Impact of training and in-vehicle task performance on manual control recovery in an automated car

William Payre, Julien Cestac, Nguyen Tong Dang, Fabrice Vienne, Patricia Delhomme

Research output: Contribution to journalArticle

29 Citations (Scopus)

Abstract

Automated driving (AD) introduces new skills needs for drivers to handle manual control recovery (MCR). In the scope of traffic safety, such skills should be investigated before this technology is available on public roads, especially in critical situations such as emergencies. The aim of the present study is to examine to what extend lack of training and in-vehicle task performance may impair MCR in a fully automated car. This study explores AD in a driving simulator, focusing on emergency MCR performance and trust in this system. 113 participants were involved. Training (simple vs. elaborated) and in-vehicle task performance (with vs. without) were manipulated. The MCR method (hands and feet used simultaneously or not) and its impact on response time and the number of pedals interactions were also examined. Training improved response time and decreased the number of interactions with the pedals. Moreover, the elaborated training group had fewer interactions with the pedals in the second and last system failure than in the first one. Performing an in-vehicle task slowed response time but did not significantly affect the number of interactions with the pedals. Response time was faster when hands and feet were used simultaneously in MCR. Elaborated training influenced trust in AD. Training drivers, using practice of the system and explaining its underlying logic, contributed to improve the human-automation performance. These results suggest the benefits of a training policy to enhance MCR performance and optimize trust in the system.
Original languageEnglish
Pages (from-to)216-227
Number of pages12
JournalTransportation Research Part F: Traffic Psychology and Behaviour
Volume46
Issue numberPart A
Early online date8 Mar 2017
DOIs
Publication statusPublished - 1 Apr 2017

Keywords

  • Fully automated driving
  • Manual control recovery
  • Non-driving related task
  • Training
  • Trust

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