Road safety has become a major concern due to the increased rate of deaths caused by road accidents. For this purpose, intelligent transportation systems are being developed to reduce the number of fatalities on the road. A plethora of work has been undertaken on the detection of different styles of behaviour such as fatigue and drunken behaviour of the drivers; however, owing to complexity of human behaviour, a lot has yet to be explored in this field to assess different styles of the abnormal behaviour to make roads safer for travelling. This research focuses on detection of a very complex driver’s behaviours: ‘tafheet’, reckless and aggressive by proposing and building a driver’s behaviour detection model in the context-aware system in the VANET environment. Tafheet behaviour is very complex behaviour shown by young drivers in the Middle East, Japan and the USA. It is characterised by driving at dangerously high speeds (beyond those commonly known in aggressive behaviour) coupled with the drifting and angular movements of the wheels of the vehicle, which is similarly aggressive and reckless driving behaviour. Thus, the dynamic Bayesian Network (DBN) framework was applied to perform reasoning relating to the uncertainty associated with driver’s behaviour and to deduce the possible combinations of the driver’s behaviour based on the information gathered by the system about the foregoing factors. Based on the concept of context-awareness, a novel Tafheet driver’s behaviour detection architecture had been built in this thesis, which had been separated into three phases: sensing phase, processing and thinking phase and the acting phase. The proposed system elaborated the interactions of various components of the architecture with each other in order to detect the required outcomes from it. The implementation of this proposed system was executed using GeNIe 2.0 software, resulting in the construction of DBN model. The DBN model was evaluated by using experimental set of data in order to substantiate its functionality and accuracy in terms of detection of tafheet, reckless and aggressive behaviours in the real time manner. It was shown that the proposed system was able to detect the selected abnormal behaviours of the driver based on the contextual data collected. The novelty of this system was that it could detect the reckless, aggressive and tafheet behaviour in sequential manner, based on the intensity of the driver’s behaviour itself. In contrast to previous detection model, this research work suggested the On Board Unit architecture for the arrangement of sensors and data processing and decision making of the proposed system, which can be used to pre-infer the complex behaviour like tafheet. Thus it has the potential to prevent the road accidents from happening due to tafheet behaviour.

<p>It is important to detect faults in bus details at an early stage. Because the driving style affects the breakdown of different details in the bus, identification of the driving style is important to minimize the number of failures in buses.</p><p>The identification of the driving style of the driver was based on the input data which contained examples of the driving runs of each class. K-nearest neighbor and neural networks algorithms were used. Different models were tested.</p><p>It was shown that the results depend on the selected driving runs. A hypothesis was suggested that the examples from different driving runs have different parameters which affect the results of the classification.</p><p>The best results were achieved by using a subset of variables chosen with help of the forward feature selection procedure. The percent of correct classifications is about 89-90 % for the k-nearest neighbor algorithm and 88-93 % for the neural networks.</p><p>Feature selection allowed a significant improvement in the results of the k-nearest neighbor algorithm and in the results of the neural networks algorithm received for the case when the training and testing data sets were selected from the different driving runs. On the other hand, feature selection did not affect the results received with the neural networks for the case when the training and testing data sets were selected from the same driving runs.</p><p>Another way to improve the results is to use smoothing. Computing the average class among a number of consequent examples allowed achieving a decrease in the error.</p>

It is important to detect faults in bus details at an early stage. Because the driving style affects the breakdown of different details in the bus, identification of the driving style is important to minimize the number of failures in buses. The identification of the driving style of the driver was based on the input data which contained examples of the driving runs of each class. K-nearest neighbor and neural networks algorithms were used. Different models were tested. It was shown that the results depend on the selected driving runs. A hypothesis was suggested that the examples from different driving runs have different parameters which affect the results of the classification. The best results were achieved by using a subset of variables chosen with help of the forward feature selection procedure. The percent of correct classifications is about 89-90 % for the k-nearest neighbor algorithm and 88-93 % for the neural networks. Feature selection allowed a significant improvement in the results of the k-nearest neighbor algorithm and in the results of the neural networks algorithm received for the case when the training and testing data sets were selected from the different driving runs. On the other hand, feature selection did not affect the results received with the neural networks for the case when the training and testing data sets were selected from the same driving runs. Another way to improve the results is to use smoothing. Computing the average class among a number of consequent examples allowed achieving a decrease in the error.

Reducing the number of motor vehicle crashes is one of the major challenges of our times. Current strategies to reduce crash rates can be divided into two groups: identifying risky driving behavior prior to crashes to proactively reduce risk and automating some or all human driving tasks using intelligent vehicle systems such as Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). For successful implementation of either strategy, a deeper understanding of human driving behavior is essential. This dissertation characterizes human driving behavior through an analysis of a large naturalistic driving study and offers four major contributions to the field. First, it describes the creation of the Surface Accelerations Reference, a catalog of all longitudinal and lateral surface accelerations found in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS). SHRP 2 NDS is the largest naturalistic driving study in the world with 34.5 million miles of data collected from over 3,500 participants driving in six separate locations across the United States. An algorithm was developed to detect each acceleration epoch and summarize key parameters, such as the mean and maxima of the magnitude, roadway properties, and driver inputs. A statistical profile was then created for each participant describing their acceleration behavior in terms of rates, percentiles, and the magnitude of the strongest event in a distance threshold. The second major contribution is quantifying the effect of several factors that influence acceleration behavior. The rate of mild to harsh acceleration epochs was modeled using negative binomial distribution-based generalized linear mixed effect models. Roadway speed category, driver age, driver gender, vehicle class, and location were used as fixed effects, and a unique participant identifier was as the random effect. Subcategories of each fixed effect were compared using incident rate ratios. Roadway speed category was found to have the largest effect on acceleration behavior, followed by driver age, vehicle class, and location. This methodology accounts for the major influences while simultaneously ensuring that the comparisons are meaningful and not driven by coincidences of data collection. The third major contribution is the extraction of acceleration-based long-term driving styles and determining their relationship to crash risk. Rates of acceleration epochs experienced on ≤ 30 mph roadways were used to cluster the participants into four groups. The metrics to cluster the participants were chosen so that they represent long-term driving style and not short-term driving behavior being influenced by transient traffic and environmental conditions. The driving style was also correlated to driving risk by comparing the crash rates, near-crash rates, and speeding behavior of the participants. Finally, the fourth major contribution is the creation of a set of interactive analytics tools that facilitate quick characterization of human driving during regular as well as safety-critical driving events. These tools enable users to answer a large and open-ended set of research questions that aid in the development of ADAS and ADS components. These analytics tools facilitate the exploration of queries such as how often do certain scenarios occur in naturalistic driving, what is the distribution of key metrics during a particular scenario, or what is the relative composition of various crash datasets? Novel visual analytics principles such as video on demand have been implemented to accelerate the sense-making loop for the user. / Doctor of Philosophy / Naturalistic driving studies collect data from participants driving their own vehicles over an extended period. These studies offer unique perspectives in understanding driving behavior by capturing routine and rare events. Two important aspects of understanding driving behavior are longitudinal acceleration, which indicates how people speed up or slow down, and lateral acceleration, which shows how people take turns. In this dissertation, millions of miles of driving data were analyzed to create an open access acceleration database representing the driving profiles of thousands of drivers. These profiles are useful to understand and model human driving behavior, which is essential for developing advanced vehicle systems and smart roadway infrastructure. The acceleration database was used to quantify the effect of various roadway properties, driver demographics, vehicle classification, and environmental factors on acceleration driving behavior. The acceleration database was also used to define distinct driving styles and their relationship to driving risk. A set of interactive analytics tools was developed that leverage naturalistic driving data by enabling users to ask a large set of questions and facilitate open-ended analysis. Novel visualization and data presentation techniques were developed to help users extract deeper insight about driving behavior faster than previously exiting tools. These tools will aid in the development and testing of automated driving systems and advanced driver assistance systems.

Over the last years, driving automation has increasingly moved into focus in human factors research. A large body of research focusses on situations in which the human driver needs to regain control. However, little research has so far been conducted on how SAE level 3+ automated driving should be designed with focus on occupant comfort. This thesis aims at identifying a comfortable driving style for automated vehicles. As a basis, it was necessary to pinpoint driving metrics, which vary between driving styles and can be manipulated in order to design a comfortable driving style. Hence, Study 1 was conducted, in which drivers (N = 24) manually drove on a highway or on urban and rural roads with certain driving styles. Results show relevant metrics (i.e., lateral and longitudinal acceleration, lateral and longitudinal jerk, quickness, and headway distance in seconds) and that these metrics vary across maneuvers and thus, a maneuver-specific analysis is recommended. As these metrics are derived from manual data, it remained unclear after Study 1, in which range the metric values should vary for comfortable automated driving. Therefore, as a second step, the main metrics were varied and the subsequent combinations implemented in an automated vehicle as well as in a dynamic simulator with two different configurations. The combinations were then subject to ratings by 72 participants. Results show that the metrics and values found in Study 1, are able to elicit a range of comfort ratings in automated driving. It was also found, that acceleration is a key variable in experiencing comfort. However, it is not the sole predictor. Additionally, as higher levels of automated driving with larger velocities are still bound to considerable constraints for on-road testing, the second study was also used to validate a dynamic driving simulator to allow comfort during automated driving to be studied. In comparison to ratings on a test track, the dynamic simulator setting with longitudinal orientation is able to show both relative and absolute validity of comfort ratings. In the third and final step, different approaches to automated maneuvers were rated by participants (N = 72) regarding the comfort they experienced. A lane change, an acceleration, and a deceleration maneuver were chosen as test maneuvers. The lateral or longitudinal acceleration was varied in each of these maneuvers. Results, again, show comfort ratings are maneuver specific. On one hand, symmetrical and early-onset lane change maneuvers and symmetrical acceleration maneuvers were preferred. However, symmetrical deceleration maneuvers and deceleration maneuvers with a slower acceleration decrease evoke the highest comfort ratings. These ratings made it possible to offer guidelines for the design of automated driving styles. Furthermore, dependence on a number of personality traits was analyzed. Results suggest the general preference for certain driving styles to be unaffected by personality. However, it seems, participants with certain personality types are less particular about their preference for certain driving styles. Summed up, comfortable automated driving is – under the investigated circumstances – characterized by maneuvers with sufficient headway distance and smooth applications of small acceleration and small jerk. These should, even so, still provide sufficient motion feedback. Surrounding traffic seems to play an important role through urgency and should be considered for on-road implementation. Differences in personality did not seem to play a crucial role.

This paper proposes a model to ensure safe and realistic human-robot interaction for an autonomous vehicle interacting with a human-driven vehicle, by incorporating the driving style of the human driver. The interaction is modeled as a game, where both agents try to maximize future rewards. The driving style of the human is captured via the role of the human driver in the game, capturing the fact that humans with different driving styles reason differently. The solution of the game is obtained using an numerical approximation and used by the autonomous vehicle to plan optimally ahead. The model is validated via simulations on a safety-critical scenario, where realistic driving style-dependent behaviour emerges naturally.

The goal of this work is a design and implementation of an on-board driver’s assistance and assessment system. The system overcomes the problem that typical evaluation of skills is performed by experts who may be subjective and are able to consider only a limited number of factors and indicators. The proposed solution is based on eight indicators, which are associated with the vehicle’s speed, acceleration, jerk, engine rotational speed and driving time. These indicators are used to estimate three driving style criteria: safety, economy and comfort. The comprehensive evaluation is done by merging all indicators into one final score. The system is designed according to User-Centred Design method and follows Internet of Things concept. Raspberry Pi minicomputer is used as a central unit to acquire and store the data during the ride and sending them to a server using GSM network. OBD-II interface is used to obtain the data from the vehicle’s network and GPS and accelerometer modules to acquire additional information. MATLAB environment on a local PC is used to process collected data. An outline of the measurements available from ODB-II interface depending on a car model is made. The proposed system has been implemented and evaluated. The evaluation, conducted by collecting readings for specific road actions at different speeds and with different dynamics, confirms that the chosen indicators reliably represent driver’s behaviour. The system was experimentally validated on a group of drivers. The obtained results prove the system’s ability to quantitatively distinguish different driving styles. The system's stability and usability were verified on long-route test. Moreover, the used spider diagram approach established a convenient visualization platform for multidimensional comparison of the result and comprehensive assessment in an intelligible manner. Overall conclusion is that the developed system is a reliable method of the drivers’ behaviour evaluation.

Fahrerassistenzsysteme werden zunehmend in Fahrzeuge eingebaut mit dem Ziel, den Fahrer beim Fahren zu unterstützen, Fahrfehler zu vermeiden und damit die Fahrsicherheit zu erhöhen. Derzeit sind häufig Systeme im Einsatz, die den Fahrer vor bestimmten Sicherheitsrisiken warnen (z.B. vor einem unbeabsichtigten Verlassen der Fahrspur). Der Trend geht aber hin zu Systemen, die stärker ins Fahrgeschehen eingreifen und somit Teile der Fahraufgabe automatisieren (z.B. selbständig die Spurhaltung des Fahrzeugs übernehmen). Aus der Forschung zur Mensch-Maschine Interaktion ist jedoch bekannt, dass Automatisierung nicht zwangsläufig zur Erhöhung von Sicherheit führt, sondern dass sie vielmehr auch unerwünschte Nebeneffekte für Performanz und Sicherheit mit sich bringen kann in dem Maße, wie Menschen an die veränderten Aufgabenanforderungen adaptieren. Im Straßenverkehr wird insbesondere befürchtet, dass Fahrer sich zu stark auf Fahrerassistenzsysteme verlassen, sich teilweise aus der Fahraufgabe zurückziehen („abschalten“) und ihre Aufmerksamkeit fahrfremden Dingen widmen. Dies kann unter Umständen dazu führen, dass Fahrer im Falle von Systemfehlern oder –ausfällen nicht mehr in der Lage sind rechtzeitig und angemessen einzugreifen bzw. die Kontrolle über das Fahrzeug zu übernehmen. Ziel der Dissertation war es zu untersuchen, inwieweit sich die Involviertheit von Fahrern in die Fahraufgabe verändert je stärker sie durch ein Assistenzsystem unterstützt werden (d.h., je stärker das System Teile der Fahraufgabe automatisiert). Um dies zu untersuchen wurden zwei theoretische Konzepte herangezogen: a) das Verlassen der Fahrer (auf ein System) und b) das Situationsbewusstsein der Fahrer. Basierend auf einer umfassenden Analyse der Forschungsliteratur zum Thema Automatisierung wurde ein theoretisches Rahmenmodell entwickelt, welches Veränderungen in der Involviertheit des Fahrers in die Fahraufgabe auf menschliche Adaptationsprozesse auf verschiedenen Ebenen zurückführt, die sich in Folge der veränderten Aufgaben­anforderungen durch zunehmende Automatisierung ergeben. Dazu zählen Veränderungen in Einstellungen, sowie in kognitiven, energetischen, und motivationalen Prozessen. Um Veränderungen in diesen Prozessen zu untersuchen, wurde eine Vielzahl an objektiven und subjektiven Maßen erhoben. Hauptgegenstand der Dissertation ist eine umfangreiche Fahrsimulatorstudie im Fahrsimulator mit Bewegungsplattform bei VTI (Swedish National Road and Transport Research Institute) in Linköping, Schweden. Dabei kamen zwei Querführungsassistenzsysteme (ein Heading Control System und ein Lane Departure Warning System) zum Einsatz, die den Fahrer in unterschiedlichem Maße bei der Spurhaltung unterstützten. Im Gegensatz zu einem Großteil der bisherigen Studien wurden prozessorientierte Performanzmaße zur Erfassung des Verlassens der Fahrer auf die Assistenzsysteme und des Situationsbewusstseins der Fahrer verwendet. Das Verlassen der Fahrer auf die Querführungsassistenzsysteme wurde durch Blickverhaltensmaße über die Bereitschaft der Fahrer erfasst, ihre visuelle Aufmerksamkeit von der Straße ab hin zu einer Zweitaufgabe im Fahrzeuginnenraum zu wenden. Zur Messung des Situationsbewusstseins der Fahrer wurden Fahrverhaltensmaße herangezogen welche als Indikator für die Schnelligkeit und Abruptheit der Reaktionen der Fahrer auf unerwartete kritische Fahrsituationen dienten. Ein Hauptbefund der Dissertation war, dass die Fahrer sich signifikant im Ausmaß ihres Verlassens auf einen hohen Grad an Assistenz unterschieden. Diese interindividuelle Varianz im Verlassen der Fahrer auf einen hohen Grad an Assistenz konnte am besten durch das Vertrauen der Fahrer in das Querführungsassistenzsystem und ihr Aktivierungsniveau erklärt werden: Je höher das Vertrauen der Fahrer in das System und je geringer ihr Aktivierungsniveau, desto stärker verließen sie sich auf das System. Individuelle Fahrermerkmale (Fahrstil) erklärten einen signifikanten Anteil der Varianz im Vertrauen der Fahrer in die Spurhalteassistenzsysteme. (ersetzt wegen neuem Herausgeber) / Advanced driver assistance systems are increasingly built in vehicles with the aim to support drivers while driving, to reduce driver errors and thereby to increase traffic safety. At present, these systems are often designed to warn drivers of specific safety risks (e.g., of an imminent departure from the driving lane). However, there is a trend towards systems that more strongly intervene in driving and that hence, automate parts of the driving task (e.g., autonomously keep the vehicle within the driving lane). However, research on human-machine interaction has shown that automation does not necessarily increase safety, but that it may also lead to unanticipated side effects on performance and safety to the extent that humans adapt to the changing task demands. A major concern in road traffic is that drivers rely too heavily on driver assistance systems, become less actively involved in the driving task, and divert their attention to things unrelated to driving. Thus, in the case of system malfunctions or failures, drivers possibly may not be prepared to intervene timely and accordingly and to regain control over the vehicle, respectively. The aim of this dissertation was to investigate changes in drivers’ active engagement in the driving task as a function of the degree to which they are supported by a driver assistance system (i.e., as a function of the degree to which the system automates the driving task). Drivers’ active task engagement was studied by referring to two theoretical concepts: a) drivers’ reliance (on a system) and b) drivers’ situation awareness. Based on an extensive review of previous research on automation, a conceptual theoretical framework was developed that links changes in operators’ active task engagement to human adaptation processes on different levels in response to the changing task demands due to automation. Among them are changes in human attitudes as well as in cognitive, motivational and energetic processes. In order to determine the relative influence of these processes, a range of objective and subjective measures was collected. The essential part of the dissertation is an extensive driving simulator study in an advanced moving-base driving simulator at VTI (Swedish National Road and Transport Research Institute) in Linköping, Sweden. Two lateral support systems (a Heading Control system and a Lane Departure Warning system) were implemented which assisted drivers to different degrees in lane keeping. Contrary to most previous automation studies, drivers’ reliance on the lane keeping assistance systems and their situation awareness were studied by using process-oriented performance-based measures. Drivers’ reliance on the lane keeping assistance systems was assessed by eye glance behaviour measures indicating drivers’ preparedness to allocate their visual attention away from the road scene to an in-vehicle secondary task. Drivers’ situation awareness was assessed by behavioural measures of the latency and magnitude of drivers’ initial reactions to unexpected critical driving situations. A major finding of the study was that drivers differed significantly in their reliance on a high level of lane keeping assistance. This interindividual variance in drivers’ reliance on higher-level assistance could be best explained by drivers’ trust in the system and their energetic arousal: The greater drivers’ trust in the system and the lower their arousal, the more did they rely on the system. Individual driver variables (driving style) explained a significant proportion of the variance in drivers’ trust in the lane keeping assistance systems. (replaced because a new publisher)

Advanced driver assistance systems are increasingly built in vehicles with the aim to support drivers while driving, to reduce driver errors and thereby to increase traffic safety. At present, these systems are often designed to warn drivers of specific safety risks (e.g., of an imminent departure from the driving lane). However, there is a trend towards systems that more strongly intervene in driving and that hence, automate parts of the driving task (e.g., autonomously keep the vehicle within the driving lane). However, research on human-machine interaction has shown that automation does not necessarily increase safety, but that it may also lead to unanticipated side effects on performance and safety to the extent that humans adapt to the changing task demands. A major concern in road traffic is that drivers rely too heavily on driver assistance systems, become less actively involved in the driving task, and divert their attention to things unrelated to driving. Thus, in the case of system malfunctions or failures, drivers possibly may not be prepared to intervene timely and accordingly and to regain control over the vehicle, respectively. The aim of this dissertation was to investigate changes in drivers’ active engagement in the driving task as a function of the degree to which they are supported by a driver assistance system (i.e., as a function of the degree to which the system automates the driving task). Drivers’ active task engagement was studied by referring to two theoretical concepts: a) drivers’ reliance (on a system) and b) drivers’ situation awareness. Based on an extensive review of previous research on automation, a conceptual theoretical framework was developed that links changes in operators’ active task engagement to human adaptation processes on different levels in response to the changing task demands due to automation. Among them are changes in human attitudes as well as in cognitive, motivational and energetic processes. In order to determine the relative influence of these processes, a range of objective and subjective measures was collected. The essential part of the dissertation is an extensive driving simulator study in an advanced moving-base driving simulator at VTI (Swedish National Road and Transport Research Institute) in Linköping, Sweden. Two lateral support systems (a Heading Control system and a Lane Departure Warning system) were implemented which assisted drivers to different degrees in lane keeping. Contrary to most previous automation studies, drivers’ reliance on the lane keeping assistance systems and their situation awareness were studied by using process-oriented performance-based measures. Drivers’ reliance on the lane keeping assistance systems was assessed by eye glance behaviour measures indicating drivers’ preparedness to allocate their visual attention away from the road scene to an in-vehicle secondary task. Drivers’ situation awareness was assessed by behavioural measures of the latency and magnitude of drivers’ initial reactions to unexpected critical driving situations. A major finding of the study was that drivers differed significantly in their reliance on a high level of lane keeping assistance. This interindividual variance in drivers’ reliance on higher-level assistance could be best explained by drivers’ trust in the system and their energetic arousal: The greater drivers’ trust in the system and the lower their arousal, the more did they rely on the system. Individual driver variables (driving style) explained a significant proportion of the variance in drivers’ trust in the lane keeping assistance systems. / Fahrerassistenzsysteme werden zunehmend in Fahrzeuge eingebaut mit dem Ziel, den Fahrer beim Fahren zu unterstützen, Fahrfehler zu vermeiden und damit die Fahrsicherheit zu erhöhen. Derzeit sind häufig Systeme im Einsatz, die den Fahrer vor bestimmten Sicherheitsrisiken warnen (z.B. vor einem unbeabsichtigten Verlassen der Fahrspur). Der Trend geht aber hin zu Systemen, die stärker ins Fahrgeschehen eingreifen und somit Teile der Fahraufgabe automatisieren (z.B. selbständig die Spurhaltung des Fahrzeugs übernehmen). Aus der Forschung zur Mensch-Maschine Interaktion ist jedoch bekannt, dass Automatisierung nicht zwangsläufig zur Erhöhung von Sicherheit führt, sondern dass sie vielmehr auch unerwünschte Nebeneffekte für Performanz und Sicherheit mit sich bringen kann in dem Maße, wie Menschen an die veränderten Aufgabenanforderungen adaptieren. Im Straßenverkehr wird insbesondere befürchtet, dass Fahrer sich zu stark auf Fahrerassistenzsysteme verlassen, sich teilweise aus der Fahraufgabe zurückziehen („abschalten“) und ihre Aufmerksamkeit fahrfremden Dingen widmen. Dies kann unter Umständen dazu führen, dass Fahrer im Falle von Systemfehlern oder –ausfällen nicht mehr in der Lage sind rechtzeitig und angemessen einzugreifen bzw. die Kontrolle über das Fahrzeug zu übernehmen. Ziel der Dissertation war es zu untersuchen, inwieweit sich die Involviertheit von Fahrern in die Fahraufgabe verändert je stärker sie durch ein Assistenzsystem unterstützt werden (d.h., je stärker das System Teile der Fahraufgabe automatisiert). Um dies zu untersuchen wurden zwei theoretische Konzepte herangezogen: a) das Verlassen der Fahrer (auf ein System) und b) das Situationsbewusstsein der Fahrer. Basierend auf einer umfassenden Analyse der Forschungsliteratur zum Thema Automatisierung wurde ein theoretisches Rahmenmodell entwickelt, welches Veränderungen in der Involviertheit des Fahrers in die Fahraufgabe auf menschliche Adaptationsprozesse auf verschiedenen Ebenen zurückführt, die sich in Folge der veränderten Aufgaben­anforderungen durch zunehmende Automatisierung ergeben. Dazu zählen Veränderungen in Einstellungen, sowie in kognitiven, energetischen, und motivationalen Prozessen. Um Veränderungen in diesen Prozessen zu untersuchen, wurde eine Vielzahl an objektiven und subjektiven Maßen erhoben. Hauptgegenstand der Dissertation ist eine umfangreiche Fahrsimulatorstudie im Fahrsimulator mit Bewegungsplattform bei VTI (Swedish National Road and Transport Research Institute) in Linköping, Schweden. Dabei kamen zwei Querführungsassistenzsysteme (ein Heading Control System und ein Lane Departure Warning System) zum Einsatz, die den Fahrer in unterschiedlichem Maße bei der Spurhaltung unterstützten. Im Gegensatz zu einem Großteil der bisherigen Studien wurden prozessorientierte Performanzmaße zur Erfassung des Verlassens der Fahrer auf die Assistenzsysteme und des Situationsbewusstseins der Fahrer verwendet. Das Verlassen der Fahrer auf die Querführungsassistenzsysteme wurde durch Blickverhaltensmaße über die Bereitschaft der Fahrer erfasst, ihre visuelle Aufmerksamkeit von der Straße ab hin zu einer Zweitaufgabe im Fahrzeuginnenraum zu wenden. Zur Messung des Situationsbewusstseins der Fahrer wurden Fahrverhaltensmaße herangezogen welche als Indikator für die Schnelligkeit und Abruptheit der Reaktionen der Fahrer auf unerwartete kritische Fahrsituationen dienten. Ein Hauptbefund der Dissertation war, dass die Fahrer sich signifikant im Ausmaß ihres Verlassens auf einen hohen Grad an Assistenz unterschieden. Diese interindividuelle Varianz im Verlassen der Fahrer auf einen hohen Grad an Assistenz konnte am besten durch das Vertrauen der Fahrer in das Querführungsassistenzsystem und ihr Aktivierungsniveau erklärt werden: Je höher das Vertrauen der Fahrer in das System und je geringer ihr Aktivierungsniveau, desto stärker verließen sie sich auf das System. Individuelle Fahrermerkmale (Fahrstil) erklärten einen signifikanten Anteil der Varianz im Vertrauen der Fahrer in die Spurhalteassistenzsysteme.

Fahrerassistenzsysteme werden zunehmend in Fahrzeuge eingebaut mit dem Ziel, den Fahrer beim Fahren zu unterstützen, Fahrfehler zu vermeiden und damit die Fahrsicherheit zu erhöhen. Derzeit sind häufig Systeme im Einsatz, die den Fahrer vor bestimmten Sicherheitsrisiken warnen (z.B. vor einem unbeabsichtigten Verlassen der Fahrspur). Der Trend geht aber hin zu Systemen, die stärker ins Fahrgeschehen eingreifen und somit Teile der Fahraufgabe automatisieren (z.B. selbständig die Spurhaltung des Fahrzeugs übernehmen). Aus der Forschung zur Mensch-Maschine Interaktion ist jedoch bekannt, dass Automatisierung nicht zwangsläufig zur Erhöhung von Sicherheit führt, sondern dass sie vielmehr auch unerwünschte Nebeneffekte für Performanz und Sicherheit mit sich bringen kann in dem Maße, wie Menschen an die veränderten Aufgabenanforderungen adaptieren. Im Straßenverkehr wird insbesondere befürchtet, dass Fahrer sich zu stark auf Fahrerassistenzsysteme verlassen, sich teilweise aus der Fahraufgabe zurückziehen („abschalten“) und ihre Aufmerksamkeit fahrfremden Dingen widmen. Dies kann unter Umständen dazu führen, dass Fahrer im Falle von Systemfehlern oder –ausfällen nicht mehr in der Lage sind rechtzeitig und angemessen einzugreifen bzw. die Kontrolle über das Fahrzeug zu übernehmen. Ziel der Dissertation war es zu untersuchen, inwieweit sich die Involviertheit von Fahrern in die Fahraufgabe verändert je stärker sie durch ein Assistenzsystem unterstützt werden (d.h., je stärker das System Teile der Fahraufgabe automatisiert). Um dies zu untersuchen wurden zwei theoretische Konzepte herangezogen: a) das Verlassen der Fahrer (auf ein System) und b) das Situationsbewusstsein der Fahrer. Basierend auf einer umfassenden Analyse der Forschungsliteratur zum Thema Automatisierung wurde ein theoretisches Rahmenmodell entwickelt, welches Veränderungen in der Involviertheit des Fahrers in die Fahraufgabe auf menschliche Adaptationsprozesse auf verschiedenen Ebenen zurückführt, die sich in Folge der veränderten Aufgaben­anforderungen durch zunehmende Automatisierung ergeben. Dazu zählen Veränderungen in Einstellungen, sowie in kognitiven, energetischen, und motivationalen Prozessen. Um Veränderungen in diesen Prozessen zu untersuchen, wurde eine Vielzahl an objektiven und subjektiven Maßen erhoben. Hauptgegenstand der Dissertation ist eine umfangreiche Fahrsimulatorstudie im Fahrsimulator mit Bewegungsplattform bei VTI (Swedish National Road and Transport Research Institute) in Linköping, Schweden. Dabei kamen zwei Querführungsassistenzsysteme (ein Heading Control System und ein Lane Departure Warning System) zum Einsatz, die den Fahrer in unterschiedlichem Maße bei der Spurhaltung unterstützten. Im Gegensatz zu einem Großteil der bisherigen Studien wurden prozessorientierte Performanzmaße zur Erfassung des Verlassens der Fahrer auf die Assistenzsysteme und des Situationsbewusstseins der Fahrer verwendet. Das Verlassen der Fahrer auf die Querführungsassistenzsysteme wurde durch Blickverhaltensmaße über die Bereitschaft der Fahrer erfasst, ihre visuelle Aufmerksamkeit von der Straße ab hin zu einer Zweitaufgabe im Fahrzeuginnenraum zu wenden. Zur Messung des Situationsbewusstseins der Fahrer wurden Fahrverhaltensmaße herangezogen welche als Indikator für die Schnelligkeit und Abruptheit der Reaktionen der Fahrer auf unerwartete kritische Fahrsituationen dienten. Ein Hauptbefund der Dissertation war, dass die Fahrer sich signifikant im Ausmaß ihres Verlassens auf einen hohen Grad an Assistenz unterschieden. Diese interindividuelle Varianz im Verlassen der Fahrer auf einen hohen Grad an Assistenz konnte am besten durch das Vertrauen der Fahrer in das Querführungsassistenzsystem und ihr Aktivierungsniveau erklärt werden: Je höher das Vertrauen der Fahrer in das System und je geringer ihr Aktivierungsniveau, desto stärker verließen sie sich auf das System. Individuelle Fahrermerkmale (Fahrstil) erklärten einen signifikanten Anteil der Varianz im Vertrauen der Fahrer in die Spurhalteassistenzsysteme. (ersetzt wegen neuem Herausgeber) / Advanced driver assistance systems are increasingly built in vehicles with the aim to support drivers while driving, to reduce driver errors and thereby to increase traffic safety. At present, these systems are often designed to warn drivers of specific safety risks (e.g., of an imminent departure from the driving lane). However, there is a trend towards systems that more strongly intervene in driving and that hence, automate parts of the driving task (e.g., autonomously keep the vehicle within the driving lane). However, research on human-machine interaction has shown that automation does not necessarily increase safety, but that it may also lead to unanticipated side effects on performance and safety to the extent that humans adapt to the changing task demands. A major concern in road traffic is that drivers rely too heavily on driver assistance systems, become less actively involved in the driving task, and divert their attention to things unrelated to driving. Thus, in the case of system malfunctions or failures, drivers possibly may not be prepared to intervene timely and accordingly and to regain control over the vehicle, respectively. The aim of this dissertation was to investigate changes in drivers’ active engagement in the driving task as a function of the degree to which they are supported by a driver assistance system (i.e., as a function of the degree to which the system automates the driving task). Drivers’ active task engagement was studied by referring to two theoretical concepts: a) drivers’ reliance (on a system) and b) drivers’ situation awareness. Based on an extensive review of previous research on automation, a conceptual theoretical framework was developed that links changes in operators’ active task engagement to human adaptation processes on different levels in response to the changing task demands due to automation. Among them are changes in human attitudes as well as in cognitive, motivational and energetic processes. In order to determine the relative influence of these processes, a range of objective and subjective measures was collected. The essential part of the dissertation is an extensive driving simulator study in an advanced moving-base driving simulator at VTI (Swedish National Road and Transport Research Institute) in Linköping, Sweden. Two lateral support systems (a Heading Control system and a Lane Departure Warning system) were implemented which assisted drivers to different degrees in lane keeping. Contrary to most previous automation studies, drivers’ reliance on the lane keeping assistance systems and their situation awareness were studied by using process-oriented performance-based measures. Drivers’ reliance on the lane keeping assistance systems was assessed by eye glance behaviour measures indicating drivers’ preparedness to allocate their visual attention away from the road scene to an in-vehicle secondary task. Drivers’ situation awareness was assessed by behavioural measures of the latency and magnitude of drivers’ initial reactions to unexpected critical driving situations. A major finding of the study was that drivers differed significantly in their reliance on a high level of lane keeping assistance. This interindividual variance in drivers’ reliance on higher-level assistance could be best explained by drivers’ trust in the system and their energetic arousal: The greater drivers’ trust in the system and the lower their arousal, the more did they rely on the system. Individual driver variables (driving style) explained a significant proportion of the variance in drivers’ trust in the lane keeping assistance systems. (replaced because a new publisher)