Are Drivers Allowed to Sleep? Sleep Inertia Effects Drivers’ Performance after Different Sleep Durations in Automated Driving

Higher levels of automated driving may offer the possibility to sleep in the driver’s seat in the car, and it is foreseeable that drivers will voluntarily or involuntarily fall asleep when they do not need to drive. Post-sleep performance impairments due to sleep inertia, a brief period of impaired cognitive performance after waking up, is a potential safety issue when drivers need to take over and drive manually. The present study assessed whether sleep inertia has an effect on driving and cognitive performance after different sleep durations. A driving simulator study with n = 13 participants was conducted. Driving and cognitive performance were analyzed after waking up from a 10–20 min sleep, a 30–60 min sleep, and after resting without sleep. The study’s results indicate that a short sleep duration does not reliably prevent sleep inertia. After the 10–20 min sleep, cognitive performance upon waking up was decreased, but the sleep inertia impairment faded within 15 min. Although the driving parameters showed no significant difference between the conditions, participants subjectively felt more tired after both sleep durations compared to resting. The small sample size of 13 participants, tested in a within-design, may have prevented medium and small effects from becoming significant. In our study, take-over was offered without time pressure, and take-over times ranged from 3.15 min to 4.09 min after the alarm bell, with a mean value of 3.56 min in both sleeping conditions. The results suggest that daytime naps without previous sleep deprivation result in mild and short-term impairments. Further research is recommended to understand the severity of impairments caused by different intensities of sleep inertia

How Visual Cues on Steering Wheel Improve Users’ Trust, Experience, and Acceptance in Automated Vehicles

With the introduction of ADAS systems and vehicle automation, an interface informing the driver of the automation state is required. This study evaluates the suitability of a visual interface comprising up to 64 LEDs on the steering wheel perimeter; it displays continuous visual feedback about the automation state—including notifications of an unscheduled hand-over due to sudden system failure. Three HMI (Human Machine Interface) designs were evaluated: two versions with visual cues on the steering wheel and one without (baseline). We implemented the designs in a driving simulator and compared the subjective responses of 38 participants to questionnaires measuring user experience, trust, and acceptance. The designs with visual cues improved the participants’ user experience, as well as their trust in, and acceptance of, automated vehicles. Moreover, both designs were well perceived by participants

FlexCAR- Die Forschungsplattform von morgen

Das FlexCAR besteht aus einer autonom gesteuerten Fahrzeugplattform für die Mobilität von morgen, welche Use-Case-gesteuert als Forschungsdemonstrator fungiert, um neue technologische Features unmittelbar aus dem Forschungsstadium nach dem Plug-and-Play-Prinzip gezielt zu implementieren. Damit kann eine frühzeitige Validierung im Hinblick auf ein künftiges Anwendungspotenzial ermöglicht werden. Offene Soft- und Hardwareschnittstellen sind hier berücksichtigt oder werden weiterentwickelt

Creating informed public acceptance by a user-centered human-machine interface for all automated transport modes : Paper presented at the Transport Research Arena (TRA), 27–30 April 2020, Helsinki, Finland

Increasing automation is ongoing in all areas of transport. This raises new challenges for the design and training of Human-Machine Interfaces (HMI) for different user groups. The EU-project Drive2theFuture investigates the needs and wants of transportation users, operators, passengers and passersby to gain their acceptance and to set the ground for a sustainable market introduction of automated transport. This paper describes how HMI concepts for the transport modes road, rail, maritime and aviation in Drive2theFuture are developed and comparatively assessed in order to be able to support an educated use of automated transport. By relying on a stepwise process, adaptable HMI strategies for different user clusters and levels of automation are defined. As a universal method, a comprehensive HMI development toolkit is developed, which can be adopted as training tool to create realistic expectations and enhance acceptance among users, operators and drivers in light of the deployment of automated vehicles

Creating informed public acceptance by a user-centered human-machine interface for all automated transport modes

Increasing automation is ongoing in all areas of transport. This raises new challenges for the design and training of Human-Machine Interfaces (HMI) for different user groups. The EU-project Drive2theFuture investigates the needs and wants of transportation users, operators, passengers and passersby to gain their acceptance and to set the ground for a sustainable market introduction of automated transport. This paper describes how HMI concepts for the transport modes road, rail, maritime and aviation in Drive2theFuture are developed and comparatively assessed in order to be able to support an educated use of automated transport. By relying on a stepwise process, adaptable HMI strategies for different user clusters and levels of automation are defined. As a universal method, a comprehensive HMI development toolkit is developed, which can be adopted as training tool to create realistic expectations and enhance acceptance among users, operators and drivers in light of the deployment of automated vehicles