Analyzing Cognitive Load Measurements of the Truck Drivers to Determine Transportation Routes and Improve Safety Driving: A Review Study

Sudiarno, Adithya; Ma’arij, Ahmad Murtaja Dzaky; Tama, Ishardita Pambudi; Larasati, Aisyah; Hardiningtyas, Dewi · 2023 · Crossref

DOI: 10.31603/ae.8301

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Summary

This review study addresses the critical issue of cognitive load among Land Logistic Drivers (LLDs) and its impact on supply chain (SC) performance and driving safety. High cognitive load, driven by uncontrollable factors such as heavy traffic and tight delivery schedules, increases stress and anger, thereby elevating the risk of vehicle crashes and disrupting SC continuity. The authors investigate the feasibility of using Electroencephalogram (EEG) technology to measure cognitive load in real-time, aiming to identify burdening transportation routes and implement corrective adjustments to improve safety and meet SC Key Performance Indicators (KPIs). The methodology involved a systematic literature review of 15 relevant studies published between 2016 and 2022, sourced from Scopus and ScienceDirect. The selection process filtered 1,733 initial records down to 15 final articles based on keywords including "Electroencephalogram" and "Cognitive." The review analyzed these studies across three primary parameters: focus detection (primarily fatigue and stress), simulation type (driving simulations versus other activities), and the specific brain lobes observed. The studies originated largely from densely populated countries like China and India, where high traffic levels correlate with increased cognitive demands on drivers. The findings confirm that EEG is a reliable, non-invasive tool for measuring cognitive load during driving. The analysis highlights that the central, parietal, and temporal lobes are critical areas for data gathering, as they manage motoric movements, decision-making, and sensory processing. Specifically, the temporal lobe showed better correlation with drowsiness than the frontal lobe, while the central lobe exhibited significant power changes during active driving. The study identifies alpha (α) and beta (β) brain wave bands as the most significant indicators of alertness and fatigue. A reduction in beta band power correlates with decreased alertness, while alpha band analysis, particularly alpha spindle measures, effectively distinguishes between alert and drowsy states. Machine learning classifiers, such as Support Vector Machines (SVM), were found to be effective in processing these EEG signals to detect fatigue and stress states with high accuracy. The significance of this research lies in its proposal to integrate EEG-based cognitive load monitoring into SC performance metrics. By detecting early signs of fatigue and high cognitive load, logistics providers can adjust routes or schedules to mitigate safety risks. The study concludes that incorporating human factor data, such as brain wave analysis, into SC KPIs can bridge the gap between operational efficiency and driver safety, ultimately reducing crash possibilities and ensuring more reliable product delivery.

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Topics

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• drowsiness detection algorithms
• workload measurement
• mental demand
• neuro workload indices
• truck driver fatigue

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software
• Theoretical Contribution: theory or model