A peripheral warning system for vehicles: Is an LED-Array a suitable display for driving assistance systems?

In dieser Arbeit wurde eine neue Warnungsanzeige für Kraftfahrzeuge untersucht. Im Gegensatz zu klassischen Fahrerassistenzsystemen zur Gefahrenwarnung war diese nicht auf die Wahrnehmung aus dem Blickfokus, sondern über die visuelle Peripherie ausgelegt. Sie bestand aus einer eindimensionalen Leiste aus mehrfarbigen LEDs, die unter der Frontscheibe platziert wurde. Aufgrund der vereinfachten Darstellung wurden Botschaften nicht in Form von Symbolen und Text, sondern über Lichtbalken vermittelt. Diese Balken konnten in Größe, Farbe, Position, Helligkeit und Animation variieren. In sieben Studien wurden die Wirkung und das Potential der Anzeige, die Verkehrssicherheit zu erhöhen, hinsichtlich verschiedener Aspekte untersucht und bewertet, ob sich das Konzept als Fahrerassistenzsystem zum Einsatz im Straßenverkehr eignet. Vor dieser Arbeit gab es nur wenige Untersuchungen zur Wirkung einer Unscharfen Warnung im Kraftfahrzeug. Insgesamt fehlte eine ganzheitliche Betrachtung des Warnkonzeptes, welche die beteiligten menschlichen Verarbeitungsprozesse darstellt und systematisch die Wirkung einzelner Gestaltungsaspekte erforscht. Die vorliegende Arbeit setzt hier an, indem sie das Konzept zunächst auf einer sehr grundlegenden Ebene untersucht und sich dann Stück für Stück konkreten Anwendungsszenarien nähert. Auf diese Weise konnte mit der vorliegenden Arbeit ein umfassendes Profil der Wirkung einer Unscharfen Warnung erstellt werden. Es konnte gezeigt werden, dass die grundsätzlichen Voraussetzungen für den Einsatz einer Unscharfen Warnung im Automobil erfüllt werden. Die erarbeiteten Ergebnisse deuten darauf hin, dass die symbolfreie Darstellung, im Vergleich zu einer klassischen symbolhaften Warnung, bei gleich guter Wirkung die Fahrer weniger von der Straße ablenkt. Das neue Warnkonzept der Unscharfen Warnung besitzt somit das Potential, Fahrer im Vergleich zu klassischen Anzeigen zu entlasten und ihre Reaktionen zu verbessern.The thesis investigates a new driving assistance system for vehicles, more specifically, a visual warning system. This display differed from other typical systems in that it was designed to be perceived by the peripheral vision instead of a direct gaze towards it. It consisted of a one-dimensional array of multi-coloured LEDs which were placed under the windshield. Because of the simplistic design, the messages to the driver did not contain any symbols or text. They were solely created from bars of light. These light bars could differ in size, colour, position, brightness and animation. The potential to increase traffic safety and the impact of the display on the driver were investigated in seven studies. The generated knowledge was then used to evaluate whether the concept was fit for implementation in real traffic. In each of the studies, different aspects relevant for the task were investigated. Up until now there was only little research about the effects of LED arrays as displays for warning systems. Previous research generally lacked a holistic appraisal which describes the relevant aspects of human perception and the specific effects of different configurations of this kind of display. This thesis closes this gap by investigating the concept on a very basic level and then iterates towards more practical use cases. In this way, the thesis manages to provide a comprehensive profile of the impact of this LED array. It could be demonstrated that all necessary basic preconditions for the implementation of such a display in the car are met. The results indicate that a display without symbols and text can achieve the same advantages as a classic system with symbols, while distracting the driver less from the street. The new display system in form of an LED array thus has the potential to be less demanding of the driver than classic displays while still enhancing his reactions

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today's society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application's requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle's safety performance are a key requirement for demonstrating its trustworthiness

Validating criticality for cyclist-vehicle interactions

Ten interactions between cyclists and vehicles at a high-traffic intersection in Braunschweig, Germany were investigated. Trajectories, speed and distances in between road users were measured based on recordings from a high-end stereo-video camera system. As objective measures minimum time to collision (TTC_min), minimum time to zebra (TTZ_min), time integrated TTC (TIT), time exposed TTC (TET), minimum predictive encroachment time (PrET_min), pedestrian risk index (PRI) and an in-house metric, the Amberlight Risk Index (ARI) were calculated. As subjective measures the video recordings were rated for risk by three experts and 126 volunteers in an online study. A correlation analysis reveals that subjective ratings of volunteers correlate strongly with TTC_min and the maximum ARI and moderately with TET, TIT, TTZ_min and PRI. The expert ratings correlate moderately with PrET_min and weakly with TET, TIT and PRI. There was only a weak correlation between the ratings of the experts and volunteers. The TTC metric family (TTC_min, TTZ_min, TET and TIT) had strong correlations with each other. Based on subjective video ratings by humans as criterion, this work shows that the minimum of the popular TTC together with the ARI are well suited to predict criticality for cyclist-vehicle interactions at intersections as perceived by volunteers. However, the human ratings did not always agree. More research is needed to investigate why and which subjective risk rating is suited best for a given situation

Effects of visualization quality on the sense of presence in a pedestrian simulator

When designing a VR user experience, presence seems to be a powerful factor. Being part of a plausible, interactable virtual environment not only inspires joy, but also allows for meaningful and immersive gameplay. The VR gaming industry is already an established multi-billion dollar business, but how can human-in-the-loop research benefit from this technology? Which visual standards must be met to ensure valid user data and does a gradation of the degree of realism affect criteria such as presence, task completion and simulator sickness and thus the validity of the simulation? The paper presents a pedestrian simulator study with thirty participants who were sequentially exposed to three versions of the same environment, where the degree of realism in visualization was varied. The study outlines the relationship between the level of visualization and the above criteria. The final data analysis allows the conclusion that a higher degree of realism leads to higher presence ratings but does not affect the overall performance of the subjects. Nonetheless the overall performance improved over time, independent of the degree of realism. Furthermore, there was no evidence of an effect of realism on simulator sickness. The rated simulator sickness was generally low and did not change over time

The study design of UDRIVE: the Naturalistic Driving Study across Europe for cars, trucks and scooters

Purpose: UDRIVE is the first large-scale European Naturalistic Driving Study on cars, trucks and powered two wheelers. The acronym stands for "European naturalistic Driving and Riding for Infrastructure & Vehicle safety and Environment". The purpose of the study is to gain a better understanding of what happens on the road in everyday traffic situations. Methods: The paper describes Naturalistic Driving Studies, a method which provides insight into the actual real-world behaviour of road users, unaffected by experimental conditions and related biases. Naturalistic driving can be defined as a study undertaken to provide insight into driver behaviour during everyday trips by recording details of the driver, the vehicle and the surroundings through unobtrusive data gathering equipment and without experimental control. Data collection will take place in six EU Member States. Results: Road User Behaviour will be studied with a focus on both safety and environment. The UDRIVE project follows the steps of the FESTA-V methodology, which was originally designed for Field Operational Tests. Conclusions: Defining research questions forms the basis of the study design and the specification of the recording equipment. Both will be described in this paper. Although the project has just started collecting data from drivers, we consider the process of designing the study as a major result which may help other initiatives to set up similar studies

Trying to validate subjective reports with a naturalistic driving data - a case against questionnaires and surveys to quantify driver distraction

The effects of different forms of driver distraction on driving distracting activities, however, is less clear. Available are comparatively well understood. How often drivers actually engage in different methods are either not able to provide a complete picture, or are extremely expensive. Post-drive questionnaires and surveys might provide a cheap solution to the problem. As part of a naturalistic driving pilot survey/questionnaire that is intended to capture the occurrence and duration of different secondary tasks. However, for a variety of reasons, this attempt was unsuccessful. It became clear that there was a huge discrepancy between the drivers’ naïve understandings of secondary tasks (what is it, what is part of it, how long is it, etc.) and scientific definitions of the same concepts. Further problems arose from the fact that even though questioned directly after the trip, many accounts appeared to have been reconstructions, rather than recollections of secondary task engagement. We conclude that subjective accounts of secondary task engagement are largely inappropriate to quantify driver distraction

Trying to validate subjective reports with a naturalistic driving data - a case against questionnaires and surveys to quantify driver distraction

The effects of different forms of driver distraction on driving distracting activities, however, is less clear. Available are comparatively well understood. How often drivers actually engage in different methods are either not able to provide a complete picture, or are extremely expensive. Post-drive questionnaires and surveys might provide a cheap solution to the problem. As part of a naturalistic driving pilot survey/questionnaire that is intended to capture the occurrence and duration of different secondary tasks. However, for a variety of reasons, this attempt was unsuccessful. It became clear that there was a huge discrepancy between the drivers’ naïve understandings of secondary tasks (what is it, what is part of it, how long is it, etc.) and scientific definitions of the same concepts. Further problems arose from the fact that even though questioned directly after the trip, many accounts appeared to have been reconstructions, rather than recollections of secondary task engagement. We conclude that subjective accounts of secondary task engagement are largely inappropriate to quantify driver distraction

Trying to validate subjective reports with a naturalistic driving data - a case against questionnaires and surveys to quantify driver distraction

The effects of different forms of driver distraction on driving distracting activities, however, is less clear. Available are comparatively well understood. How often drivers actually engage in different methods are either not able to provide a complete picture, or are extremely expensive. Post-drive questionnaires and surveys might provide a cheap solution to the problem. As part of a naturalistic driving pilot survey/questionnaire that is intended to capture the occurrence and duration of different secondary tasks. However, for a variety of reasons, this attempt was unsuccessful. It became clear that there was a huge discrepancy between the drivers’ naïve understandings of secondary tasks (what is it, what is part of it, how long is it, etc.) and scientific definitions of the same concepts. Further problems arose from the fact that even though questioned directly after the trip, many accounts appeared to have been reconstructions, rather than recollections of secondary task engagement. We conclude that subjective accounts of secondary task engagement are largely inappropriate to quantify driver distraction

Trying to validate subjective reports with a naturalistic driving data - a case against questionnaires and surveys to quantify driver distraction

The effects of different forms of driver distraction on driving distracting activities, however, is less clear. Available are comparatively well understood. How often drivers actually engage in different methods are either not able to provide a complete picture, or are extremely expensive. Post-drive questionnaires and surveys might provide a cheap solution to the problem. As part of a naturalistic driving pilot survey/questionnaire that is intended to capture the occurrence and duration of different secondary tasks. However, for a variety of reasons, this attempt was unsuccessful. It became clear that there was a huge discrepancy between the drivers’ naïve understandings of secondary tasks (what is it, what is part of it, how long is it, etc.) and scientific definitions of the same concepts. Further problems arose from the fact that even though questioned directly after the trip, many accounts appeared to have been reconstructions, rather than recollections of secondary task engagement. We conclude that subjective accounts of secondary task engagement are largely inappropriate to quantify driver distraction