Platooning, whereby automated vehicles travel closely together in a group, is attractive in terms of safety and efficiency. However, concerns exist about the psychological state of the platooning driver, who is exempted from direct control, yet remains responsible for monitoring the outside environment to detect potential threats. By means of a driving simulator experiment, we investigated the effects on recorded and self-reported measures of workload and stress for three task-instruction conditions: (1) No Task, in which participants had to monitor the road, (2) Voluntary Task, in which participants could do whatever they wanted, and (3) Detection Task, in which participants had to detect red cars. Twenty-two participants performed three 40-min runs in a constant-speed platoon, one condition per run in counterbalanced order. Contrary to some classic literature suggesting that humans are poor monitors, in the Detection Task condition participants attained a high mean detection rate (94.7%) and a low mean false alarm rate (0.8%). Results of the Dundee Stress State Questionnaire indicated that automated platooning was less distressing in the Voluntary Task than in the Detection Task and No Task conditions. In terms of heart rate variability, the Voluntary Task condition yielded a lower power in the low-frequency range relative to the high-frequency range (LF/HF ratio) than the Detection Task condition. Moreover, a strong time-on-task effect was found, whereby the mean heart rate dropped from the first to the third run. In conclusion, participants are able to remain attentive for a prolonged platooning drive, and the type of monitoring task has effects on the driver's psychological state.

Automated driving systems are increasingly prevalent on public roads, but there is currently little knowledge on the level of workload and stress of drivers operating an automated vehicle in a real environment. The present study aimed to measure driver workload and stress during partially automated driving in real traffic. We recorded heart rate, heart rate variability, respiratory rate, and subjective responses of nine test drivers in the Tesla Model S with Autopilot. The participants, who were experienced with driver assistance systems but naïve to the Tesla, drove a 32 min motorway route back and forth while following a lead car in regular traffic. In one of the two drives, participants performed a heads-up detection task of bridges they went underneath. Averaged across the two drives, the participants' mean self-reported overall workload score on the NASA Task Load Index was 19%. Moreover, the participants showed a reduction in heart rate and self-reported workload over time, suggesting that the participants became accustomed to the experiment and technology. The mean hit (i.e., pressing the button near a bridge) rate in the detection task was 88%. In conclusion, driving with the Tesla Autopilot on a motorway involved a low level of workload that decreased with time on task.

Previous research shows that drivers of automated vehicles are likely to engage in visually demanding tasks, causing impaired situation awareness. How mental task demands affect situation awareness is less clear. In a driving simulator experiment, 33 participants completed three 40-min runs in an automated platoon, each run with a different level of mental task demands. Results showed that high task demands (i.e., performing a 2-back task, a working memory task in which participants had to recall a letter, presented two letters ago) induced high self-reported mental demands (71% on the NASA Task Load Index), while participants reported low levels of self-reported task engagement (measured with the Dundee Stress State Questionnaire) in all three task conditions in comparison to the pre-task measurement. Participants' situation awareness, as measured using a thinkout-loud protocol, was affected by mental task demands, with participants being more involved with the mental task itself (i.e., to remember letters) and less likely to comment on situational features (e.g., car, looking, overtaking) when task demands increased. Furthermore, our results shed light on temporal effects, with heart rate decreasing and self-constructed mental models of automation growing in complexity, with run number. It is concluded that mental task demands reduce situation awareness, and that not only type-of-task, but also time-on-task, should be considered in Human Factors research of automated driving.

The future adoption of automated vehicles poses many challenges, with one of the more important being the preservation of control over vehicles that are no longer (fully) operated by drivers. There is consensus that vehicles should not perform actions that are unacceptable to humans. In this paper, we introduce the concept of Meaningful Human Control (MHC) as a function of a framework of the Automated Driving System (ADS). This framework is constructed through the core components that make up the ADS, primarily considered within the categories of the vehicle and driver. Identification of these components and the chain of control allow traceability of MHC to be performed, and aids vehicle manufacturers, software developers, other vehicle component designers, and vehicle-and driver licensing authorities to address many challenges related to the design and preservation of human control in automated vehicles. Operationalisation of MHC is discussed in the paper including a suggested approach that should aid understanding and the application of the concept. Four application examples are given and recommendations are made in regard to vehicle design, human machine interaction, transition of control, driver training, vehicle approval, and other topics. The framework and presented concept also allow researchers to identify areas to perform more explicit and relevant research and develop models that can be applied to perform projections of future impacts. Relevance to human factors/Relevance to ergonomics theory The preservation of control over vehicles that are no longer (fully) operated by drivers is of major importance and a highly relevant topic in human factors and ergonomics research. This paper introduces the concept of Meaningful Human Control (MHC) as a function of a framework of the Automated Driving System (ADS) to address some of these challenges. The framework is essential for the construction of the theory of MHC and the operationalisation in various field connected to human factors, HMI, automated vehicle software design. This aids vehicle manufacturers, software developers, other vehicle component

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