After Vehicle Automation Fails: Analysis of Driver Steering Behavior after a Sudden Deactivation of Control
Azadeh DinparastDjadid; John D. Lee; Chris Schwarz; Vindhya Venkatraman; Timothy L. Brown; John Gasper; Pujitha Gunaratne · 2018 · International Journal of Automotive Engineering
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Vehicles with SAE Level 2 or 3 automation rely on the driver to intervene and resume control when failures occur. In cases which the driver must steer upon regaining control, the initial conditions of the vehicle’s state variables can affect the success of the drivers' recovery. Hence, a model to determine the consequences of these initial states could help identify the requirements of shared control to guarantee a smooth recovery after an automation failure. Such a modeling tool should be simple, such as a two-point visual continuous control model of steering. Data to validate such a model we
Abstract by Azadeh DinparastDjadid; John D. Lee; Chris Schwarz; Vindhya Venkatraman; Timothy L. Brown; John Gasper; Pujitha Gunaratne, International Journal of Automotive Engineering (2018) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data
Negative / Null Result ReportMeteorological Causes of the Secular Variations in Observed Extreme Precipitation Events for the Conterminous United States
Negative / Null Result ReportThe Next Generation of Sepsis Clinical Trial Designs
Negative / Null Result ReportAnalysis of DNA Methylation in Young People: Limited Evidence for an Association Between Victimization Stress and Epigenetic Variation in Blood
Negative / Null Result ReportStudy preregistration: an early example and analysis.
Negative / Null Result ReportInsights Into LSTM Fully Convolutional Networks for Time Series Classification
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: DOAJ · DOI 10.20485/jsaeijae.9.4_208
