In the last couple of decades, there has been an unparalleled growth in number of people who can afford motorized vehicles. This is increasing the number of vehicles on roads at an alarming rate and existing infrastructure and conventional methods of traffic management are becoming inefficient both on highways and in urban areas. It is very important that our highways are up and running 24/7 as they not only provide a passage for human beings to move from one place to another, but also are the most important mode for intercity or international transfer of goods. There is an utter need of adapting the new world order, where daily processes are driven with the help of innovative technologies. It is highly likely that technological advancements like autonomous or connected vehicles, big data and the Internet of things can provide highway operators with a solution that might resolve unforeseeable challenges. This investigative exploratory research identifies and highlights the impact of new technological advancements in the automotive industry on highways and highway operators. The data for this research was collected on a Likert scale type online survey, from different organizations around the world (actively or passively involved in highway operations). The data was further tested for its empirical significance with non-parametric binomial and Wilcoxon signed rank tests, supported by a descriptive analysis. The results of this study are in line with theoretical and conceptual work done by several independent corporations and academic researchers. It is evident form the opinions of seasoned professionals that these technological advancements withhold the potential to resolve all potential challenges and revolutionize highway operations.

No description available.

A big challenge regarding the development of autonomous vehicles is how to handle complex situations. If an autonomous vehicle ends up in a situation where it cannot make a decision on its own it will cause the car to stop, unable to continue driving. For these situations, human intervention is required. By making it possible to control the car remotely there is no need for an actual human in the car. Instead, a human operator can remotely control one or several cars from a distance. The purpose of this project was to identify such complex situations, evaluate remote control options and implement one of these controllers to drive the SVEA cars in the Smart Mobility Lab. After evaluation of possible remote control options, the PlayStation controller was chosen to be the simplest and most intuitive steering option. The controller was successfully implemented first in simulation and then on the SVEA cars in the Smart Mobility Lab. A test track was designed to measure the performance of the implemented controller and to be able to measure user-friendliness through a survey. It was concluded that a majority of the participants would not feel comfortable steering a real car using the PlayStation controller. However, a more extensive evaluation would be required to draw any major conclusions.

Intersections represents one of the most common accident sites in traffic today. The biggest cause of accidents is obstructed view and subpar communication between vehicles. Since autonomous vehicles rely on sensors that require a direct view intersections are some of the most complex situations. Where the potential for inter vehicular communication exists between modern vehicles, it is absent in the older generation. An overseeing intersection system can fill this function during the transition period to fully autonomous traffic. This project aimed to implement an intersection system to assist autonomous vehicles through a crossroad. The assist system’s objective was to collect and transmit data from cars close to the junction to the autonomous vehicles nearby. The concept was tested in simulations by having models traverse a crossroad to evaluate how it utilised the external information. No persistent conclusion could be made due to insufficient simulation environment and vehicle model control.

Utvecklingen med automatiserade funktioner i fordon går framåt och i framtiden är det möjligt att bilen är helt självkörande. Stockholm växer och även trafiken vilket medför utmaningar att lösa som exempelvis att öka framkomligheten, tillgänglighet och hållbarhet. Därför har Stockholms stad tagit fram mål för tydliggöra vad det framtida gaturummen ska uppfylla. Syftet med denna rapport var att sammanställa information om hur autonoma fordon kan påverka gatuutformningen samt jämföra resultatet med målen om gatuplanering i Stockholms stad. Resultatet visade att autonoma fordon kan möjliggöra en ökad delningsekonomi inom transportsystemet där allt färre människor äger ett eget fordon. I stället kan fordonen ingå i nya mobilitetslösningar. Detta kan medföra en förbättrad markanvändning och nya möjligheter till hur vi använder gaturummen. Exempelvis nämns gaturummet kunna stängas av för trafik efter rusningstid och ge plats för andra aktiviteter. Resultatet visade även att fordonenen har behov för mer integrerad teknik i gaturummen samt krav på högre läsbarhet av linjemarkeringar och vägmärken. En slutsats som kunde dras var att många utav Stockholms stads mål kan uppnås, exempelvis miljömål, mindre trängsel och högre trafiksäkerhet. Av resultatet framgår det att de två viktigaste faktorerna är den politiska och tekniska utveckling kring autonoma fordon. / The development with automated functions in vehicles is moving forward and in the future it is possible that cars is completely self-driving. Stockholm is growing and also the traffic, which poses challenges to solve, such as increasing accessibility, availability and sustainability. Therefore, the City of Stockholm has developed goals for clarifying what the future street areas should fulfill. The purpose of this report was to compile information on how autonomous vehicles can influence street design and compare the results with the goals of street planning in the City of Stockholm. The result showed that autonomous vehicles can enable an increased sharing economy within the transport system, which means that fewer people own their own vehicle. Instead, the vehicles can be included in new mobility solutions. This can lead to improved land use and new opportunities for how we use the street space. For example, the street room is mentioned as being able to be switched off for traffic after rush hour and providing space for other activities. The results also showed that the vehicles need more integrated technology in the street space and requirements for higher readability of line markings and road signs. One conclusion was that many of Stockholm's goals can be achieved, such as environmental goals, less congestion and higher road safety. The result shows that the two most important factors are the political and technical development of autonomous vehicles.

Autonomous vehicles are complex, technologically opaque, and can vary greatly in what perceptual capabilities they are endowed with. Because of this, it is reasonable to expect people to have difficulties in accurately inferring what an autonomous vehicle has and has not seen, and also how they will act, in a traffic situation. To facilitate effective interaction in traffic, autonomous vehicles should therefore be developed with people’s assumptions in mind, and design efforts should be made to communicate the vehicles' relevant perceptual beliefs. For such efforts to be effective however, they need to be grounded in empirical data of what assumptions people make about autonomous vehicles' perceptual capabilities. Using a novel method, the present study aims to contribute to this by investigating how people's understanding of the visual capabilities of autonomous vehicles compare to their understanding of those of human drivers with respect to (Q1) what the vehicle/driver can and cannot see in various traffic situations, (Q2) how certain they are of Q1, and (Q3) the level of agreement in their judgement of Q1. Additionally, we examine whether (Q4) there is a correlation between individual differences in anthropomorphizing and Q1. The results indicate that people generally believe autonomous vehicles and human drivers have the same perceptual capabilities, and that they therefore are subject to similar limitations. The results also indicate that people are equally certain of their beliefs in both cases, strongly agree in both cases, and that individual differences in anthropomorphizing are not associated with these beliefs. Implications for development of autonomous vehicles and future research are discussed.

Autonomous vehicles are regularly predicted to be on the verge of broad integration into regular traffic. A crucial aspect of successful traffic interactions is one agent’s ability to adequately understand other agents’ capabilities and limitations. Within the current state of the art concerning self-driving cars, there is a discrepancy between what people tend to believe the capabilities of self-driving cars are, and what those capabilities actually are. The aim of this study was to investigate whether people attribute the capacity of object permanence to self-driving cars roughly in the same manner as they would to a human driver. The study was conducted with online participants (N = 105). The results showed that the participants did not attribute object permanence differently between a self-driven car and a human driver. This indicates that people attribute object permanence similarly to self-driving cars as they do toward human drivers. Furthermore, the results indicate no connection between participants’ tendency to anthropomorphize and whether they attributed object permanence or not. The findings provide evidence for the issues connected to the perceptual belief problem in human-robot interaction, where people attribute capabilities to autonomous vehicles that are not there. The results highlight the importance of understanding which mechanisms underlie these attributions as well as when they happen, in order to mitigate unrealistic expectations.

Artificiell intelligens är en form av teknik som blir vanligare inom samhället. I takt med att tekniken utvecklas blir även diskussionen inom området mer utvecklad vilket resulterat i att eventuella problem och möjligheter blivit mer tillgänglig information. Det finns en avsaknad av tankar och förväntningar från privatpersoners synvinkel inom ämnet vilket kan ses som negativt då de förväntas vara en majoritet av användarbasen för tekniken. Eftersom denna typ av teknik förutspås ta över ett större ansvar av mänskliga uppgifter är det viktigt att klarlägga olika typer av tillvägagångssätt samt utvecklingsperspektiv i syfte att skapa ett hälsosamt och välfungerande AI-system till respektive områden. Studien syftar till att belysa bidragande faktorer till attityder och åsikter relaterade specifikt till AI-styrda fordon ur privatpersoners perspektiv samt hur dessa kan påverka en eventuell implementering och använder sig av en kvalitativ metod. Den data som används inom arbetet har samlats in via semistrukturerade intervjuer med personer som anmält att de vill delta i studien. Analysen genomförs baserat på innovationsspridningsteorin (IDT) och relevant tidigare forskning för att undersöka vad som påverkar användare att adoptera tekniken eller inte. Faktorer som identifierades vara påverkande för adoptionsprocessen var oro över att tekniken inte skulle fungera på ett kompatibelt sätt med mänskliga värderingar, ett krav på utförlig testning samt möjligheten till att reducera olyckor eller klimatpåverkan relaterat till trafik. Utifrån dessa faktorer härleddes förslag till implementeringsprocesser vilket bestod av expanderande statligt kontrollerad testning inom kollektivtrafiken, tydligt klarlagda strukturella regler och avgränsningar samt ett främjande av de positiva faktorer som möjliggörs av AI-styrda fordon. Detta främjande kan genomföras med en effektiv kommunikation som drar nytta av vår bristfällliga rationella beslutsprocess och använder starka känslomässiga intryck. / Artificial intelligence is a form of technology that is becoming increasingly more common within society. As the technology evolves, the discussion within the subject is also increasing which has made information about eventual problems and possibilities more public. There is a shortage of thoughts and expectations from the private individual’s point of view regarding this topic which can be a negative thing due to this group being expected to make up the majority of the technology’s user base. Because this type of technology is predicted to take on a larger responsibility of human tasks it is important to clarify different approaches and development perspectives in order to create a healthy and well-functioning AI-system within respective areas. The study intends to highlight contributing factors to attitudes and opinions specifically related to AI-controlled vehicles from the public's view as well as how these can affect an eventual implementation and is carried out with a qualitative method. The data that is used is gathered through semi-structured interviews with people that expressed interest in participating in the study. The analysis is based on the diffusion of innovations theory (IDT) and relevant earlier research in order to examine what influences users to adopt the technology or not. The factors that were identified to be affecting this process were worry that the technology would not work in a compatible way with human values, a demand for extensive testing as well as the possibility to reduce accidents or the affect on climate related to traffic. Several suggestions for implementation were derived from these factors which consisted of continuous expanded testing within public transport regulated by the state, clear structural rules and limitations as well as a promotion of the positive factors made possible by AI-controlled vehicles. This promotion can be done through effective communication which takes advantage of our flawed rational decision making and uses strong emotional impressions.

Background. This research centers on tackling the serious global problem of trafficaccidents. With more than a million deaths each year and numerous injuries, it’svital to predict and prevent these accidents. By combining satellite images and dataon accidents, this study uses a mix of advanced learning methods to build a modelthat can foresee accidents. This model aims to improve how accurately we predictaccidents and understand what causes them. Ultimately, this could lead to betterroad safety, smoother maintenance, and even benefits for self-driving cars and insurance. Objective.The objective of this thesis is to create a predictive model that improvesthe accuracy of traffic accident severity forecasts by integrating satellite imagery andhistorical accident data and comparing this model with stand-alone data models.Through this hybrid approach, the aim is to enhance prediction precision and gaindeeper insights into the underlying factors contributing to accidents, thereby potentially aiding in the reduction of accidents and their resulting impact. Method.The proposed method involves doing a literature review to find currentimage recognition models and then experimentation by training a Logistic Regression, Random Forest, SVM classifier, VGG19, and the hybrid model using the CNNand VGG19 and then comparing their performance using metrics mentioned in thethesis work. Results.The performance of the proposed method is evaluated using various metrics, including precision, recall, F1 score, and confusion matrix, on a large datasetof labeled images. The results indicate that a high accuracy of 81.7% is achieved indetecting traffic accident severity through our proposed approach where the modelbuilt on individual structural data and image data got an accuracy of 58.4% and72.5%. The potential utilization of our proposed method can detect safe and dangerous locations for accidents. Conclusion.The predictive modeling of Traffic accidents are performed using thethree different types of datasets which are structural data, satellite images, and acombination of both. The finalized architectures are an SVM classifier, VGG19, anda hybrid input model using CNN and VGG19. These models are compared in orderto find the best-performing approach. The results indicate that our hybrid modelhas the best accuracy with 81.7% indicating a strong performance by the model.

Syftet med denna studie är att undersökavilket användargränssnitt som potentiella användare föredrar för att utbyta trafikrelateradinformation. Forskningsfrågan som ska besvaras är följande. Vilket användargränssnittföredras för kommunikation i ett självkörande fordon? Genom att läsa denna studie fårläsaren en fördjupad insikt för hur föredragna användargränssnitt kan öka acceptansen hospotentiella användare. En kvantitativ metod användes för att genomföra enstickprovsundersökning med hjälp av en webbaserad enkät som distribuerades på olika sättsom Facebook, Linkedin, m.m, för att besvara studiens syfte. Den empiriskadatainsamlingen resulterade i 201 insamlade svar. Resultatet visade att 41,3 % avrespondenterna föredrog skärmgränssnitt och 35,3% föredrog ett multimodalt gränssnitt föratt integrera med ett självkörande fordon. Sammanlagt 84,1% av respondenterna besvaradeatt användningen av det önskade gränssnittet skulle öka effektiviteten ochkommunikationen vid utbyte av information med fordonet. Slutsatsen är att valet avanvändargränssnitt kan påverkas av olika faktorer, såsom erfarenheter och teknologiskaförväntningar. Framtida utveckling av gränssnitt och teknologier bör sträva efter attinkludera en mångfald av alternativ för att tillgodose användarnas behov och preferensernär det gäller att kommunicera med fordon. / Syftet med denna studie är att undersöka vilket användargränssnitt som potentiella användare föredrar för att utbyta trafikrelaterad information. Forskningsfrågan som ska besvaras är följande. Vilket användargränssnitt föredras för kommunikation i ett självkörande fordon? Genom att läsa denna studie får läsaren en fördjupad insikt för hur föredragna användargränssnitt kan öka acceptansen hos potentiella användare. En kvantitativ metod användes för att genomföra en stickprovsundersökning med hjälp av en webbaserad enkät som distribuerades på olika sätt som Facebook, Linkedin, m.m, för att besvara studiens syfte. Den empiriska datainsamlingen resulterade i 201 insamlade svar. Resultatet visade att 41,3 % av respondenterna föredrog skärmgränssnitt och 35,3% föredrog ett multimodalt gränssnitt för att integrera med ett självkörande fordon. Sammanlagt 84,1% av respondenterna besvarade att användningen av det önskade gränssnittet skulle öka effektiviteten och kommunikationen vid utbyte av information med fordonet. Slutsatsen är att valet av användargränssnitt kan påverkas av olika faktorer, såsom erfarenheter och teknologiska förväntningar. Framtida utveckling av gränssnitt och teknologier bör sträva efter att inkludera en mångfald av alternativ för att tillgodose användarnas behov och preferenser när det gäller att kommunicera med fordon.