Sharing data pipelines: Why sharing data may not be enough, and what to do about it.

New research challenges and low-cost technological solutions drive the motivation to record behavior in multivariate ways using high temporal resolution. Making such data accessible and usable is more complicated than it may seem. Methods like ECG, EEG, and eye tracking can produce very large amounts of data in a short time. Further, context data to explain the observed behavior must be recorded as well. E.g., in a field study using an instrumented research vehicle, the position of the vehicle and the distance to the vehicle in front could act as context data. To make this multitude of data analyzable, data must be cleaned and fused in data pipelines. Cleaning happens in multiple stages, and requires decisions which have direct effects on patterns in the data. Time series data are often up- or down sampled, potentially altering characteristics of signals of interest. Sharing the data pipeline alongside an uncleaned version of the data therefore should be the default when publishing research results. Data science has developed a number of solutions to store and document data and data pipelines, whose benefits and costs will be discussed in this talk. These approaches can be structured in three interdependent dimensions: data storage, data processing, and competencies required by developers and users of data pipelines. Data from empirical studies can be very challenging to store, process, and document. Solutions to these issues do exist, but they require a training which is yet to be implemented in the typical Psychology curriculum

Modeling Driving Behavior at Roundabouts: Impact of Roundabout Layout and Surrounding Traffic on Driving Behavior

Driving behavior prediction at roundabouts is an important challenge to improve driving safety by supporting drivers with intelligent assistance systems. To predict the driving behavior effciently steering wheel status was proven to have robust predictability based on a Support Vector Machine algorithm. Previous research has not considered potential effects of roundabout layout and surrounding traffic on driving behavior, but that consideration can certainly improve the prediction results. Therefore, this study investigated how roundabout layout and surrounding traffic impact driving behavior of an ego car. A simulator study was conducted to collect driving behavior data with different roundabout layout settings and different surrounding cyclist position settings. The local minima/maxima of the steering angle was found to have a logarithmic relationship with the roundabout geometric feature. The impact of the surrounding traffic on the ego driver behavior was also found: When there were surrounding cyclists, the recognition rate of ego driver behavior patterns reached 100% later than when there was no surrounding traffic. In conclusion, driving behavior at roundabouts is effected by both roundabout layout and surrounding traffic, and the relationship can be expressed in a quantitative way

Task and domain modelling and validation for dynamic situations

Task and work domain analysis are widely used methodologies to model the domain of traffic systems. However, their validation beyond expert judgement has received little attention. The problem is exacerbated by traffic systems being complex and dynamic environments where discrete task execution stages are difficult to model. We argue that model validation should follow a template linking conceptual task models with operationalised task models, which in turn are used to make quantitative predictions about the domain. Successful predictions will increase a model's claim to validty whereas failed predictions question that claim. Further, it is important to address those variables relevant to a domain. For human behavior in traffic systems, these variables are represented by the TASC conceptual framework presented here. TASC splits human behavior into the Task under consideration, Actions taken, the Situation, and the human embodied Cognition. The validation approach to is demonstrated using a lightweight work domain analysis of the driving task. An abstraction hierarchy of the traffic system was constructed with special focus on values and priority measures. For selected values and priority measures, testable predictions were made regarding human drivers assuming the abstraction hierarchy to be valid. Subsequently, data were gathered in a driving simulator. Twenty-one participants drove on a two-lane motorway in two scenarios in random order. The "controlled" scenario consisted of vehicles showing very predictable behavior; the "realistic" scenario had medium-dense traffic behaving similarly to everyday traffic. The participants were instructed to drive according to traffic rules. Eye-tracking data were recorded. Nine participants drove the two scenarios again while being instructed to think aloud focusing on perceptions and goals. Based on the data, we produced separate graphical representations for the TASC levels of action, situation, and cognition representing the time course of the drive for each subject. The cognition-level was split into perception (eye tracking) and goals (thinking aloud). Behavior on right lane differed markedly from behavior on left lane. Values appeared clearly in driving actions, gaze behavior, and thinking aloud utterances. Predictions from the abstraction hierarchy were statistically evaluated using linear mixed models. Generally, observed data followed the predictions. The template for task and domain model validation could be effectively used to address questions of validity regarding the abstraction hierarchy. Turning values into predictions regarding defined measures also helped to sharpen the Abstraction hierarchy on a conceptual level. The TASC-framework proved very useful to analyse dynamic situations. However, it also became clear that values had been underspecified in their original formulation. Additionally, it the linkage of values and actual measures for the values was identified as a potential issue complication questions of validity. More effort should be directed towards validation of task models. We recommend making operationalisation of task models standard practice when conducting task analysis to help planning of evaluation studies and assessment of generalizability of results beyond the task environment studied. To gain a better understanding of the cognition of task execution, more research into setting of multiple goals, action selection, and situation representation in dynamic environments is highly desirable

ATO-Cargo: Betriebsverfahren für die Rückfallebenen des hochautomatisierten Bahnbetriebes / ATO-Cargo: Operating procedures for the fallback levels of highly automated railway operation

DE: Im Projekt ATO-Cargo wird ein vollautomatisierter Betrieb von Güterzügen getestet, bei dem ein Remote Supervision and Control Center (RSC) insbesondere Aufgaben in der Rückfallebene übernehmen soll. Dies umfasst bspw. die Betriebsart Remote Train Operation. Das Deutsche Zentrum für Luft- und Raumfahrt entwirft in diesem Projekt unter anderem Betriebsverfahren für Use Cases, in denen das RSC involviert ist. Anschließend werden Human Factors Analysen zu den Arbeitsprozessen eines RSC-Operators durchgeführt sowie die möglichen Veränderungen in den Anforderungen an die menschliche Leistungsfähigkeit für die neue Rolle des RSC-Operators identifiziert. EN: In the ATO-Cargo project, a fully automated operation of freight trains is being tested, in which a Remote Supervision and Control Centre (RSC) is to take over tasks in the fallback level in particular. This includes, for example, the Remote Train Operation mode. In this project, the German Aerospace Center (DLR) is designing, among other things, operating procedures for use cases in which the RSC is involved. Subsequently, human factors analyses are carried out on the work processes of an RSC operator and the possible changes in the human performance requirements for the new role of the RSC operator are identified

Task modelling and model validation for car driving

Task analysis is a powerful tool to model human behavior within sociotechnical systems. However, the validation beyond expert judgement has received inadequate attention. The problem is exacerbated in dynamic environments where discrete task execution stages are difficult to model. We argue that model validation should follow an iterative approach using the TASC conceptual framework presented here. Building on Gray and Boehm-Davis' (2000) notion of interactive behavior, TASC splits behavior into the Task under consideration, Actions taken, the Situation, and the human embodied Cognition. The approach consists of a) conducting the task analysis to define a task model, b) data collection, and c) validating the task model on the levels of action, situation, and cognition. This framework is demonstrated using a lightweight task analysis of the driving task. A Cognitive Work Analysis (CWA) of the driving task was conducted, yielding five top-level goals. Subsequently, data were gathered in a driving simulator. Twenty-one participants drove on a two-lane motorway in two scenarios in random order. The "controlled" scenario consisted of vehicles showing very predictable behavior; the "realistic" scenario had medium-dense traffic behaving similarly to everyday traffic. The participants were instructed to drive according to traffic rules. Eye-tracking data were recorded. Nine participants drove the two scenarios again while being instructed to think aloud focusing on perceptions and goals. Based on the data, we produced separate graphical representations for the TASC levels of action, situation, and cognition representing the time course of the drive for each subject. The cognition-level was split into perception (eye tracking) and goals (thinking aloud). Finally, on each level, each CWA goal was operationalized and statistically evaluated using linear mixed models. Behavior on right lane differed markedly from behavior on left lane in line with the CWA goals. Goals appeared clearly in driving actions, gaze behavior, and thinking aloud utterances. Visual behavior shows a distinctive pattern depending on situational requirements in different phazes of the drive. The TASC-framework proved very useful to validate the CWA task analysis. The idea of task analysis has limitations in modelling driving because of a strong reliance on discrete states. Yet an important property of the driving task is its execution in the continuous world of time, space, and energy. Goals act frequently not as states to be achieved but as constraints on possible actions and can be quickly altered depending on the dynamic situation. More effort should be directed towards validation of task models. We recommend making operationalization of task models standard practice when conducting task analysis to help planning of evaluation studies and assessment of generalizability of results beyond the task environment studied. To gain a better understanding of the cognition of task execution, more research into setting of multiple goals, action selection, and situation representation in dynamic environments is highly desirable

HALC - Highway Assist Lane Chane. Gesamt-Schlusbericht.

Das Projekt "HALC - Highway Assist - Lane Change" hatte die Untersuchung, Entwicklung und Einführung von Funktionen zur integrierten Längs- und Querführung für automatisiertes Fahren speziell für schwere und mittlere Nutzfahrzeugen zum Ziel. HALC adressiert sämtliche Szenarien auf Autobahnen und autobahnähnlichen Straßen mit baulich getrennten Fahrbahnen im Geschwindigkeitsbereich von 0 bis 89 km/h. Basierend auf der Analyse aktueller Unfallsituationen sowie einer Aufgabenanalyse sollten Fahrermodelle konzipiert und entwickelt werden, die das Fahrverhalten der Fahrer und die Interaktion mit den Fahrzeugsystemen auf die Bediensituation in Nutzfahrzeugen angepasst abbilden und die Entwicklung eines an Nutzfahrzeuge angepassten Highway Assist Systems ermöglichen, das aufbauend auf bisher entwickelten Funktionen wie z.B. dem Lane Keeping Assistent (LKA) alle erforderlichen Fahraufgaben von Spurhaltung über Bestimmung von Fahrstreifenwechselmöglichkeiten bis hin zur sicheren Durchführung von Fahrstreifenwechseln beinhaltet. Der Automatisierungsgrad des HALC-Systems gemäß SAE J3016 Definition ist "Partial Driving Automation" (Level 2). Damit hat der Fahrer die Aufgabe, das System und die Umwelt jederzeit zu überwachen und jederzeit bereit zu sein, die Fahraufgabe vollständig übernehmen zu können. Der Technology Readiness Level (TRL) für das entwickelte HALC-System ist 6 von 9 (Prototyp in Einsatzumgebung)

Human Centered Development of SAE-2 Automation for Truck Driving

Compared with passenger cars, automating driving functions for trucks and understanding related driver behaviour are considerably less advanced, potentially due to difficulties accessing relevant technology and subject matter experts (SMEs). Project HALC's (HighwayAssist with Lane Change) first objective was to develop the HALC system, which is an automation assisting truck drivers on highways. The system performs longitudinal and lateral control of commercial trucks, including lane changes. To optimally support truck drivers' interactions with the HALC system, we developed its functionality and human-machine interface (HMI) in close cooperation with SMEs. As an SAE level 2 (SAE International, 2014) type automation, the system may require drivers to resume control anytime. Thus, the second objective was to develop a monitoring system to ensure drivers' takeover readiness. The project's third objective was to understand better truck driver behaviour based on data collected within the project. This knowledge was then used to inform the HALC system's design and implementation

Digitizing Travel Experience: Assessing, Modeling and Visualizing the Experiences of Travelers in Shared Mobility Services

During shared travel, humans regularly have negative experiences resulting from unmet needs in terms of safety, comfort, accessibility, efficiency, reliability or information. Frequent negative travel experiences motivate travelers to use private motorized transport instead of more sustainable, shared mobility services. It is difficult for shared transport providers to react to such negative experiences, as these mostly depend on individual needs and situational factors and can therefore rarely be counteracted with static one-size-fits-all solutions. Additionally, (real-time) information about a traveler’s experience is not (digitally) available to providers and thus a situation-adapted reaction is often not possible. Therefore, methods to assess travel experience and make travel experience digitally available are highly important for enabling means to render shared transport more attractive. Here, we present initial research on digitizing travel experience exemplified by an envisioned automated shuttle line

Similarities in the Perception of Color and Velocity. An Empirical Investigation of the Cognitive Representation of Velocity.

Object properties that can be perceived simultaneously and without focused attention are called features (Treisman, 1980). Examples include color and the movement of objects. To date, the question whether velocity is a Feature or not has received little attention. In order to build exact cognitive models of visual object recognition this research gap has to be filled. Therefore, the current study compared the visual perceptibility of color and velocity changes. A varying number of moving and non-moving squares was presented to 22 participants. In conditions with moving objects squares moved on linear trajectories with different velocities. Further, the color of the squares was varied between conditions, with the squares being either unicolored or multicolored. Based on paradigms from working memory research, the first presentation of the stimuli was followed by a brief pause and a second presentation (Luck & Vogel, 1997). In the second presentation color, velocity or both attributes of one square were changed. The participants had to decide whether both presentations were identical or differed. From the number of correct and incorrect answers the sensitivity inde

D5.6 – Techniques and Tools for Empirical Analysis Vs2.0 incl. Handbooks and Requirements Analysis Update

Abschlussbericht für das AP5 (Empirical Human Factor Analysis Techniques and Tools) des EU-Projektes HoliDes