Asset managers continue to prepare physical infrastructure investments needed to accommodate the emerging technologies, namely vehicle connectivity, electrification, and automation. The provision of new infrastructure and modification of existing infrastructure is expected to incur a significant amount of capital investment. Secondly, with increasing EV and CAV operations, the revenues typically earned from vehicle registrations and fuel tax are expected to change due to changing demand for vehicle ownership and amount of travel, respectively. This research estimated (i) the changes in highway expenditures in an era of ECAV operations, (ii) the net change in highway revenues that can be expected to arise from ECAV operations, and (iii) the changes in user equity across the highway user groups (vehicle classes). In assessing the changes in highway expenditures, the research developed a model to predict the cost of highway infrastructure stewardship based on current and/ or future system usage. <div><br></div><div>The results of the research reveal that CAVs are expected to significantly change the travel patterns, leading to increased system usage which in turn results in increased wear and tear on highway infrastructure. This, with the need for new infrastructure to support and accommodate the new technologies is expected to result in increased highway expenditure. At the same time, CAVs are expected to have significantly improved fuel economy as compared to their human driven counterparts, leading to a decrease in fuel consumption per vehicle, resulting in reduced fuel revenues. Furthermore, the prominence of EVs is expected to exacerbate this problem. This thesis proposed a revision to the current user fee structure to address these impacts. This revision contains two major parts designed to address the system efficiency and equity in the near and long term. For the near term, this thesis recommended a variable tax scheme under which each vehicle class pays a different fuel tax rate. This ensures that both equity and system efficiency are improved during the transition to ECAV. In the long term, this thesis recommended supplementing the fuel tax with a distance based VMT tax, applicable to electric vehicles.<br></div>

What are pedestrian attitudes towards driverless vehicles that have no human driver? In this paper, we use virtual reality to simulate a virtual scene where pedestrians interact with driverless vehicles. This was an exploratory study where 15 users encounter a driverless vehicle at a crosswalk in the virtual scene. Data was collected in the form of video and audio recordings, semi-structured interviews and participant sketches to explain the crosswalk scenes they experience. An interaction design framework for vehicle-pedestrian interaction in an autonomous vehicle has been suggested which can be used to design and model driverless vehicle behaviour before the autonomous vehicle technology is deployed widely. / Vad är fotgängares inställning till förare utan fordon som inte har någon mänsklig förare? I det här dokumentet använder vi virtuell verklighet för att simulera en virtuell scen där fotgängare interagerar med förare utan bil. Det här var en undersökande studie där 15 användare möter ett förarefritt fordon vid en korsning i den virtuella scenen. Uppgifterna samlades i form av video- och ljudinspelningar, halvstrukturerade intervjuer och deltagarskisser för att förklara de övergripande scenerna de upplever. En ram för interaktionsdesign för fordonets fotgängarinteraktion i ett autonomt fordon har föreslagits, vilket kan användas för att utforma och modellera körlösa fordonsbeteenden innan den autonoma fordonstekniken används brett.

This study explores the application of multi-agent reinforcement learning (MARL) to enhance the decision-making, safety, and passenger comfort of Autonomous Vehicles (AVs)at uncontrolled intersections. The research aims to assess the potential of MARL in modeling multiple agents interacting within a shared environment, reflecting real-world situations where AVs interact with multiple actors. The findings suggest that AVs trained using aMARL approach with global experiences can better navigate intersection scenarios than AVs trained on local (individual) experiences. This capability is a critical precursor to achieving Level 5 autonomy, where vehicles are expected to manage all aspects of the driving task under all conditions. The research contributes to the ongoing discourse on enhancing autonomous vehicle technology through multi-agent reinforcement learning and informs the development of sophisticated training methodologies for autonomous driving.