Frederik Naujoks

Advisory warnings based on cooperative perception

The Ko-PER (cooperative perception) research project aims at improvements of active traffic safety through cooperative perception systems. Within the project a prototype of a cooperative warning system was realized. This system provides early advisory warnings which are especially useful in critical situations with occluded conflict partners. The development process was accompanied by a series of driving simulator studies to determine both the potential to reduce traffic conflicts and important design characteristics of early advisory warning signals. The most important details of the prototype systems components inter-vehicle information-fusion and situation analysis are described and the achieved warning timings are compared to the results of the driving simulator studies.

Your Turn or My Turn?: Design of a Human-Machine Interface for Conditional Automation

Cooperative Conditionally Automated Driving (CAD) systems pose new challenges to the development of human-machine interfaces (HMI). The systems current status and intentions must be communicated unambiguously to ensure safe driver-system interaction and acceptance. This topic is becoming increasingly important as advanced automated driving functions are expected to carry out tactical and strategical driving maneuvers. Within the current study, an HMI for CAD was designed and evaluated by a sample of human factors experts (N=6). The participants passed seven interaction scenarios in which they either had to take over control or let the system execute a maneuver. Driving task responsibility was explicitly communicated by the HMI (e.g., by coloring and semantic text information). Quantitative and qualitative system usability was examined during and after the drive. Results pointed towards a very good overall usability and acceptance. Except for one case, interactions went according to the systems intention. Suggested design improvements were implemented.

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.

Understanding and Applying the Concept of “Driver Availability” in Automated Driving

Several levels of automated driving functions require the human as a fallback driver in case system performance limits are exceeded. Human factors research in this area is especially concerned with human performance in these take-over situations and the influence of the driver state. Based on work of the publicly funded project Ko-HAF the paper introduces a comprehensive model of the transition process from automated driving to manual driving and specifies relevant time stamps and time windows. The concept of Driver Availability is regarded as a quantitative measure that relates the estimated time required to safely take-over manual control to the available time budget. A conceptual framework outlines potential influencing factors on driver availability as well as ways to apply the measure in a real-time application.

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.

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

Graphical abstract

Testing Scenarios for Human Factors Research in Level 3 Automated Vehicles

The paper proposes a taxonomy for testing scenarios used in human factors research of Level 3 automated vehicles. Therefore, the literature was reviewed and testing scenarios were extracted. To categorize these scenarios, the four factors urgency, predictability, criticality and complexity of the driver response are introduced and defined. Furthermore, testing scenarios are suggested in dependence of the most important human factors research questions in Level 3 automated driving. The taxonomy thereby serves as a guidance and framework for the scenario selection and design of experiments in the context of Level 3 automated vehicle guidance.

Learning the “Language” of Road Users - How Shall a Self-driving Car Convey Its Intention to Cooperate to Other Human Drivers?

Communication between road users is ruled by road traffic regulations, but there are also implicit laws of communication. Especially lane changes in dense traffic scenarios require not only communicating one’s intention but also cooperating with other drivers. Self-driving vehicles will need to communicate with conventional vehicles on the road during the transition period to full automation. But how does a driver show his willingness to cooperate? A driving simulator study with N = 28 drivers in a dense traffic scenario on the highway was conducted. It was assumed that different lag vehicle reaction behavior on turn signals of the ego driver would influence the ego driver in his subjective evaluation of the situation. Three main effects, deceleration, the amount of velocity reduction and reaction time concerning perceived cooperation were found. The results of the study can be used to design cooperative driving strategies between self-driving and manually driven vehicles.