What would drivers like to know during automated driving? Information needs at different levels of automation.

Automated driving changes the role of the driver from an active operator towards a supervisor during partially automated driving and passenger in the highly automated driving mode. To foster successful interaction between humans and automated systems, feedback on automation stages and behaviors is considered a key factor. The present study used a two-step procedure to investigate drivers’ information needs during partially and highly automated driving in comparison to manual driving for highway scenarios. The first step consisted in an expert focus group on expected information needs. Results showed that independent from specific scenarios, information should provide transparency, comprehensibility, and predictability of system actions. This includes the current system status, the remaining time to a change in the level of automation, the fallback level as well as reasons and a preview for ongoing and subsequent maneuvers. In the second step, results from the expert focus group were used to set up a driving simulator study. A sample of 20 participants performed three highway trips on the same route either in the manual, partially automated (hands-on, permanent monitoring, no secondary task) as well as highly automated condition (cloze test on a laptop as secondary task). Questionnaires and interviews about information needs were applied after each trip and glance behavior was analyzed. Information needs showed great variance between the drivers, which can mainly be explained by trust in automation. Partially automated driving was considered more exhausting than the other conditions due to the continuous supervision task. Information needs for the automated conditions were primarily related to the supervision of the system, whereas requested information during manual driving was centered on performing the current driving task. Glance data supported these patterns: during partially automated driving, drivers showed most and longer control glances at the mirrors and instrument cluster. Secondary task engagement during highly automated driving varied in dependence of trust in automation and the perceived complexity of the situation. However, less salient objects in a situation, such as traffic signs, were not perceived and no control glances were performed. It can be concluded that information needs change for partially and highly automated driving. Requested information is primarily focused on the status, transparency and comprehensibility of system action in contrast to driving-task related information during manual driving. These changes need to be considered in the human-machine-interface (HMI) design for automated driving. Corresponding author: Matthias Beggiato, TU Chemnitz, Cognitive and Engineering Psychology, Wilhelm-Raabe-Straße 43, 09120 Chemnitz, Germany. phone: +49(0)371-531-38654; fax: +49(0)371-531-838654; e-mail: matthias.beggiato@psychologie.tu-chemnitz.de. Acknowledgements: We would like to thank André Dettmann for the technical support of the driving simulator study.