Are Electrodermal Activity-Based Indicators of Driver Cognitive Distraction Robust to Varying Traffic Conditions and Adaptive Cruise Control Use?

2025-08-14

AI Summary

This study investigates the reliability of electrodermal activity (EDA) as an indicator of driver cognitive distraction and its applicability under varying traffic complexity and adaptive cruise control (ACC) usage. Using a high-ecological-validity driving simulator, we concurrently recorded three EDA metrics—skin conductance level (SCL), SCR amplitude, and SCR frequency—and systematically analyzed their responsiveness to changes in cognitive load. Results show that SCR frequency robustly reflects cognitive distraction independent of traffic complexity, whereas SCL and SCR amplitude respond significantly not only to cognitive distraction and ACC activation but also to traffic environment variations—revealing differential sensitivity of EDA components to multiple sources of driving workload. These findings provide a theoretical foundation and evidence-based guidance for selecting appropriate EDA metrics in real-time, dynamic-cognitive-state monitoring during driving.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Application Domains: Mobility, Driving & Flight
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

In this simulator study, we investigate whether and how electrodermal activity (EDA) reflects driver cognitive distraction under varying traffic conditions and adaptive cruise control (ACC) use. Participants drove in six scenarios, combining two levels of cognitive distraction (presence/absence of a mental calculation task) and three levels of driving environment complexity (different traffic conditions). Throughout the experiment, they were free to activate or deactivate ACC (ACC use, two levels). We analyzed three EDA-based indicators of cognitive distraction: SCL (mean skin conductance level), SCR amplitude (mean amplitude of skin conductance responses), and SCR rate (rate of skin conductance responses). Results indicate that all three indicators were significantly influenced by cognitive distraction and ACC use, while environment complexity influenced SCL and SCR amplitude, but not SCR rate. These findings suggest that EDA-based indicators reflect variations in drivers' mental workload due not only to cognitive distraction, but also to driving environment and automation use.

Problem

• Assessing EDA indicators' robustness to traffic conditions
• Evaluating cognitive distraction detection under ACC use
• Validating electrodermal activity for driver mental workload

Innovation

• EDA indicators detect cognitive distraction variations
• Analyzed SCL, SCR amplitude and rate under different conditions
• Integrated traffic complexity and ACC use in workload assessment