A Systematic Review and Meta-Analysis of Takeover Performance During Conditionally Automated Driving. (November 2022) - Weaver, Bradley W.; DeLucia, Patricia R.
Objective: The aim of this paper was to synthesize the experimental research on factors that affect takeover performance during conditionally automated driving. Background: For conditionally automated driving, the automated driving system (ADS) can handle the entire dynamic driving task but only for limited domains. When the system reaches a limit, the driver is responsible for taking over vehicle control, which may be affected by how much time they are provided to take over, what they were doing prior to the takeover, or the type of information provided to them during the takeover. Method: Out of 8446 articles identified by a systematic literature search, 48 articles containing 51 experiments were included in the meta-analysis. Coded independent variables were time budget, non-driving related task engagement and resource demands, and information support during the takeover. Coded dependent variables were takeover timing and quality measures. Results: Engaging in non-driving related tasks results in degraded takeover performance, particularly if it has overlapping resource demands with the driving task. Weak evidence suggests takeover performance is impaired with shorter time budgets. Current implementations of information support did not affect takeover performance. Conclusion: Future research and implementation should focus on providing the driver more time to take over while automation is active and should further explore information support. Application: The results of the current paper indicate the need for the development and deployment of vehicle-to-everything (V2X) services and driver monitoring.