Recent studies on robustness of machine learning systems shows that today’s autonomous vehicles struggle with very basic visual disturbances such as rain or snow. There is also a lack of training data that includes off road scenes or scenes with different forms of deformation to the road surface. The purpose of this thesis is to address the lack of off-road scenes in current dataset for training of autonomous vehicles and the issue of visual disturbances by building a simulated 3D environment for generating training scenarios and training data for specific environments. The synthesised scenes is implemented using modern OpenGL, and we propose methods to synthesis rutting and the formation of potholes on road surfaces as well as rain and fog with a parameterized approach. \\ The generated datasets are tested through semantic segmentation using state of the art pretrained neural networks. The results show that the neural networks accurately identifies the road surface in in clear weather as long as the road surface is mostly coherent. The synthesised rain and fog decrease performance of the neural networks significantly. \\ Generating training data with the method presented in this thesis and incorporating it as part of the training data used in training neural networks for autonomous driving systems could be used to improve performance in certain scenarios. Specifically, it could improve performance in driving scenes with heavy road deformations, and in scenes with low visibility. Further research is needed to conclude that the data is useful, but the results generated in this thesis is promising.

Tidigare forskning har visat att unga i jämförelse med äldre förare, har en mer positiv inställning till att ta risker och håller en generellt högre hastighet i trafiken (Hatfield & Fernandes, 2009). Forskning har också visat att unga förare, framför allt män har en tendens att överskatta sin körförmåga (De Craen et al., 2011). Självskattning kan mätas medolika metoder, något som bland annat Sundström (2008) menar kan påverka resultatet. Flera studier har visat att unga förare kan göra en mer exakt bedömning av sin körprestation när de bedömer en specifik körförmåga snarare än när de jämför deras allmänna körprestation med den genomsnittliga förarens (Mynttinen et al., 2009a; Mynttinen et al., 2009b). Syftet med den aktuella studien var att undersöka om självskattad körprestation påverkas av körerfarenhet samt om det fanns något samband mellan körprestation och självskattad körprestation eller riskbedömning. Studien ämnade att göra detta genom att besvara följande frågeställningar 1. Finns det ett samband mellan ålder och självskattad körförmåga? 2. Finns det ett samband mellan körprestation och självskattad körprestation? 3. Finns det ett samband mellan körprestation och riskbedömning? Totalt deltog 48 förare i studien, där hälften var i åldrarna 18–25 och resterande i åldrarna 45–60. Experimentet genomfördes hos Statens väg- och transportforskningsinstitut (VTI) där deltagarna fick köra ett 20 minuter långt scenario i en stationär körsimulator. Innan, under och efter körningen ombads deltagarna att skatta sin körprestation och upplevda risk. Efter körningen hölls en kompletterande intervju. Resultaten visade att det inte fanns något signifikant samband mellan ålder och självskattad körförmåga, något som tyder på att körerfarenhet inte hade någon inverkan på självskattning. Det fanns en antydan till samband mellan körprestation och både självskattning och riskbedömning. För riskbedömning generellt fanns en potentiell effekt för både ålder och kön, något som inte fanns hos självskattad körprestation. Studien ger en indikation på samband för både körprestation och självskattad körprestation samt körprestation och riskbedömning som behöver studeras vidare med ett större underlag. / Previous research has shown that young in comparison with older drivers, have a more positive attitude towards taking risks and maintain a generally higher speed in traffic (Hatfield & Fernandes, 2009). Research has also shown that young drivers, especially men, tend tooverestimate their ability to drive (De Craen et al., 2011). Self-assessment can be measured using different methods, something that Sundström (2008), with others,believes can affect the result.Several studies have shown that young drivers can make a more accurate assessment of their driving performance when assessing a specific driving ability rather than when they are assessingtheir general driving performance against the average driver (Mynttinen et al., 2009a; Mynttinen et al., 2009b). The purpose of the current study was to investigate whether self-rated driving performance was affected by driving experience and whether there was any correlation between driving performance and self-rated driving performance or risk assessment. The study intended to do this by answering the following questions 1. Is there a correlation between age and self-rated driving ability? 2. Is there a correlation between driving performance and self-rated driving performance? 3. Is there a correlation between driving performance and risk assessment? A total of 48 drivers participated in the study, half of them were in the age group 18–25 and the rest were in the age group 45–60. The experiment was carried out at the Swedish National Road and Transport Research Institute (VTI) where the participants completed a 20-minute long driving scenario in a stationary simulator. Before, during and after the drive, participants were asked to estimate their driving performance and perceived risk. After that, a supplementary interview was held. The results showed that there was no significant relationship between age and self-rated driving ability, which indicates that driving experience had no effect on self-assessment. There was a hint of correlation between driving performance and both self-assessment and risk assessment. For risk assessment in general, there was an effect for both age and gender, something that did not exist in self-rated driving performance. The study provides an indication of a relationship between both driving performance and self-rated driving performance, as well as driving performance and risk assessment that need to be studied further with a larger sample.

Recent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both drivers and passengers. In this article, we propose a reference architecture for a context-aware driving assistant system. Moreover, we exemplify this architecture with a real prototype of a driving assistance system called Driving coach. This prototype collects, fuses and analyses diverse information, like digital map, weather, traffic situation, as well as vehicle information to provide drivers in-depth information regarding their previous trip along with personalised hints to improve their fuel-efficient driving in the future. The Driving coach system monitors its own performance, as well as driver feedback to correct itself to serve the driver more appropriately.

Seeking a valuable and relevant topic for the future of mobility. the author came across the pain point trust in relation to artificial intelligence. Advances in the creation of artificial intelligence and deep learning ensure that our everyday lives are increasingly shaped by algorithms, sometimes consciously, sometimes unconsciously.For many people, this idea causes discomfort, and especially in situations of one‘s own vulnerability, the question of how an AI will handle more responsible tasks in the future will be essential. The automotive industry will also be shaped by this issue. In the intelligent car of the future, people will at least partially relinquish both control and privacy. Autonomous driving will be a test of trust for future users, as will the question of digital ethics and the collection of private data. In this thesis, a possible answer to the question was explored, how we can shape the approach and interaction with technology - especially artificial intelligence - in the future in order to create trustf uluser experiences. For this purpose, beyond the formal-aesthetic elaboration, the main focus was on interactive solutions and communication with AI, how an AI behaves in the vehicle and how it can contribute to making users feel comfortable in such a context. BMW Me&AI describes a scenario in which potential customers get to know an intelligent vehicle for the first time and are carefully introduced to its processes and possibilities. Inspired by soft robotics, the presented interior design is mainly defined by a holistic concept of soft interaction surfaces. Three basic scenarios are described in which passengers have the freedom to either look over AI‘s shoulder, sit back and focus on other things, or be completely on their own. This created a result that became unique in its dynamics and degree of adaptability and posed a real challenge, especially for the creative process, which in retrospect clearly paid off.

<p dir="ltr">The growth of electric vehicles (EVs) offers several benefits for air quality improvement and emissions reduction. Nonetheless, EVs also pose several challenges in the area of highway transportation. These barriers are related to the limitations of EV technology, particularly the charge duration and speed of battery recharging, which translate to vehicle range anxiety for EV users. A promising solution to these concerns is V2V DWC technology (Vehicle to Vehicle Dynamic Wireless Charging), particularly mobile energy disseminators (MEDs). The MED is mounted on a large vehicle or truck that charges all participating EVs within a specified locus from the MED. However, current research on MEDs offers solutions that are widely considered impractical for deployment, particularly in urban environments where range anxiety is common. Acknowledging such gap in the literature, this thesis proposes a comprehensive methodological framework for optimal MED deployment decisions. In the first component of the framework, a practical system, termed “ChargingEnv” is developed using reinforcement learning (RL). ChargingEnv simulates the highway environment, which consists of streams of EVs and an MED. The simulation accounts for a possible misalignment of the charging panel and incorporates a realistic EV battery model. The second component of the framework uses multiple deep RL benchmark models that are trained in “ChargingEnv” to maximize EV service quality within limited charging resource constraints. In this study, numerical experiments were conducted to demonstrate the MED deployment decision framework’s efficacy. The findings indicate that the framework’s trained model can substantially improve EV travel range and alleviate battery depletion concerns. This could serve as a vital tool that allows public-sector road agencies or private-sector commercial entities to efficiently orchestrate MED deployments to maximize service cost-effectiveness.</p>

This thesis integrates scoring, gamification, and Corporate Sustainability Reporting(CSR) to encourage sustainable behaviours through electronic driving journals. As environmental stewardship becomes increasingly important, the European Union's Corporate Sustainability Reporting Directive (CSRD) mandates greater transparency in reporting environmental, social, and governance (ESG) activities. In this context, driving journals in Sweden, primarily used to distinguish between professional and personal vehicle use, emerge as a critical factor in influencing sustainable practices. The study explores the potential of scoring and gamification, which involves incorporating game-design elements into non-game contexts to transform routine compliance activities into engaging processes that promote sustainable behaviour. By combining these journals with the requirements of the CSRD, the research examines their effectiveness in fostering an environmentally conscious driving culture among corporate fleets and individual users. The study employs a multidimensional approach, which involves conducting a comprehensive literature review, analysing the market, and practically applying the findings with Northtracker, a leading driving journal provider. The goal is to examine how aligning Northtracker's systems with CSRD guidelines can help promote and comply with sustainable practices by enhancing user engagement. The results indicate that incorporating scoring and gamified elements within driving journals can effectively increase user participation and compliance, leading to a measurable reduction in environmental impact resulting from improved driving habits. Nonetheless, the study highlights several challenges, such as privacy concerns, data accuracy, and user acceptance, which require a balanced approach to scoring and gamification in sustainability initiatives. The thesis concludes by recommending strategic actions that policymakers and corporations can take to exploit this integration's potential fully. These implications go beyond the immediate scope of driving journals and offer valuable insights into the broader application of scoring and gamification in environmental sustainability efforts.

No description available.

Using questionnaire survey generated data from the DWI Probation Program in Dallas County. This study investigated the factors related to the perceived effectiveness of that program from the probationers perspectives. The findings in this study indicate that the perceived effectiveness of the DWI program by the probationers is an area that calls for more research and investigations. The findings have shown that factors related to the perceived effectiveness of the program by the probationers have a profound effect on the efficiency of the program as a whole in order to achieve its stated objectives.

Performing multiple tasks simultaneously is proposed to have an influence on the amount of mental resources available for attending to incoming stimuli’s. Concentration is presumed to be divided between focussing on driving (incoming visual information) while attending to incoming auditory information. The study aimed to investigate the influence of the tempo of music on concentration and driving ability by means of simulation. Concentration was measured by driving errors (DE) whereas driving ability was measured by lap-times (LT) and elicited behaviour. Four treatment conditions were utilised; that is a no-music (NM) control condition, low tempo music (LTM)-, medium tempo music (MTM)- and high tempo music (HTM) treatment conditions. Results found that the tempo of music does not have an influence on concentration; however, significant results were obtained indicating that the tempo of music does have an influence on driving behaviour. / Psychology / M.A. Soc. Sc. (Psychology)

The development of driver assistance and automated vehicle control is in process and finds its way more and more into urban traffic environments. Here, the complexity of traffic situations is highly challenging and requires system approaches to comprehend such situations. The key element is the process of situation assessment to identify critical situations in advance and derive adequate warning and intervention strategies. This thesis introduces a system approach to establish a situation assessment process with the focus on the prediction of the driver intention. The system design is based on the Situation Awareness model by Endsley. Further, a prediction algorithm is created using Hidden Markov Models. To define the parameters of the models, an existing database is used and previously analyzed to identify reasonable variables that indicate an intended driving direction while approaching the intersection. Here, vehicle dynamics are used instead of driver inputs to enable a further extension of the prediction, i.e.\\ to predict the driving intention of other vehicles detected by sensors. High prediction rates at temporal distances of several seconds before entering the intersection are accomplished. The prediction is integrated in a system for situation assessment including an intersection model. A Matlab tool is created with an interface to the vehicle CAN bus and the intersection modeling which uses digital map data to establish a representation of the intersection. To identify differences and similarities in the process of approaching an intersection dependent on the intersection shape and regulation, a naturalistic driving study is conducted. Here, the distance to the intersection and velocity is observed on driver inputs related to the upcoming intersection (leaving the gas pedal, pushing the brake, using the turn signal). The findings are used to determine separate prediction models dependent on shape and regulation of the upcoming intersection. The system runs in real-time and is tested in a real traffic environment. / Die Entwicklung von Fahrerassistenz und automatisiertem Fahren ist in vollem Gange und entwickelt sich zunehmend in Richtung urbanen Verkehrsraum. Hier stellen besonders komplexe Verkehrssituationen sowohl für den Fahrer als auch für Assistenzsysteme eine Herausforderung dar. Zur Bewältigung dieser Situationen sind neue Systemansätze notwendig, die eine Situationsanalyse und -bewertung beinhalten. Dieser Prozess der Situationseinschätzung ist der Schlüssel zum Erkennen von kritischen Situationen und daraus abgeleiteten Warnungs- und Eingriffsstrategien. Diese Arbeit stellt einen Systemansatz vor, welcher den Prozess der Situationseinschätzung abbildet mit einem Fokus auf die Prädiktion der Fahrerintention. Das Systemdesign basiert dabei auf dem Situation Awareness Model von Endsley. Der Prädiktionsalgorithmus ist mit Hilfe von Hidden Markov Modellen umgesetzt. Zur Bestimmung der Modellparameter wurde eine existierende Datenbasis genutzt und zur Bestimmung von relevanten Variablen für die Prädiktion der Fahrtrichtung während der Kreuzungsannäherung analysiert. Dabei wurden Daten zur Fahrdynamik ausgewählt anstelle von Fahrereingaben um die Prädiktion später auf externe Fahrzeuge mittels Sensorinformationen zu erweitern. Es wurden hohe Prädiktionsraten bei zeitlichen Abständen von mehreren Sekunden bis zum Kreuzungseintritt erzielt. Die Prädiktion wurde in das System zur Situationseinschätzung integriert. Weiterhin beinhaltet das System eine statische Kreuzungsmodellierung. Dabei werden digitale Kartendaten genutzt um eine Repräsentation der Kreuzung und ihrer statischen Attribute zu erzeugen und die der Kreuzungsform entsprechenden Prädiktionsmodelle auszuwählen. Das Gesamtsystem ist als Matlab Tool mit einer Schnittstelle zum CAN Bus implementiert. Weiterhin wurde eine Fahrstudie zum natürlichen Fahrverhalten durchgeführt um mögliche Unterschiede und Gemeinsamkeiten bei der Annäherung an Kreuzungen in Abhängigkeit der Form und Regulierung zu identifizieren. Hierbei wurde die Distanz zur Kreuzung und die Geschwindigkeit bei Fahrereingaben im Bezug zur folgenden Kreuzung gemessen (Gaspedalverlassen, Bremspedalbetätigung, Blinkeraktivierung). Die Ergebnisse der Studie wurden genutzt um die Notwendigkeit verschiedener Prädiktionsmodelle in Abhängigkeit von Form der Kreuzung zu bestimmen. Das System läuft in Echtzeit und wurde im realen Straßenverkehr getestet.