Short answer

Designers of automated driving systems must prioritize safety by developing systems that actively manage driver attention and ensure reliable takeover procedures, rather than assuming drivers will remain vigilant.

Field
Human Factors
Source
Accident Analysis & Prevention (2025)
Method
Systematic Literature Review
Evidence
Strong effect

Increased driver drowsiness, often exacerbated by prolonged use of automated driving systems and engagement in non-driving tasks, leads to slower reaction times and poorer performance when a human driver must retake control. This human factors research insight is drawn from a 2025 study published in Accident Analysis & Prevention. Using Systematic literature review, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Designers of automated driving systems must prioritize safety by developing systems that actively manage driver attention and ensure reliable takeover procedures, rather than assuming drivers will remain vigilant.

Study
Human FactorsNew This WeekStrong effect

Driver Drowsiness Significantly Impairs Takeover Performance in Automated Vehicles

Increased driver drowsiness, often exacerbated by prolonged use of automated driving systems and engagement in non-driving tasks, leads to slower reaction times and poorer performance when a human driver must retake control.

Accident Analysis & Prevention · 2025

01

Key Findings

  • 01Driver drowsiness increases with the duration of automated driving and higher levels of automation.
  • 02Engaging in non-driving related tasks (NDRTs) can reduce subjective and physiological signs of drowsiness but negatively impacts takeover performance.
  • 03Drowsiness leads to increased reaction times and reduced effectiveness during manual takeovers.
02

Application

Design takeaway

Designers of automated driving systems must prioritize safety by developing systems that actively manage driver attention and ensure reliable takeover procedures, rather than assuming drivers will remain vigilant.

How to apply

When designing or evaluating automated driving features, incorporate driver monitoring systems that detect drowsiness and implement alerts or interventions to ensure safe takeover. Consider how non-driving tasks might be managed or limited during critical phases of automation.

Project actions

  • 01When researching user interfaces for automated systems, consider how to keep drivers alert or how to ensure safe transitions.
  • 02Investigate physiological or behavioural indicators of drowsiness that could be integrated into a design.
03

Method & Evidence

AimTo systematically review and synthesize current research on the impact of driver drowsiness on takeover performance in automated driving systems, identifying key influencing factors and their effects.
MethodSystematic Literature Review
ProcedureA systematic search of Web of Science, PubMed, and Scopus databases was conducted for studies published between March 2021 and October 2024, focusing on automated driving (Level 2+) and measurements of driver drowsiness and takeover performance. Existing relevant studies from a 2022 review were also incorporated, resulting in a total of 29 articles for analysis.
ContextAutomated Driving Systems (ADS) in simulated or real-world vehicle environments.

Variables

IV["Duration of automated driving","Level of automation","Engagement in non-driving related tasks (NDRTs)"]
DV["Driver drowsiness indicators (e.g., Karolinska Sleepiness Scale, blink frequency, heart rate)","Takeover performance indicators (e.g., reaction time, braking time, steering response)"]
CV["Type of vehicle automation","Driving environment (simulated vs. real-world)","Participant characteristics (e.g., age, driving experience)"]
04

Strengths & Limitations

Strengths

  • +Comprehensive literature search adhering to PRISMA guidelines.
  • +Inclusion of both recent and prior relevant studies for a thorough overview.

Limitations

It can be difficult to accurately measure drowsiness in a controlled setting, and ethical considerations limit the extent to which participants can be made genuinely drowsy.

Reliability & validity

The reliability of findings is strengthened by the systematic review methodology and the inclusion of multiple studies. Validity is supported by the focus on controlled experimental designs. However, the reliance on simulated environments may limit external validity.

Think critically

Given that non-driving tasks reduce drowsiness but worsen takeover performance, how can automated systems be designed to balance driver engagement with the need for situational awareness during critical takeover events?

05

Design Principles

"Automated systems should be designed to proactively manage human-system interaction, especially during critical transitions of control, accounting for human physiological and psychological states."

As vehicle automation becomes more prevalent, understanding the human factors involved in the transition of control is critical for safety. This research highlights a significant risk: drivers may become less attentive and slower to react when automation fails or requires human intervention, directly impacting the safety of automated driving systems.

06

What This Means for Your Design

When cars drive themselves for a long time, drivers get sleepy. If the car then needs the driver to take over, the sleepy driver will be much slower to react, which is dangerous.

How to use in your project

  • 1.Reference this study when discussing the human factors challenges of implementing automation and the need for careful consideration of driver states in your design process.
07

Add to My Project

08

Quick Cite

Paragraph starter

The integration of automated driving systems necessitates a thorough understanding of human factors, particularly driver drowsiness. Research indicates that prolonged use of automation, especially at higher levels, significantly increases driver drowsiness, which in turn impairs takeover performance. Studies show that engaging in non-driving related tasks, while potentially alleviating subjective feelings of tiredness, leads to detrimental effects on reaction times and overall takeover effectiveness. Therefore, any design incorporating automation must include robust strategies for monitoring driver alertness and ensuring safe, timely transitions of control.

09

Source

Accident Analysis & Prevention

Exploring the effect of driver drowsiness on takeover performance during automated driving: An updated literature review

journal · 2025

View source

Questions About This Research

What does the research say about driver drowsiness significantly impairs takeover performance in automated vehicles?
Designers of automated driving systems must prioritize safety by developing systems that actively manage driver attention and ensure reliable takeover procedures, rather than assuming drivers will remain vigilant. Evidence: Accident Analysis & Prevention (2025).
Why does "Driver Drowsiness Significantly Impairs Takeover Performance in Automated Vehicles" matter for design?
As vehicle automation becomes more prevalent, understanding the human factors involved in the transition of control is critical for safety. This research highlights a significant risk: drivers may become less attentive and slower to react when automation fails or requires human intervention, directly impacting the safety of automated driving systems.
How can designers apply this research?
Designers of automated driving systems must prioritize safety by developing systems that actively manage driver attention and ensure reliable takeover procedures, rather than assuming drivers will remain vigilant.
What were the main findings?
Driver drowsiness increases with the duration of automated driving and higher levels of automation.. Engaging in non-driving related tasks (NDRTs) can reduce subjective and physiological signs of drowsiness but negatively impacts takeover performance.. Drowsiness leads to increased reaction times and reduced effectiveness during manual takeovers.
What research method was used?
Systematic Literature Review.
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2025 journal from Accident Analysis & Prevention.
What should I do differently in my next project?
When designing or evaluating automated driving features, incorporate driver monitoring systems that detect drowsiness and implement alerts or interventions to ensure safe takeover. Consider how non-driving tasks might be managed or limited during critical phases of automation.
What are the limitations?
The review primarily relies on simulated environments, and findings may not perfectly translate to real-world driving conditions. The definition and measurement of drowsiness and takeover performance can vary across studies.