Objective: This study aimed at investigating the driver’s takeover performance when switching from working on different non–driving related tasks (NDRTs) while driving with a conditionally automated driving function (SAE L3), which was simulated by a Wizard of Oz vehicle, to manual vehicle control under naturalistic driving conditions. Background: Conditionally automated driving systems, which are currently close to market introduction, require the user to stay fallback ready. As users will be allowed to engage in more complex NDRTs during the automated drive than when driving manually, the time needed to regain full manual control could likely be increased. Method: Thirty-four users engaged in different everyday NDRTs while driving automatically with a Wizard of Oz vehicle. After approximately either 5 min or 15 min of automated driving, users were requested to take back vehicle control in noncritical situations. The test drive took place in everyday traffic on German freeways in the metropolitan area of Stuttgart. Results: Particularly tasks that required users to turn away from the central road scene or hold an object in their hands led to increased takeover times. Accordingly, increased variance in the driver’s lane position was found shortly after the switch to manual control. However, the drivers rated the takeover situations to be mostly “harmless.” Conclusion: Drivers managed to regain control over the vehicle safely, but they needed more time to prepare for the manual takeover when the NDRTs caused motoric workload. Application: The timings found in the study can be used to design comfortable and safe takeover concepts for automated vehicles.

Conditionally Automated Driving (CAD) functions need to be carefully examined regarding related driver attitudes such as trust and usability to increase their acceptance among future system users. By adding speech output to an existing audio-visual Human-Machine Interface (HMI), the level of trust in automation was suspected to be increased due to semantic information and the application of anthropomorphic features such as voice and gender. To test this assumption, N = 17 drivers completed two simulator drives, once with ('Speech + generic') and once without additional speech output (`Generic'). Having interacted with the automated system in different scenarios (i.e., letting the system execute a maneuver vs. taking over control from the vehicle), drivers completed comparative questionnaires on trust, anthropomorphism and usability. The applied questionnaire on trust in automation was structured after theoretical implications and derived from previous research on trust in automation. Results showed that the `Speech + generic' system was rated as superior for all three attitude measures compared to the `Generic' system. Furthermore, the present study describes a first approach on comprehensively examining trust in vehicle automation. We brought forth evidence that speech output is highly relevant in order to improve driver attitudes that affect acceptance of automated systems.

Automated driving systems (ADS) and a combination of these with advanced driver assistance systems (ADAS) will soon be available to a large consumer population. Apart from testing automated driving features and human–machine interfaces (HMI), the development and evaluation of training for interacting with driving automation has been largely neglected. The present work outlines the conceptual development of two possible approaches of user education which are the owner’s manual and an interactive tutorial. These approaches are investigated by comparing them to a baseline consisting of generic information about the system function. Using a between-subjects design, N = 24 participants complete one training prior to interacting with the ADS HMI in a driving simulator. Results show that both the owner’s manual and an interactive tutorial led to an increased understanding of driving automation systems as well as an increased interaction performance. This work contributes to method development for the evaluation of ADS by proposing two alternative approaches of user education and their implications for both application in realistic settings and HMI testing.