the random forest algorithm, a machine learning approach, to analyze the comprehensive dataset comprising both driving and gaze metrics. The intent was to classify the cognitive attentional states of drivers. The analysis yielded a notable finding. The glance behavior emerged as a highly effective indicator of cognitive distraction, particularly in acceleration scenarios, where it demonstrated a remarkable detection accuracy of 90.8%. This key discovery underscores the criticality of visual attention as a metric in driver behavior and cognitive engagement, especially in the context of interactions with eco-driving systems.

Keywords: Cognitive distraction, Eco-driving, Connected vehicle, Classification, Machine learning

DOI: 10.54941/ahfe1005226