Evaluating Effects of Cognitive Load, Takeover Request Lead Time, and Traffic Density on Drivers’ Takeover Performance in Conditionally Automated Driving
Autor: | Jinyong Kim, X. Jessie Yang, Dawn M. Tilbury, Lionel P. Robert, Elizabeth Pulver, Feng Zhou, Na Du, Anuj K. Pradhan |
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Rok vydání: | 2020 |
Předmět: |
050210 logistics & transportation
Computer science media_common.quotation_subject 05 social sciences Control (management) Applied psychology Driving automation 0502 economics and business Traffic conditions Driving simulation 0501 psychology and cognitive sciences Quality (business) Automotive user interfaces 050107 human factors Lead time Cognitive load media_common |
Zdroj: | AutomotiveUI |
Popis: | In conditionally automated driving, drivers engaged in non-driving related tasks (NDRTs) have difficulty taking over control of the vehicle when requested. This study aimed to examine the relationships between takeover performance and drivers’ cognitive load, takeover request (TOR) lead time, and traffic density. We conducted a driving simulation experiment with 80 participants, where they experienced 8 takeover events. For each takeover event, drivers’ subjective ratings of takeover readiness, objective measures of takeover timing and quality, and NDRT performance were collected. Results showed that drivers had lower takeover readiness and worse performance when they were in high cognitive load, short TOR lead time, and heavy oncoming traffic density conditions. Interestingly, if drivers had low cognitive load, they paid more attention to driving environments and responded more quickly to takeover requests in high oncoming traffic conditions. The results have implications for the design of in-vehicle alert systems to help improve takeover performance. |
Databáze: | OpenAIRE |
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