Abstract

Driverless cars are a hot topic in today’s industry where several vehicle manufacturers try to create a reliable system for automated driving. The advantages of highly automated vehicles are many, safer roads and a lower environmental impact are some of the arguments for this technology. However, the notion of highly automated cars give rise to a large number of human factor issues regarding the safety and reliability of the automated system as well as concern about the driver’s role in the system."br/""br/"The purpose of this study was to explore the effects of systematic variations in traffic complexity and external time pressure on decision-making time in a simulated situation using a web-based testing platform. A secondary focus was to examine whether measures of cognitive performance and driver attitudes have an effect on decision-making time."br/""br/" The results show that systematic variations in both time pressure and traffic complexity have an effect on decision-making time. This indicates that drivers are able to adapt their decision-making to facilitate the requirements of a certain situation. The results also indicate that intelligence; speed of processing and driver attitudes has an effect on decision-making time.

Abstract

utomated truck platooning is getting an increasing interest for its potentially beneficial effects on fuel consumption, driver workload, traffic flow efficiency, and safety. Nevertheless, one major challenge lies in the safe and comfortable transitions of control from the automated system back to the human drivers, especially when they have been inattentive during highly automated driving. In this study, we investigated truck drivers’ take-over response times after a system initiated request to take back control in non-critical truck platooning scenarios. 22 professional truck drivers participated in the truck driving simulator experiment and everyone was instructed to drive under three task conditions during highly automated driving: Driver monitoring condition (drivers were instructed to monitor the surroundings), Driver not-monitoring condition (drivers were provided with a hand-held tablet and were asked to use this), and Eyes-closed condition (drivers were not allowed to open their eyes). The total take-over response time was divided into the perception response time and the movement response time by manual video annotation. Results showed significantly longer total take-over times with high variability in both Driver not-monitoring and Eyes-closed conditions compared to the Driver monitoring condition. Hand movement response time was found to be the dominant component of the total take-over time, being influenced by the motoric manoeuvres to resume physical readiness before taking over control (e.g., putting away the hand-held tablet, or adjusting seating position). These results suggest the importance of a personalized driver readiness predictor as an input parameter for a safe and comfortable transition of control. © 2019 Elsevier Ltd

Abstract

The highly automated driving research is one of the most remarkable developments in the automotive world. Still, ordinary people can also realize the milestones facing more and more built-in vehicle assistant services brought by the new car manufacturers. The navigation system is also part of the 'success story' because the maps are currently high-resolution 3D databases, and permanently better visualization supports the drivers. The creation of these map databases relies on cutting-edge field data capture techniques, like aerial surveys. The paper presents a technology based on aerial photographs' evaluation done by human operators. The captured photographs were transformed into distortion-free orthophotos; then, skilled humans evaluated the expected database elements: road and lane segments. The map creation procedure aimed to serve the content fully compatible with the standards of the industry. This evaluation procedure resulted in a road and a lane-level graph, clearly in harmony with the traffic rules. The CAD-drawings of each operator form an excellent starting point to start new artificial intelligence developments.