This diploma thesis summarizes basic findings about issues of making development samples of PCB. The emphasis is especially on required quality which complies with mentioned norms. The theoretical section includes methodology for evaluating quality dismounted boards, assembling and soldering, parameters of components under different environmental circumstances. The practical part is carried out in cooperation with the department EEG in R&D Automotive Lighting Jihlava. It is dedicated to the design and the preparation of development samples where the quality is assessed according to given methodology from the theoretical part. PCB are analyzed by an X-ray, metallographic grinding and other methods. Recommendations are given and based on results for improvements.

Abgelenktes Fahren gehört zu den Hauptursachen von Verkehrsunfällen und kann auf visuelle, manuelle oder kognitive Ablenkungsquellen zurückgeführt werden. Jede dieser Ablenkungsquellen wurde bereits mit negativen Effekten auf die Fahrerleistung in Zusammenhang gebracht. Obschon ein weitgehender Konsens über negative Auswirkungen von visueller/visuell-manueller Ablenkung besteht, sind die Wirkungen kognitiver Ablenkung auf Fahrfehler und Unfälle noch immer umstritten. Viele experimentelle Studien haben negative Auswirkungen kognitiver Ablenkung auf die Fahrerleistung berichtet. Demgegenüber stehen jedoch die Ergebnisse der Mehrzahl vorliegender „naturalistic driving studies“, die kein erhöhtes Unfallrisiko oder sogar protektive Effekte in diesem Zusammenhang fanden. Die aktuelle Entwicklung hin zu Mensch-Fahrzeug-Schnittstellen, die die Bedienung diverser Anwendungen mittels Sprachsteuerung ermöglichen, führt zu einem Anstieg von kognitiver Beanspruchung beim Fahren. Es ist daher von entscheidender Bedeutung, die Auswirkungen kognitiver Ablenkung auf die Fahrerleistung zu erfassen, um den Verantwortungsträgern in der Gesellschaft, den Regierungen und der Industrie eine Risikoabschätzung dieser Funktionen zu ermöglichen und die Sicherheit von Mensch-Fahrzeug-Schnittstellen zu erhöhen. Das Hauptziel dieser Dissertation bestand darin, die Effekte von kognitiver Ablenkung auf die Fahrerleistung zu untersuchen. Verschiedene Arten kognitiver Ablenkung, die sich beim Fahren unter realen Bedingungen häufig auf die Fahrer auswirken, wurden in dieser Arbeit kodiert und analysiert: kognitiv ablenkende Nebenaufgaben (z.B. telefonieren, singen), Fahreremotionen (z.B. Freude, Wut/Frustration, Traurigkeit) und Kombinationen von Fahreremotionen und Nebenaufgaben (z.B. Streit mit dem Beifahrer oder am Telefon). Bei der Untersuchung von Effekten kognitiver Ablenkung auf das Fahren sind Umwelt-, Situations- und Personenfaktoren zu berücksichtigen, da sie Mediator- und Moderatorvariablen bei der Erfassung des relativen Risikos von Ablenkung beim Fahren im Straßenverkehr darstellen. Daher folgte diese Dissertation dem ganzheitlichen Ansatz, so viele relevante Variablen wie möglich zu betrachten, die mit der Ausführung kognitiv ablenkender Tätigkeiten interagieren. Zu diesem Zweck wurden Daten der derzeit umfangreichsten „naturalistic driving study“ (the second Strategic Highway Research Program, SHRP 2) kodiert und analysiert, um möglichst viele Situationen, in denen eine kognitive Beanspruchung die Fahrerleistung potenziell beeinflusste, umfassend zu bewerten. Gleichzeitig wurde eine große Zahl von Mediator- und Moderatorvariablen betrachtet, die beim Fahren im realen Straßenverkehr auftreten (z.B. Einfluss von Kreuzungen, Wetter, etc.). Dieser Ansatz sollte das Verständnis und die externe Validität der Ergebnisse erhöhen und stellt einen wichtigen Schritt hin zu einem vollständigen Modell jener Variablen dar, die entweder zu unangemessen Verhaltensweisen und Unfällen beitragen oder sie reduzieren. Im Rahmen der Dissertation wurden vier Studien durchgeführt, die auf der Grundlage von zwei SHRP 2 Datensätzen die Zusammenhänge zwischen kognitiven und anderen Ablenkungsquellen, Umwelt-, Situations- und Personenfaktoren und Fahrerleistung untersuchten. Weiterhin wurden Kausalfaktoren in 315 vom Fahrer verursachten Unfällen und Beinaheunfällen, die mit Fahrerablenkung, Fahrerbeeinträchtigung oder keinem dieser Faktoren assoziiert waren, analysiert. Die erste Studie untersuchte die Auswirkungen von Wut beim Fahren und Streit mit dem Beifahrer oder jemandem am Telefon auf die Fahrerleistung. Wut beim Fahren ging mit einer Häufung aggressiver Verhaltensweisen einher, jedoch nicht mit einer Erhöhung von Fahrfehlern. Streitgespräche mit dem Beifahrer oder einer Person am Telefon (das heißt, wenn mutmaßlich das höchste Maß an kognitiver Ablenkung vorlag), schienen darüber hinaus mit keiner Form von unangemessenen Verhaltensweisen im Zusammenhang zu stehen. Die zweite Studie untersuchte, wie sich kognitive, visuelle und manuelle Fahrerablenkung, emotionale Beeinträchtigung sowie Umwelt-, Situations- und Persönlichkeitsfaktoren auf die Fahrerleistung auswirken. Ein Zusammenhang zwischen kognitiver Ablenkung und einer Verschlechterung der Fahrerleistung konnte nicht festgestellt werden. Die dritte Studie replizierte und erweiterte Ergebnisse der zweiten Untersuchung auf der Grundlage eines größeren Datensatzes, bestehend aus Fahrsegmenten, die Unfällen, Beinaheunfällen und Baselines vorausgingen und weder emotionale noch andere Fahrerbeeinträchtigungen enthielten. In Übereinstimmung mit den Ergebnissen der ersten und zweiten Studie, wurde keine Assoziation zwischen kognitiver Ablenkung und einer verschlechterten Fahrerleistung festgestellt. Bei der vierten Studie handelte es sich um eine vergleichende Analyse von Risikofaktoren für Unfälle/ Beinaheunfälle, die mit verschiedenen Arten von Ablenkung, Beeinträchtigung oder keinem von beiden, assoziiert waren. Unfälle, denen eine kognitive Ablenkung vorausgegangen war, waren vor allem mit von Ablenkung unabhängigen Fahrfehlern verbunden - genau wie die Unfälle, denen keine beobachtbare Nebentätigkeit vorausgegangen war. Dieses Ergebnis lässt vermuten, dass in früheren „naturalistic driving studies“, das Unfallrisiko von kognitiv ablenkenden Nebentätigkeiten eventuell sogar überschätzt wurde. Zusammenfassend legen die Ergebnisse die Schlussfolgerung nahe, dass kognitive Ablenkung durch beobachtbare emotionale Beeinträchtigung, (überwiegend) kognitiv ablenkende Nebenaufgaben oder die Kombination dieser beiden Faktoren, nicht mit sichtbaren negativen Auswirkungen auf die Fahrerleistung im tatsächlichen Straßenverkehr assoziiert werden kann. Im Gegensatz dazu hatten ablenkende Tätigkeiten, die zu Blickabwendungen von der Straße führen, und solche, die mit einem besonders hohen Unfallrisiko assoziiert werden, die größte Wahrscheinlichkeit Fahrfehler und Unfälle zu verursachen. / Driver distractions are among the leading causes of motor vehicle accidents. Such distractions can stem from competing visual, manual, or cognitive resources, all of which have been associated with detrimental effects on driving performance. Although the negative impacts of visual/visual-manual distraction are widely agreed upon, the effects of cognitive load on driving errors and crash risk are still debated. On the one hand, numerous experimental studies have shown adverse effects of cognitive distraction on driving performance. In contrast, most existing naturalistic driving studies have either not revealed increased crash/near-crash risk due to cognitive distraction, or have even reported a safety benefit. The number of in-vehicle tasks placing cognitive load on the driver is increasing in recent years due to the development of auditory human–machine interfaces such as voice control for several functions. This has enhanced the need to assess how cognitive distraction affects driving performance. These results are necessary to provide society, government, and industry with valid risk estimates, which will affect decision making regarding how to enhance the safety of using in-vehicle human-machine interfaces while driving. Therefore, the main objective of this thesis was to investigate how cognitive distraction affects driving performance. Different types of cognitive distraction that commonly affect most drivers in naturalistic conditions were coded and analyzed in the present thesis, including: cognitively distracting secondary tasks (e.g., talking on the phone, singing), driver emotion (e.g., happiness, anger/frustration, sadness), and combinations of driver emotion and secondary task demand (e.g., arguing with a passenger or with someone on the phone). Environmental, situational, and individual factors cannot be ignored when investigating the effects of cognitive distraction on driving performance, as they are mediating and moderating variables for estimating distraction relative risk in naturalistic driving. Therefore, a holistic approach guided this thesis towards incorporating as many important variables as possible that interact with the engagement in cognitively distracting activities. Data from the largest naturalistic driving study ever conducted (the second Strategic Highway Research Program, SHRP 2) were coded and analyzed to comprehensively assess many situations in which cognitive load potentially affected driving performance. Further, the goal was to simultaneously consider many possible mediating and moderating variables existent in real-world traffic (such as intersection influences, weather, etc.). This approach should increase understanding and external validity of the results, as well as represent an important step towards building a complete model depicting variables that contribute to or mitigate aberrant driving behaviors and crash risk. Four different analyses focused on two SHRP 2 data subsets to assess the relationship between cognitive and other distraction sources, environmental, situational, and individual factors, as well as driving performance. In addition, contributing factors in 315 at-fault crash and near-crash events associated with driver distraction, driver impairment, or neither of the two were analyzed. The first study examined driving performance in relation to driving anger as well as arguing with a passenger or with someone on the phone. Results showed that driving anger was associated with more frequent aggressive driving behaviors without increasing driving error frequency. Furthermore, when a conflict arose with a passenger or with someone on the phone (i.e., when the level of cognitive distraction was expected to be highest), there did not appear to be a link to any type of aberrant driving behavior. The second study analyzed driving performance based on cognitive, visual, and manual driver distraction, emotional impairment, as well as environmental, situational, and individual factors. Cognitive distraction was not associated with any decline in driving performance. The purpose of the third analysis was to replicate and extend the second study’s effects based on a larger data sample of driving segments preceding crashes, near-crashes, and matched baselines, of drivers not exhibiting emotional or other impairment types. Corroborating the first and second study’s results, there was no association between cognitive distractions and impaired driving performance. Finally, the fourth study compared the risk factors of crashes/near-crashes associated with either different driver distraction types, impairment, or neither. Crashes preceded by cognitive distraction were mainly associated with driving errors unrelated to the secondary task demands, as were the crashes preceded by no observable secondary task. This finding suggests that previous studies analyzing naturalistic driving data may have even overestimated the crash risk of cognitively distracting secondary task engagement. In summary, this thesis provides compelling evidence that cognitive distraction, either through observable emotional impairment, (mainly) cognitively distracting secondary tasks, or the combination of both, has no apparent relation with poorer driving performance observable in real-world traffic. On the contrary, distracting activities requiring the driver’s gaze to move away from the forward roadway and those associated with a particularly high crash risk had the highest chances of causing driving errors and crashes.

This paper discusses the challenges and the trade-offs in the design of laser drivers for very-short distance optical communications. A prototype integrated circuit is designed and fabricated in 28 nm super-low-power CMOS technology. The power consumption of the transmitter is 17.2 mW excluding the VCSEL that in our test has a DC power consumption of 10 mW. The active area of the driver is only 0.0045 mm². The driver can achieve an error-free (<BER < 10^12) electrical data-rate of 25 Gbit/s using a pseudo random bit sequence of 2^7-1. When the driver is connected to the VCSEL module an open optical eye is reported at 15 Gbit/s. In the tested bias point the VCSEL module has a measured bandwidth of 10.7 GHz.

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<p dir="ltr">Distracted driving is at the forefront of the leading causes of road accidents. Therefore, research advancements in Driver Monitoring Systems (DMS) are vital in facilitating prevention techniques. These systems must be able to detect anomalous driving behavior by evaluating deviations from some predefined normal driving behavior. This thesis proposes an improved contrastive learning approach that introduces a hybrid loss function combining triplet loss and supervised contrastive loss, as well as improvements to the projection head of the framework. It progresses the architecture by performing a multi-threshold severity calculation and data processing using an exponential moving average technique. Due to the unbounded possibilities of anomalous driving behaviors, the proposed framework was tested on the Driver Anomaly Detection (DAD) dataset that incorporates multi-modal and multi-view inputs in an open set recognition setting. The test set of the DAD dataset has anomalous actions that are unseen by the trained model; therefore, high precision on such a dataset demonstrates success on any other closed-set recognition task. The proposed framework achieved an impressive accuracy, reaching 94.14\%, AUC-ROC at 0.9787, and AUC-PR at 0.9781 on the test set. These findings contribute to in-vehicle monitoring by providing a scalable and adaptable framework suitable for real-world conditions.</p>

Advanced Driver Assistance Systems (ADAS) offer the possibility of helping drivers to fulfill their driving tasks. Automated vehicles are capable of communicating with surrounding vehicles (V2V) and infrastructure (V2I) in order to collect and provide essential information about driving environment. Studies have proved that automated vehicles have a potential to decrease traffic congestion on road networks by reducing the time headway, enhancing the traffic capacity and improving the safety margins in car following. Furthermore, vehicle movement and driver’s behavior of conventional vehicles will be affected by the presence of automated vehicles in traffic networks. Despite different encouraging factors, automated driving raises some concerns such as possible loss of situation awareness, overreliance on automation and degrading driving skills in absence of practice. Moreover, coping with complex scenarios, such as merging at ramps and overtaking, in terms of interaction between automated vehicles and conventional vehicles need more research. This thesis work aims to investigate the effects of automated vehicles on driver’s behavior and traffic performance. A broad literature review in the area of driving simulators and psychological studies was performed to examine the automated vehicle effects on driver’s behavior. Findings from the literature survey, which has been served as setup values in the simulation study of the current work, reveal that the conventional vehicles, which are driving close to the platoon of automated vehicles with short time headway, tend to reduce their time headway and spend more time under their critical time headway. Additionally, driving highly automated vehicles is tedious in a long run, reduce situation awareness and can intensify driver drowsiness, exclusively in light traffic. In order to investigate the influences of automated vehicles on traffic performance, a microscopic simulation case study consisting of different penetration rates of automated vehicles (0, 50 and 100 percentages) was conducted in VISSIM software. The scenario network is a three-lane autobahn segment of 2.9 kilometers including an off-ramp, on-ramp and a roundabout with some surrounding urban roads. Outputs of the microscopic simulation in this study reveal that the positive effects of automated vehicles on roads are especially highlighted when the network is crowded (e.g. peak hours). This can definitely count as a constructive point for the future of road networks with higher demands. In details, average density of autobahn segment remarkably decreased by 8.09% during p.m. peak hours in scenario with automated vehicles. Besides, Smoother traffic flow with less queue in the weaving segment was observed. Result of the scenario with 50% share of automated vehicles moreover shows a feasible interaction between conventional vehicles and automated vehicles. Meaningful outputs of this case study, based on the input data from literature review, demonstrate the capability of VISSIM software to simulate the presence of automated vehicles in great extent, not only as an automated vehicle scenario but also a share of them, in traffic network. The validity of the output values nonetheless needs future research work on urban and rural roads with different traffic conditions.

For this project the main goal was to design, simulate, build and test a DC motor driver. To accomplish this four specific parts had to be design. First a DCDC converter that converts the input signal from an external power supply in a controllable DC output. The second part was a DCDC converter that converts the DC input in a voltage useful for the function generator, the third part is the function generator that provides a signal to the H-Bridge, and the final part is the H-Bridge itself. The goal is to compare the measurement with the simulations to the expectations. What we are interested in is the influence of EMF when the RPM of the motor is zero or close to zero.

This work provides an in-depth-view of driver motivational aspects when driver assistance Systems (DAS) are considered. Thereby, the role of driver actual experience with DAS use was also identified and highlighted. A central outcome of this thesis is the STADIUM model describing the interplay of motivational factors that determine the engagement in secondary activities while taking actual DAS use experience into account. The role of motives in showing attentive behaviour depending on DAS (the navigation system) could also be underlined. The relevance, enrichment and need of combining qualitative and quantitative approaches when the effects of safety countermeasures on driver behaviour are investigated could also be shown. The results are discussed in terms of hierarchical driver behaviour models, the theory of planned behaviour and its extended versions and the strengths of the introduced studies and limitations. Implications for traffic safety are provided and future research issues are recommended. / Diese Arbeit liefert einen gründlichen Einblick, welche Rolle motivationale Aspekte spielen, wenn Fahrerassistenzsysteme (FAS) genutzt werden. Dabei wurde auch die Funktion der tatsächlichen Erfahrung mit FAS identifiziert und hervorgehoben. Ein zentrales Ergebnis dieser Arbeit ist das STADIUM Modell, welches das Zusammenspiel motivationaler Faktoren in Abhängigkeit von der tatsächlichen Erfahrung mit FAS erklärt, die wiederum bestimmen, inwieweit und ob andere Aktivitäten während des Fahrens ausgeführt werden. Außerdem konnte unterstrichen werden, welche Rolle Motive spielen, aufmerksames Verhalten in Abhängigkeit von der Nutzung von FAS (dem Navigationssystem) zu zeigen. Zusätzlich konnte dargestellt werden, wie relevant, bereichernd und nützlich es ist, qualitative und quantitative Methoden zu kombinieren, wenn die Effekte von FAS auf das FahrerInnenverhalten untersucht werden. Die Ergebnisse werden diskutiert indem auf hierarchische Fahrerverhaltensmodelle, auf die Theorie des geplanten Verhaltens und ihre erweiterten Versionen und auf die Stärken und Schwächen der Studien Bezug genommen wird. Es werden Implikationen dargestellt und zukünftige Forschungsfragen und Problemstellungen empfohlen.

The thesis presents an investigation of pregnant women s safety and comfort needs during car travel. A survey is used to investigate all aspects and problems of car travel. This survey is a comprehensive examination of the entire driving activity with much detail of reported difficulties from pregnant women that forms a novel resource for the automotive engineers. The survey results are used to generate guidelines for the automotive industry. A series of sled tests are presented that investigate seat belt use in pregnancy including the use of lap belt positioners. The peak abdominal pressure results clearly agree with current guidelines that the lap belt should be positioned across the hips and not across the abdomen. This research includes a novel anthropometric dataset for 107 pregnant women including measurements especially selected for the field of automotive design and to describe the changes of pregnancy. This includes investigation of pregnant driver s proximity to the steering wheel. A novel measurement of knee splay is used to define the pregnant women s preference to sit with their knees widely spaced instead of knees together, in both normal sitting and in a car. Comparison is made between the pregnant women's measurements and the available data in the literature for non-pregnant women and males, and this shows that pregnant women can be excluded from designs if the accommodation does not consider their needs. The pregnant women's anthropometric data is presented as a novel website in order to make the data available to the automotive industry. This website is generated for use by automotive engineers and is designed to suit their usability needs and the general trends within the industry, in order to make the site more user-friendly and more likely to be used as a reference for pregnant occupant's needs.

Vehicle dynamics development is strongly based on subjective assessments (SA) of vehicle prototypes, which is expensive and time consuming. Consequently, in the age of computer- aided engineering (CAE), there is a drive towards reducing this dependency on physical test- ing. However, computers are known for their remarkable processing capacity, not for their feelings. Therefore, before SA can be computed, it is required to properly understand the cor- relation between SA and objective metrics (OM), which can be calculated by simulations, and to understand how this knowledge can enable a more efficient and effective development process. The approach to this research was firstly to identify key OM and SA in vehicle dynamics, based on the multicollinearity of OM and of SA, and on interviews with expert drivers. Sec- ondly, linear regressions and artificial neural network (ANN) were used to identify the ranges of preferred OM that lead to good SA-ratings. This result is the base for objective require- ments, a must in effective vehicle dynamics development and verification. The main result of this doctoral thesis is the development of a method capable of predicting SA from combinations of key OM. Firstly, this method generates a classification map of ve- hicles solely based on their OM, which allows for a qualitative prediction of the steering feel of a new vehicle based on its position, and that of its neighbours, in the map. This prediction is enhanced with descriptive word-clouds, which summarizes in a few words the comments of expert test drivers to each vehicle in the map. Then, a second superimposed ANN displays the evolution of SA-ratings in the map, and therefore, allows one to forecast the SA-rating for the new vehicle. Moreover, this method has been used to analyse the effect of the tolerances of OM requirements, as well as to verify the previously identified preferred range of OM. This thesis focused on OM-SA correlations in summer conditions, but it also aimed to in- crease the effectiveness of vehicle dynamics development in general. For winter conditions, where objective testing is not yet mature, this research initiates the definition and identifica- tion of robust objective manoeuvres and OM. Experimental data were used together with CAE optimisations and ANOVA-analysis to optimise the manoeuvres, which were verified in a second experiment. To improve the quality and efficiency of SA, Volvo’s Moving Base Driving Simulator (MBDS) was validated for vehicle dynamics SA-ratings. Furthermore, a tablet-app to aid vehicle dynamics SA was developed and validated. Combined this research encompasses a comprehensive method for a more effective and ob- jective development process for vehicle dynamics. This has been done by increasing the un- derstanding of OM, SA and their relations, which enables more effective SA (key SA, MBDS, SA-app), facilitates objective requirements and therefore CAE development, identi- fies key OM and their preferred ranges, and which allow to predict SA solely based on OM. / <p>QC 20170223</p> / iCOMSA