Takeover performance evaluation using driving simulation: a systematic review and meta-analysis

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Sónia Soares (Author) FEUP; António Lobo (Author) FEUP; Sara Ferreira (Author) FEUP; Liliana Cunha (Author) FPCEUP; António Fidalgo Couto (Author) FEUP

Journal

Title: European Transport Research Review

Vol. 13 No. 1, Article 47

ISSN: 1867-0717

Publisher: Springer Nature

Indexing

ISI Web of Science

Scopus

Scientific classification

CORDIS: Technological sciences > Engineering > Civil engineering ; Social sciences > Psychological sciences > Psychology

FOS: Engineering and technology > Civil engineering ; Social sciences > Psychology

Associated Projects

AUTODRIVING - Autodriving - Modelação do comportamento do condutor em contexto de veículo autónomo com recurso ao simulador de condução

Other information

DOI: 10.1186/s12544-021-00505-2

Abstract (EN): Introduction: In a context of increasing automation of road transport, many researchers have been dedicated to analyse the risks and safety implications of resuming the manual control of a vehicle after a period of automated driving. This paper performs a systematic review about drivers’ performance during takeover manoeuvres in driving simulator, a tool that is widely used in the evaluation of automated systems to reproduce risky situations that would not be possible to test in real roads. Objectives: The main objectives are to provide a framework for the main strategies, experimental conditions and results obtained by takeover research using driving simulation, as well as to find whether different approaches may lead to different outcomes. Methodology: First, a literature search following the PRISMA statement guidelines and checklist resulted in 36 relevant papers, which were described in detail according to the type of scenarios and takeover events, drivers’ engagement in secondary tasks and the assessed takeover performance measures. Then, those papers were included in a meta-analysis combining PAM clustering and ANOVA techniques to find patterns among the experimental conditions and to determine if those patterns have influence on the observed takeover performance. Conclusions: Less complex experiments without secondary task engagement and conducted in low-fidelity simulators are associated with lower takeover times and crash rates. The takeover time increases with the time budget of the first alert, which reduces the pressure for a driver’s quick intervention. © 2021, The Author(s).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 18