Measures of Driver Behavior and Cognitive Workload in a Driving Simulator and in a Real Traffic Environment - Experiences from Two Experimental Studies in Sweden

Cici, Ruggero; Högman, Lennart; Patten, Christopher · 2001 · Crossref

DOI: 10.17077/drivingassessment.1024

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the growing cognitive demands placed on drivers by Intelligent Transportation Systems (ITS) and in-vehicle information systems (IVIS), such as navigation devices and onboard computers. Motivated by the increasing prevalence of these technologies, the authors present findings from two experimental studies conducted in Sweden to evaluate methods for assessing driver behavior and cognitive workload. The research aims to identify suitable standard methods for measuring the mental strain caused by external environmental factors and internal vehicle systems. The first study utilized a high-fidelity driving simulator to assess 21 subjects unfamiliar with the routes, who drove five predefined routes through a visualization of the Södra Länken tunnel system. Data collection included continuous electrodermal activity (EDA) recordings, driving performance metrics (speed, acceleration, braking, lateral position), and subjective workload ratings using a modified NASA-TLX scale. The second study was a field experiment involving 24 professional drivers in Linköping City. Participants drove an instrumented Volvo equipped with a GPS navigation system, comparing memory-based driving against navigation-assisted driving. Cognitive load was measured using a Peripheral Detection Task (PDT) device, which required subjects to respond to LED stimuli while driving. The navigation instructions were varied across three modes: verbal, visual, and combined visual/ verbal. Subjective workload was again assessed using the NASA-TLX questionnaire. In the simulator study, results indicated significant orientation and road choice problems. Approximately 50% of drivers missed critical road sign information and made critical errors when entering the tunnel system, while 30–50% made lane choice errors within the tunnel. Route 5 was identified as the most difficult based on NASA-TLX ratings. High correlations were found between physiological responses (EDA variability) and subjective ratings of overall difficulty (r = 0.90) and feeling of uncertainty (r = 0.85), validating EDA as a measure of mental strain. In the field study, navigation mode significantly affected PDT performance but had little effect on driving performance metrics. Reaction times were longer and hit rates lower during navigation-assisted driving compared to memory-based driving. Specifically, visual and combined visual/verbal instructions significantly impaired PDT performance, whereas verbal instructions did not. Subjective assessments generally favored memory-based driving, with significant differences found only in frustration levels during full navigation trials. The significance of these findings lies in the validation of specific assessment methodologies for traffic safety. The simulator study demonstrates the utility of combining EDA and subjective ratings to evaluate road design and cognitive workload in complex environments. The field study highlights the sensitivity of the PDT method to visual distraction caused by IVIS. The authors conclude that because driving requires continuous visual processing, visual distraction is a critical safety component. Consequently, they recommend that PDT performance should hold a predominant status in test batteries for evaluating the safety of IVIS and Advanced Driver Assistance Systems (ADAS).

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• mental demand
• workload measurement
• stress driving
• cognitive capacity variation
• road complexity
• simulator validity fidelity

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: physiological data
• Methodological Resource: validation psychometrics
• Theoretical Contribution: theory or model