How different mental workload levels affect the take-over control after automated driving

Mercedes Bueno · Ebru Doğan · F. Hadj Selem · Éric Monacelli · S. Boverie · A. Guillaume

76 citations0/0 files storedIEEE International Conference on Intelligent Transportation Systems (ITSC)

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Human-Automation Interaction and Safety Vehicle Dynamics and Control Systems Sleep and Work-Related Fatigue

Abstract

Automated driving represents a new way of driving and, therefore, significant changes in driver behaviour are expected. In this study, we analysed the impact of different levels of mental workload associated with a non-driving task in response to a take-over request (TOR). Thirty participants drove in a driving simulator through three experimental conditions: once in manual driving and twice following an automated driving during which a non-driving task was performed. The impact of the mental workload was evaluated by manipulating the mental workload induced by the non-driving task (low versus high mental workload). The results showed a negative effect of automated driving on take-over performance regardless of the level of mental workload. These results point out that engagement in non-driving task leads to reduced situation awareness. Interpretations of results and research prospects are discussed.

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1988Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research 1997Humans and Automation: Use, Misuse, Disuse, Abuse 1962The associative basis of the creative process.2014Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence 2014Transition to manual: Driver behaviour when resuming control from a highly automated vehicle 2015What determines the take-over time? An integrated model approach of driver take-over after automated driving 2014How Traffic Situations and Non-Driving Related Tasks Affect the Take-Over Quality in Highly Automated Driving 2016Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations

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2022Predicting Driver Takeover Time in Conditionally Automated Driving 2021How drowsiness and distraction can interfere with take-over performance: A systematic and meta-analysis review 2021How the Initial Level of Trust in Automated Driving Impacts Drivers’ Behaviour and Early Trust Construction 2023Using pupillometry and gaze-based metrics for understanding drivers’ mental workload during automated driving

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