Predicting takeover response to silent automated vehicle failures

Current and foreseeable automated vehicles are not able to respond appropriately in all circumstances and require human monitoring. An experimental examination of steering automation failure shows that response latency, variability and corrective manoeuvring systematically depend on failure severity and the cognitive load of the driver. The results are formalised into a probabilistic predictive model of response latencies that accounts for failure severity, cognitive load and variability within and between drivers. The model predicts high rates of unsafe outcomes in plausible automation failure scenarios. These findings underline that understanding variability in failure responses is crucial for understanding outcomes in automation failures.

• Record URL:
  • https://doi.org/10.1371/journal.pone.0242825
• Supplemental Notes:
  • © 2020 Callum Mole et al.
• Authors:
  • Mole, Callum
  • Pekkanen, Jami
  • Sheppard, William
  • Louw, Tyron
  • Romano, Richard
  • Merat, Natasha
  • Markkula, Gustav
  • Wilkie, Richard
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: e0242825
• Serial:
  • PLoS One
  • Volume: 15
  • Issue Number: 11
  • Publisher: Public Library of Science
  • EISSN: 1932-6203
  • Serial URL: https://journals.plos.org/plosone/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Auditory perception; Autonomous vehicle handover; Autonomous vehicles; Drivers; Mechanical failure; Predictive models
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01762950
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 29 2021 4:27PM