Objective: Aim of the study was to evaluate the impact of different non-driving related tasks (NDR tasks) on takeover performance in highly automated driving.
Background: During highly automated driving, it is allowed to engage in NDR tasks temporarily. However, drivers must be able to take over control when reaching a system limit. There is evidence that the type of NDR task has an impact on takeover performance, but little is known about the specific task characteristics that account for performance decrements.
Method: Thirty participants drove in a simulator using a highly automated driving system. Each participant faced five critical takeover situations. Based on assumptions of Wickens's multiple resource theory, stimulus and response modalities of a prototypical NDR task were systematically manipulated. Additionally, in one experimental group, the task was locked out simultaneously with the takeover request.
Results: Task modalities had significant effects on several measures of takeover performance. A visual-manual texting task degraded performance the most, particularly when performed handheld. In contrast, takeover performance with an auditory-vocal task was comparable to a baseline without any task. Task lockout was associated with faster hands-on-wheel times but not altered brake response times.
Conclusion: Results showed that NDR task modalities are relevant factors for takeover performance. An NDR task lockout was highly accepted by the drivers and showed moderate benefits for the first takeover reaction.
Application: Knowledge about the impact of NDR task characteristics is an enabler for adaptive takeover concepts. In addition, it might help regulators to make decisions on allowed NDR tasks during automated driving.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1177/0018720818768199 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Triboelectric sensor gloves for real-time behavior identification and takeover time adjustment in conditionally automated vehicles.
Nat Commun
January 2025
Department of Traffic Management School, People's Public Security University of China, Beijing, 100038, China.
The takeover issue, especially the setting of the takeover time budget, is a critical factor restricting the implementation and development of conditionally automated vehicles. The general fixed takeover time budget has certain limitations, as it does not take into account the driver's non-driving behaviors. Here, we propose an intelligent takeover assistance system consisting of all-round sensing gloves, a non-driving behavior identification module, and a takeover time budget determination module.
View Article and Find Full Text PDFObjective: This study explores the effectiveness of conversational prompts on enhancing driver monitoring behavior and takeover performance in partially automated driving under two non-driving-related task (NDRT) scenarios with varying workloads.
Background: Driver disengagement in partially automated driving is a serious safety concern. Intermittent conversational prompts that require responses may be a solution.
Classifying crash causation patterns in 2-vehicle collisions between autonomous and conventional vehicles.
Traffic Inj Prev
January 2025
School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha, Hunan, China.
Objective: This study aims to investigate the causes of 2-vehicle collisions involving an autonomous vehicle (AV) and a conventional vehicle (CV). Prior research has primarily focused on the causes of crashes from the perspective of AVs, often neglecting the interactions with CVs.
Method: To address this limitation, the study proposes a classification framework for crash causation patterns in 2-vehicle collisions involving an AV and a CV, considering their interactions.
Using a visual attentional task and eye-tracking to investigate the effects of pre-takeover resource engagement on attention restoration in conditionally automated driving.
Traffic Inj Prev
January 2025
National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Centre, Beijing, China.
Objective: Attention forms the foundation for the formation of situation awareness. Low situation awareness can lead to driving performance decline, which can be dangerous in driving. The goal of this study is to investigate how different types of pre-takeover tasks, involving cognitive, visual and physical resources engagement, as well as individual attentional function, affect driver's attention restoration in conditionally automated driving.
View Article and Find Full Text PDFWhen automation fails - Investigating cognitive stability and flexibility in a multitasking scenario.
Appl Ergon
January 2025
Universität der Bundeswehr München, Werner-Heisenberg-Weg 39, 85579, Neubiberg, Germany.
Managing multiple tasks simultaneously often results in performance decrements due to limited cognitive resources. Task prioritization, requiring effective cognitive control, is a strategy to mitigate these effects and is influenced by the stability-flexibility dilemma. While previous studies have investigated the stability-flexibility dilemma in fully manual multitasking environments, this study explores how cognitive control modes interact with automation reliability.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!