Literature DB >> 22235530

A contribution to situation awareness analysis: understanding how mismatched expectations affect road safety.

Christophe Mundutéguy1, Isabelle Ragot-Court.   

Abstract

OBJECTIVE: The aim of the study was to clarify how knowledge elaborated by specific experience may lead to erroneous expectations during interactions between drivers and riders.
BACKGROUND: Situation awareness is partly determined by prior knowledge. Unshared knowledge may cause difficulties in managing driving interactions, but there is still an important gap in the literature devoted to this field of research.
METHOD: There were 226 participants, distinguished according to their vehicle use (for drivers, type of vehicle driven and whether they were exclusive or dual drivers; for motorcycle and scooter riders, the type of powered two-wheeler [PTW] used and its engine size) and their driving experience. Focusing on the most vulnerable users, we studied prior representations to interactions using a series of closed questions on drivers' performance relating to different stages of the interaction process from the perspective both of drivers' self-reflection and of riders' expectations.
RESULTS: Although most drivers are self-confident, their abilities tend to be questioned by riders. Owners of medium or large motorbikes feel that drivers do not assess their approach speed accurately. Similarly, scooter riders doubt drivers' ability to assess the distance that separates them from PTWs. Riders who use medium or large motorbikes are more likely to question drivers' skills in relation to crossing situations. Scooter riders do so more often for overtaking situations.
CONCLUSION: The development of shared prior knowledge is essential to prevent accidents and incidents between drivers and riders. APPLICATION: To help improve effectiveness, we recommend specific ways of embedding each type of road user profile in training, prevention, and research.

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Year:  2011        PMID: 22235530     DOI: 10.1177/0018720811420841

Source DB:  PubMed          Journal:  Hum Factors        ISSN: 0018-7208            Impact factor:   2.888


  1 in total

1.  Digital Twin-Enabled Online Battlefield Learning with Random Finite Sets.

Authors:  Peng Wang; Mei Yang; Jiancheng Zhu; Yong Peng; Ge Li
Journal:  Comput Intell Neurosci       Date:  2021-05-13
  1 in total

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