License to Supervise:Influence of Driving Automation on Driver Licensing

To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects drivers’ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation

Design considerations on user-interaction for semi-automated driving

The automotive industry has recently made first steps towards implementation of automated driving, by introducing lateral control as addition to longitudinal control (i.e. ACC). This automated control is allowed during specific situations within existing infrastructure (e.g. motorway cruising). During these circumstances, the role of the driver changes from actively operating the vehicle to supervising the system. Due to being placed remote from the control-loop, vigilance and overreliance, performing supervisory tasks is something humans are typically not very good at. For this reason, Human Factors experts have often raised concerns about the implementation of semi-automation. Nonetheless, we observed that recommendations on how to design appropriate interaction between driver and automation, are rather scarce. Therefore, we reviewed Human Factors’ literature and three existing interface examples to retrieve recommendations on desired driver-vehicle interaction. The most important design considerations were: (a) Avoid mode confusion by informing the driver appropriately about system state; (b) Support awareness of the system’s operational envelope, i.e. helping drivers understand the boundary limits within which the automation is able to operate; (c) Provide instructions with respect to the required role of the driver. From the examples reviewed, we concluded that interfaces representing a road situation graphically, while depicting elements relevant for system functioning (e.g. detection of road lines and/or target vehicle), provide effective solutions to support driver’s awareness of the system’s operational envelope. Nonetheless, we observed that there is at this moment no univocal understanding of what kind of interface mechanism works overall best. No consensus has been reached on appropriate ways to communicate mode-changes and to effectively instruct drivers when a role-change is required (i.e. retrieving control). Because confusion might easily occur when needing to interpret mode-information in time-critical situations and revealing the associated driver’s role, we strongly recommend more focus on drivers’ instructions in the development of future interfaces

Human-centered challenges and contributions for the implementation of automated driving

Automated driving is expected to increase safety and efficiency of road transport. With regard to the implementation of automated driving, we observed that those aspects which need to be further developed especially relate to human capabilities. Based on this observation and the understanding that automation will most likely be applied in terms of partially automated driving, we distinguished 2 major challenges for the implementation of partially automated driving: (1) Defining appropriate levels of automation, and; (2) Developing appropriate transitions between manual control and automation. The Assisted Driver Model has provided a framework for the first challenge, because this model recommends levels of automation dependent on traffic situations. To conclude, this research also provided brief directions on the second challenge, i.e. solutions how to accommodate drivers with partially automatio

Assessment of Driving Proficiency When Drivers Utilize Assistance Systems: The Case of Adaptive Cruise Control

Driver assistance systems (ADAS), and especially those containing driving automation, change the role of drivers to supervisors who need to safeguard the system’s operation. Despite the aim to increase safety, the new tasks (supervision and intervention) may jeopardize safety. Consequently, safety officers address the need for specific training on ADAS. However, these tasks are not assessed in driver licensing today. Therefore, we developed a framework to assess in-practice driving proficiency when drivers utilize ADAS. This study evaluated whether the proposed framework is able to identify meaningful differences in driving proficiency between driving with and without assistance. We applied the framework to perform a qualitative assessment of driving proficiency with 12 novice drivers in a field experiment, comparable to a license test. The assistance system concerned Adaptive Cruise Control (ACC). The test showed that driving with ACC has a negative influence on self-initiated manoeuvres (especially lane changes) and sometimes led to improved adaptations to manoeuvres initiated by other road users (like merging in traffic). These results are in line with previous research and demonstrate the framework’s successfulness to assess novice drivers’ proficiency to utilize ADAS in road-traffic. Therewith, the proposed framework provides important means for driving instructors and examiners to address the safe operation of ADAS