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Predicting takeover response to silent automated vehicle failures
Callum Mole1, Jami Pekkanen1,2, William Sheppard1
1School of Psychology, University of Leeds, Leeds, United Kingdom.
Plos One
|November 30, 2020
Summary
Automated vehicles need human oversight as failures can be unpredictable. Driver cognitive load and failure severity significantly impact response times and safety outcomes in automated driving systems.
Area of Science:
- Human-computer interaction
- Automotive engineering
- Cognitive psychology
Background:
- Current automated vehicles require continuous human monitoring due to limitations in responding to all situations.
- Understanding driver responses to automation failures is critical for safety.
Purpose of the Study:
- To experimentally examine steering automation failure.
- To develop a predictive model for driver response latencies during automation failures.
- To assess the impact of failure severity and cognitive load on driver responses.
Main Methods:
- Experimental examination of steering automation failure scenarios.
- Analysis of response latency, variability, and corrective maneuvering.
- Development of a probabilistic predictive model incorporating failure severity and cognitive load.
Main Results:
- Driver response latency and corrective actions systematically depend on failure severity and cognitive load.
- Significant variability observed in driver responses, both within and between individuals.
- The predictive model indicates high rates of unsafe outcomes in plausible failure scenarios.
Conclusions:
- Variability in driver responses to automation failures is a key factor in determining safety outcomes.
- Current automated systems necessitate robust human monitoring protocols.
- Further research is needed to mitigate risks associated with automation failures in diverse cognitive states.
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