This study was conducted to determine whether adding in-vehicle technology to vehicles resulted in increased driver awareness of motorcycles. The specific technology tested consisted of a warning light which illuminated on the vehicle’s instrument panel when the vehicle was near a motorcycle. The effect of motorcycle color on driver awareness was also explored. Participants were recruited to drive a highidelity driving simulator in a city environment. Eye-tracker data was collected and used to determine how much attention drivers paid to the motorcycles in the simulation. Results showed that the in-vehicle technology significantly increased driver awareness of motorcycles, but the color of the motorcycles had no impact on driver awareness.

In this master thesis the dynamics of a modern main battle tank's turret traverse and gun elevation have been modelled. The models of dynamic motion have been coupled to two different types of electric motors, namely a direct-current motor and an induction motor. These have been modelled in MATLAB and SIMULINK together with the mechanical systems in the turret traverse and gun elevation. The goal of this project was to develop non-ideal models of the combined mechanical and electrical systems, but the main focus has been the dynamics of the electric motors. / I denna examensavhandling har modeller av elektriska motorer som driver tornet samt elevation av eldröret på en stridsvagn tagits fram. De två motorer som undersökts är en likströmsmotor och en induktionsmotor. Dessa har kopplats till mekaniska system som representerar rotation av stridsvagnens torn och elevation av eldröret. Modelleringen har gjorts i MATLAB och SIMULINK. Målet med denna studie var att ta fram icke-ideala modeller av både de elektriska motorerna och de mekaniska systemen för torn- och eldrörsdrift.

Motivation - Komplexe Anforderungen an die Karosserie - Unbekannte Auswirkungen von Parametervariationen in der frühen Phase - Informationsdefizit führt zu Iterationsschleifen Ziel Beurteilung der Auswirkung von Parametervariationen von Werkstoffen, Produktionsverfahren, Geometrien auf die Karosserie-Eigenschaften

A plug-in hybrid electric vehicle (PHEV) is a vehicle powered by a combination of an internal combustion engine and an electric motor with a battery pack. The battery pack can be charged by plugging the vehicle into the electric grid or from using excess engine power. A PHEV allows for all electric operation for limited distances, while having the operation and range of a conventional hybrid electric vehicle on longer trips. A PHEV design with design parameters electric motor size, engine size, battery capacity, and battery chemistry type, is optimized with minimum cost as a figure of merit. The PHEV is required to meet a fixed set of performance constraints consisting of 0-60 mph acceleration, 50-70 mph acceleration, 0-30 mph acceleration in all electric operation, top speed, grade ability, and all electric range. The optimization is carried out for values of all electric range of 10, 20, and 40 miles. The social and economic impacts of the optimum designs in terms of reduced gasoline consumption and carbon emissions reduction are calculated. Argonne National Laboratorys Powertrain Systems Analysis Toolkit is used to simulate the performance and fuel economy of the PHEV designs. The costs of different PHEV components and the present value of battery replacements over the vehicles life are used to determine the designs drivetrain cost. The resulting optimum PHEVs are designs using lead acid battery type. The optimum design parameter values are all determined by a single controlling performance constraint. The PHEV designs show a 63% to 80% reduction in gasoline consumption and a 53% to 47% reduction in CO2 emissions. The PHEV designs have an annual gas savings of $696 to $643 per year over the average sedan meeting the 27.5 mpg CAFE standards.

Research has suggested that affective states have critical effects on various cognitive processes and performance. Evidence from driving studies has also emphasized the importance of driver situation awareness (Endsley, 1995b) for driving performance and safety. However, to date, no research has investigated the relationship between affective effects and driver situation awareness. Two studies examined the relationship between a driver's affective states and situation awareness. In Experiment 1, 30 undergraduates drove in a simulator after either anger or neutral affect induction. Results suggested that an induced angry state can degrade driver situation awareness and driving performance more than the neutral state. Interestingly, the angry state did not influence participants' perceived workload. Experiment 2 explored the possibilities of using an "attention deployment" emotion regulation strategy as an intervention for mitigating angry effects on driving, via an adaptive speech-based system. 60 undergraduates drove the same scenario as in Experiment 1 after affect induction with different intervention conditions: anger with no sound; anger with the ER system: directive/ command style emotion regulation messages; anger with the SA system: suggestive/ notification style situation awareness prompts; or neutral with no sound. Results showed that both speech-based systems can not only enhance driver situation awareness and driving performance, but also reduce the anger level and perceived workload. Participants rated the ER system as more effective, but they rated the SA system as less annoying and less authoritative than the ER system. Based on the results of Experiment 2, regression models were constructed between a driver's affective states and driving performance, being mediated by situation awareness (full mediation for speeding and partial mediation for collision). These results allow researchers to construct a more detailed driver behavior model by showing how an affective state can influence driver situation awareness and performance. The practical implications of this research include the use of situation awareness prompts as a possible strategy for mitigating affective effects, for the design of an affect detection and mitigation system for drivers.

The automobile is a cultural artifact embedded in our lives and imbued with meaning. Autonomous vehicle technology stands to alter not just the way we drive or whether we drive, it also has the power to fundamentally change the way we live. The development of driverless cars enables the examination of the complex relationships that individuals have with the automobile and reveals the fears associated with this technological change.

The decision to select the best Intelligent Transportation System (ITS) technologies from available options has always been a challenging task. The availability of connected vehicle/automated vehicle (CV/AV) technologies in the near future is expected to add to the complexity of the ITS investment decision-making process. The goal of this research is to develop a multi-criteria decision-making analysis (MCDA) framework to support traffic agencies’ decision-making process with consideration of CV/AV technologies. The decision to select between technology alternatives is based on identified performance measures and criteria, and constraints associated with each technology. Methods inspired by the literature were developed for incident/bottleneck detection and back-of-queue (BOQ) estimation and warning based on connected vehicle (CV) technologies. The mobility benefits of incident/bottleneck detection with different technologies were assessed using microscopic simulation. The performance of technology alternatives was assessed using simulated CV and traffic detector data in a microscopic simulation environment to be used in the proposed MCDA method for the purpose of alternative selection. In addition to assessing performance measures, there are a number of constraints and risks that need to be assessed in the alternative selection process. Traditional alternative analyses based on deterministic return on investment analysis are unable to capture the risks and uncertainties associated with the investment problem. This research utilizes a combination of a stochastic return on investment and a multi-criteria decision analysis method referred to as the Analytical Hierarchy Process (AHP) to select between ITS deployment alternatives considering emerging technologies. The approach is applied to an ITS investment case study to support freeway bottleneck management. The results of this dissertation indicate that utilizing CV data for freeway segments is significantly more cost-effective than using point detectors in detecting incidents and providing travel time estimates one year after CV technology becomes mandatory for all new vehicles and for corridors with moderate to heavy traffic. However, for corridors with light, there is a probability of CV deployment not being effective in the first few years due to low measurement reliability of travel times and high latency of incident detection, associated with smaller sample sizes of the collected data.

Motivated by recent advances in vehicle positioning and vehicle-to-infrastructure (V2I) communication, traffic signal controllers are able to make smarter decisions. Most of the current state-of-the-practice signal priority control systems aim to provide priority for only one mode or based on first-come-first-served logic. Consideration of priority control in a more general framework allows for several different modes of travelers to request priority at any time from any approach and for other traffic control operating principles, such as coordination, to be considered within an integrated signal timing framework. This leads to provision of priority to connected priority eligible vehicles with minimum negative impact on regular vehicles. This dissertation focuses on providing a real-time decision making framework for multi modal traffic signal control that considers several transportation modes in a unified framework using Connected Vehicle (CV) technologies. The unified framework is based on a systems architecture for CVs that is applicable in both simulated and real world (field) testing conditions. The system architecture is used to design both hardware-in-the-loop and software-in-the-loop CV simulation environment. A real-time priority control optimization model and an implementation algorithm are developed using priority eligible vehicles data. The optimization model is extended to include signal coordination concepts. As the penetration rate of the CVs increases, the ability to predict the queue more accurately increases. It is shown that accurate queue prediction improves the performance of the optimization model in reducing priority eligible vehicles delay. The model is generalized to consider regular CVs as well as priority vehicles and coordination priority requests in a unified mathematical model. It is shown than the model can react properly to the decision makers' modal preferences.

Motivation - Komplexe Anforderungen an die Karosserie - Unbekannte Auswirkungen von Parametervariationen in der frühen Phase - Informationsdefizit führt zu Iterationsschleifen Ziel Beurteilung der Auswirkung von Parametervariationen von Werkstoffen, Produktionsverfahren, Geometrien auf die Karosserie-Eigenschaften

<p dir="ltr">Each year, approximately 1.35 million people die globally due to vehicle crashes, and in the United States alone, 42,915 traffic fatalities were recorded in 2021, reflecting a 10.5% increase from 2020 and an 18% increase from 2019. Driver fatigue and drowsiness significantly contribute to these fatalities, as fatigue severely impairs a driver’s alertness and responsiveness, leading to a higher risk of accident. Given the prevalence of drowsy driving accidents, it is crucial to implement advanced systems that alert drivers to their drowsy condition, significantly reducing traffic-related deaths and injuries. While these systems have shown significant effects in reducing the risks related to drowsy driving, most commercially available and widely researched alert systems heavily rely on auditory and visual sensory channels. These modalities may cause "alarm fatigue," leading drivers to ignore or deactivate the systems entirely, and result in a lower driving experience. Due to their frequent occurrence and potential annoyance, the National Highway Traffic Safety Administration (NHTSA) recommends that auditory warnings, which are the most commonly used modality in current driver alert systems, are generally unsuitable for first-stage cautionary alerts. Despite NHTSA human factors guidance, most in-vehicle warning systems consist of auditory and visual modalities, even in the first cautionary stage alerts. Therefore, advanced alerting systems that balance the functionality of alerts and driving experience, using non-audio and non-visual modalities, are needed.</p><p dir="ltr">With this motivation, the purpose of this Ph.D. dissertation work is to propose a novel approach to both olfactory and climate adaptive alerting systems and demonstrate their usability in in-vehicle engagement experiences. In Study 1 (Chapter 3), the use of behavioral metrics and physiological sensing was validated to assess drivers' cognitive states during driving. This validation laid the groundwork for the future evaluation of the effects of the proposed alerting system in Study 2(Chapter 4) and Study 3 (Chapter 5). In Study 2, the impact of olfactory and climate stimuli on drivers' cognitive states was investigated by studying time-variant changes. This investigation helped determine if the proposed stimuli can be effectively utilized in driver alerting systems. In Study 3, the proposed sensory-integrated alerting adaptive systems were developed and evaluated for their effect on drivers in a drowsy state. The evaluations focused on the systems’ abilities to provide a sufficient salient effect, sustained arousal effect, and driver satisfaction.</p><p dir="ltr">This dissertation introduces a new approach to driving alert systems to ensure both alert functionality and driving experience. Ultimately, this work offers a new direction for developing advanced alerting systems, particularly for first-stage warnings.</p>