Predicting takeover response to silent automated vehicle failures

Autor: Tyron Louw, Richard Romano, Natasha Merat, Jami Pekkanen, Richard M. Wilkie, Gustav Markkula, William E. A. Sheppard, Callum Mole
Přispěvatelé: Cognitive Science, TRU (Traffic Research Unit)
Rok vydání: 2020
Předmět:
6162 Cognitive science
Male
Thin-Layer Chromatography
INFORMATION
IMPACT
Vision
Computer science
Social Sciences
Transportation
Automation
Wheels
SENSORIMOTOR CONTROL
Psychology
050107 human factors
Multidisciplinary
Chromatographic Techniques
05 social sciences
Accidents
Traffic
Transportation Infrastructure
Navigation
TIME
Risk analysis (engineering)
BRAKE RESPONSE
Medicine
Engineering and Technology
Female
Steering
Sensory Perception
TRANSITION
Research Article
Adult
Automobile Driving
Science
Cognitive Neuroscience
MODELS
Research and Analysis Methods
COGNITIVE LOAD
Civil Engineering
Industrial Engineering
0502 economics and business
Reaction Time
Humans
0501 psychology and cognitive sciences
Man-Machine Systems
Vision
Ocular
Behavior
050210 logistics & transportation
business.industry
Mechanical Engineering
Cognitive Psychology
Biology and Life Sciences
PERFORMANCE
Control Engineering
Roads
VISUAL CONTROL
Planar Chromatography
Cognitive Science
Perception
Chromatography
Thin Layer
business
Neuroscience
Zdroj: PLoS ONE
PLoS ONE, Vol 15, Iss 11, p e0242825 (2020)
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0242825
Popis: It remains a huge challenge to create Automated Vehicles (AVs) that are able to respond safely in all possible circumstances. Silent failures will occur when an AV fails to keep within the safety envelope and does not detect this failure or alert the human driver. To ensure AV safety, it is crucial to have a better understanding of human capabilities responding to silent failures. A highly controlled experiment was conducted to test drivers detecting and steering in response to a range of lane keeping failures of automation, using Time-to-Lane-Crossing (TLC) as the primary performance metric. Bayesian hierarchical modelling was used to construct predictive models that showed drivers responded more slowly (and less consistently) during less critical failures (for each 1 s increase in TLC at failure there was a 0.36 s increase in TLC at takeover). A manipulation that increased cognitive load impaired driver performance further (TLC at takeover decreased by 0.1 s and variability increased by 10\%). Steering response magnitudes scaled according to TLC at takeover, but increased cognitive load dampened steering. Whilst these results demonstrate increased risk caused by additional cognitive load, the magnitude of the effect was fairly small compared to the within and between participant variability. Modelling this variability allowed simulations of hypothetical silent failures to be run based on different road conditions (varied curvature, width and speed) and various delays in response times. This modelling suggests that a high proportion of silent failures would result in unsafe transitions of control from AV to a human driver.
Databáze: OpenAIRE
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