The Driver's Response to Decreasing Vehicle Separations during Transitions into the Automated Lane

Bloomfield, John R.; Christensen, J. Marty; Carroll, S. A.; Watson, G. S. · 1996 · ROSA P / United States. Federal Highway Administration. Office of Safety and Traffic Operations

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Summary

This study investigates driver comfort levels during transitions in a generic Automated Highway System (AHS), specifically focusing on the scenario where a vehicle enters the automated lane ahead of the driver’s vehicle, thereby reducing the separation distance. The research was conducted to address human factors issues related to AHS design and operation, particularly the acceptability of decreasing inter-vehicle gaps when a new lead vehicle joins a string of automated vehicles. The study aims to determine how driver comfort varies under normal automated conditions and during these entry transitions, examining the influence of driver demographics, system parameters, and entry timing. The experiment utilized the Iowa Driving Simulator, featuring a moving base platform and high-fidelity visual projection. Sixty volunteer drivers participated, comprising equal numbers of males and females, with half aged 25–34 years and half aged 65 or older. Participants drove a simulator vehicle that initially led a string of automated vehicles in the left lane of a three-lane expressway. The experimental design involved two phases: first, monitoring comfort during normal AHS operation; second, introducing a second vehicle into the automated lane ahead of the simulator vehicle. This entering vehicle accelerated from 88.6 km/h (55 mi/h) to the design velocity, causing the gap to decrease until it became the new lead vehicle. Drivers indicated their comfort levels continuously using a lever, with forward movement indicating comfort and backward movement indicating discomfort. Variables manipulated included design velocity, inter-string gap, and the timing of the entering vehicle’s arrival. Results indicated that drivers generally reported positive comfort levels (89.9% of trials) when leading a string under normal operating conditions. However, comfort levels significantly decreased when a second vehicle entered the lane ahead. In 86.2% of these trials, comfort declined, dropping to negative levels in 71.6% of cases. Demographic analysis revealed that male drivers reported higher comfort levels than females, and younger drivers were more comfortable than older drivers. Specifically, mean comfort levels during the entry phase were -0.37 for younger males, -0.45 for younger females, -0.54 for older males, and -0.71 for older females. The study also found indications that the sharp decrease in comfort was triggered by time-to-collision estimates rather than separation distance alone. The findings suggest that while drivers are generally comfortable with automated lane keeping, the transition of leadership within a vehicle string causes significant discomfort, particularly among older and female drivers. This highlights critical human factors challenges for AHS implementation, indicating that system designs must account for varying comfort thresholds across demographic groups. The results imply that strategies to mitigate discomfort during vehicle entry transitions, such as adjusting acceleration profiles or providing better informational cues, may be necessary to ensure user acceptance and safety in automated highway systems.

Key finding

Driver comfort levels decreased in 86.2 percent of trials when a second vehicle entered the automated lane, dropping to negative comfort levels in 71.6 percent of those trials.

Methodology

simulator

Sample size: 60

Provenance

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

Topics

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• traffic density
• following distance
• passenger motion sickness comfort
• speed choice
• situational awareness
• automation surprise

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: behavioral performance data
• Methodological Resource: tool software
• Theoretical Contribution: computational model