Katharina Wiedemann

A Human-Machine Interface for Cooperative Highly Automated Driving

Cooperative perception of the traffic environment will enable Highly Automated Driving (HAD) functions to provide timelier and more complex Take-Over Requests (TOR) than it is possible with vehicle-localized perception alone. Furthermore, cooperative perception will extend automated vehicles’ capability of performing tactic and strategic maneuvers independently of any driver intervention (e.g., avoiding of obstacles). In this paper, resulting challenges to the design of the Human-Machine Interface (HMI) are discussed and a prototypical HMI is presented. The prototype is evaluated by experts from the field of cognitive ergonomics in a small-scale simulator study.

A Review of Non-driving-related Tasks Used in Studies on Automated Driving

Conditionally automated driving (CAD) relieves the driver from monitoring current traffic conditions. This type of driving inherently enables the driver to execute different non-driving-related tasks (NDRTs). However, the driver still must be available as a backup option. With this in mind, the classification and evaluation of various NDRTs concerning their impact on driver performance in takeover scenarios represent an important contribution toward the creation of safe CAD functions. We reviewed various NDRTs that were used in studies on automated driving. The focus was on assigning aspects of these activities (e.g., ability to visually monitor traffic, necessity of sustained attention to NDRT, etc.) to various steps of the takeover process (e.g., noticing and interpreting takeover requests), which could be impaired by the execution of the respective NDRT. This, in turn, would increase the demands on the driver with respect to managing the takeover situation.

The Importance of Interruption Management for Usefulness and Acceptance of Automated Driving

To increase the safety in use of automated vehicles, Human Factors research has focused primarily on driver performance during take-over situations. However, surveys on public opinion on automated vehicles still report a lack of acceptance of the technology. In this review, we give an overview on how taking the changed role of the driver into account when designing Human-Machine Interfaces (HMI) of automated vehicles could increase the usefulness of the technology, which might in turn result in increased public acceptance. We propose that balancing the drivers need of being informed about the automated vehicles status, actions and intentions with the desire to engage in non-driving related tasks (NDRTs) is likely to play an important role in this process.

Improving Usefulness of Automated Driving by Lowering Primary Task Interference through HMI Design

During conditionally automated driving (CAD), driving time can be used for non-driving-related tasks (NDRTs). To increase safety and comfort of an automated ride, upcoming automated manoeuvres such as lane changes or speed adaptations may be communicated to the driver. However, as the driver’s primary task consists of performing NDRTs, they might prefer to be informed in a nondistracting way. In this paper, the potential of using speech output to improve human-automation interaction is explored. A sample of 17 participants completed different situations which involved communication between the automation and the driver in a motion-based driving simulator. The Human-Machine Interface (HMI) of the automated driving system consisted of a visual-auditory HMI with either generic auditory feedback (i.e., standard information tones) or additional speech output. The drivers were asked to perform a common NDRT during the drive. Compared to generic auditory output, communicating upcoming automated manoeuvres additionally by speech led to a decrease in self-reported visual workload and decreased monitoring of the visual HMI. However, interruptions of the NDRT were not affected by additional speech output. Participants clearly favoured the HMI with additional speech-based output, demonstrating the potential of speech to enhance usefulness and acceptance of automated vehicles.

Use Cases for Assessing, Testing, and Validating the Human Machine Interface of Automated Driving Systems

Reflecting the increasing demand for harmonization of human machine interfaces (HMI) of automated vehicles, different taxonomies of use cases for investigating automated driving systems (ADS) have been proposed. Existing taxonomies tend to serve specific purposes such as categorizing transitions between automation modes; however, they cannot be generalized to different systems or combinations of systems. In particular, there is no exhaustive set of use cases that allows entities to assess and validate the HMI of a given ADS that takes into account all possible system modes and transitions. The present paper describes a newly developed framework based on combinatorics of SAE (Society of Automotive Engineers) automation levels that incorporates a comprehensive taxonomy of use cases required for the assessment and validation of ADS HMIs. This forms a much-needed basis for test methods required to verify whether an HMI meets minimum requirements such as those outlined in the National Highway Traffic Safety Administration’s Federal Automated Vehicles policy.

Expert-based controllability assessment of control transitions from automated to manual driving

Graphical abstract

An Augmented Reality Display for Conditionally Automated Driving

This paper investigates whether an Augmented Reality Head-up Display (AR-HUD) supports usability and reduces visual demand during conditionally automated driving. In a driving simulator study, 24 drivers experienced several driving scenarios while driving with conditional automation. The drivers completed one drive with a fully developed HMI designed for automated driving (AD-HMI) that presented visual information in the cluster display and included auditory and tactile output. In another drive, the same drivers were additionally supported by dynamic and static visual feedback via an AR-HUD concept. The latter was preferred by more than 80% of the sample due to the higher information content and the possibility to leave the eyes on the road. Drivers rated the AR concept to be better understandable and more useful. Eye-tracking revealed lower percentage of gazes to the instrument cluster during AR-HUD drives.

Effect of different alcohol levels on take-over performance in conditionally automated driving

Automated driving systems are getting pushed into the consumer market, with varying degrees of automation. Most often the drivers task will consist of being available as a fall-back level when the automation reaches its limits. These so-called take-over situations have attracted a great body of research, focusing on various human factors aspects (e.g., sleepiness) that could undermine the safety of control transitions between automated and manual driving. However, a major source of accidents in manual driving, alcohol consumption, has been a non-issue so far, although a false understanding of the drivers responsibility (i.e., being available as a fallback level) might promote driving under its influence. In this experiment, N = 36 drivers were exposed to different levels of blood alcohol concentrations (BACs: placebo vs. 0.05% vs. 0.08%) in a high fidelity driving simulator, and the effect on take-over time and quality was assessed. The results point out that a 0.08% BAC increases the time needed to re-engage in the driving task and impairs several aspects of longitudinal and lateral vehicle control, whereas 0.05% BAC did only go along with descriptive impairments in fewer parameters.