Alexandra Neukum

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.

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.

Designing Emergency Steering and Evasion Assist to Enhance Safety in Use and Controllability

The development of evasion systems is a challenge when it comes to the design of the human machine interaction. To effectively assist the driver and prevent a collision, an emergency evasive manoeuvre has to be highly dynamic which may have an adverse effect on controllability in case of system failures. Therefore it is important to find a good trade-off between effectiveness, safety in use and controllability when designing such a system. There exist several different possibilities to perform an emergency evasive manoeuvre. At the moment, systems using directional torque overlays to perform an evasive manoeuvre are favoured although systems using differential braking, steer-by-wire or even combinations are also discussed.

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.

Development and Evaluation of Methods to Assess Controllability and Safety in Use Within the UR:BAN Project

The subproject UR:BAN KON “Controllability“ developed and evaluated methods which can be used to assess safety and controllability in early-stage development of new driver assistance systems. The empirical studies focused on emergency steering and evasion assistants that help the driver to avoid collisions in time-critical scenarios. Several factors like available manoeuvring space, drivers’ attention and characteristics of the system design could be identified which influence controllability and safety in use. Additionally, results from several research environments were compared and evaluated regarding their validity.

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.

Vielversprechender Einsatz der Fahrsimulation in der Psychiatrie

Die Fahrsimulation entwickelt sich seit Jahren vor allem für den Bereich der Fahrschule weiter. Diese Entwicklung lässt sich allerdings auch zur Evaluation der Fahreignung von Patienten mit psychischen Erkrankungen oder die Therapie von Angststörungen nach einem traumatischen Erlebnis nutzen.