The purpose of this bachelor thesis was to discover how an articulated vehicle can park itself using a pre-defined parking path with a combination of ultrasonic sensors as well as a rotary angle sensor. The project was divided into two parts: constructing a small scale demonstrator and the software controlling the demonstrator. The demonstrator was constructed from offthe- shelf components and custom parts. The truck was designed based on a rear wheel driven truck with Ackermann steering. The localization of a parking spot and measuring other distances was done with ultrasonic sensors and the hitch angle was measured by a rotary angle sensor. The performance of the demonstrator was evaluated by measuring the trailers angle difference from the center line of the parking spot. The performance was deemed to be reasonably good with successful parkings in 8 out of 10 attempts. / Kandidatarbetet syftar till att undersöka hur ett ledat fordon kan parkera sig självt efter en förbestämd parkeringsrutt med en kombination av flera ultraljudssensorer samt en vinkelgivare. Projektet består av två delar; konstruktion av ett miniatyrfordon samt mjukvaran som styr fordonet. Fordonet tillverkades från butiksköpta komponenter och skräddarsydda delar. Lastbilens design var baserad på en bakhjulsdriven Ackermannstyrd lastbil. Identifieringen av en parkeringsplats samt avståndsmätning hanterades av ultraljudssensorer och hitch vinkeln mättes av en vinkelgivare. Miniatyrfordonets prestanda utvärderades genom att mäta släpets vinkelskillnad från centerlinjen av parkeringsplatsen. Prestandan ansågs att vara tillräckligt god med lyckade parkeringar i 8 av 10 tester.

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Autonomous vehicles (AVs) have become increasingly prevalent in modern society, with multiple companies pursuing avenues to advance technology. Despite the promises of safer driving, mitigating accidents and reducing stress, mixed opinions exist on the reliability and trustworthiness of the technology. While many factors form opinions regarding AVs, the component of personal values in AV discourse has not been well-documented. This research focuses on understanding which personal values are most prominent in AV discourse and how individuals prioritize these personal values. We studied two AV-focused data sources to gain perspective: 24 TED Talk transcripts and 20,000 Reddit user posts. Next, we scored the prevalence of personal values using the Personal Values Dictionary (PVD). Our results found that self-direction, achievement, power, stimulation, and conformity had an overall positive inclination in AV discourse. However, we also found value conflicts between both data sets, indicating a potential dissonance between professional opinions and those of typical consumers. Our findings provide insight into values literature and issues consumers bring up when discussing or adopting AVs. Our research on value conflicts can also link to consumers' potential adoption of AVs. Thus, we provide potentially useful perspectives for companies to address these concerns directly in their AV production, advertising, and design philosophies.

Automated vehicles are those which are capable of sensing their environments in order to perform at least some aspects of the safety-critical control (like steering, throttling, or braking) without direct human input. As a guide for planners and policymakers, the objective of this thesis is to develop a strong foundation for anticipating the potential impacts resulting from advancements in vehicle automation. To establish the foundation, this thesis uses a robust qualitative methodology, coupling a review of literature on the potential advantages and disadvantages of vehicle automation and lessons from past innovations in transportation, with recent trends of the Millennial Generation, carsharing services, and a series of interviews with thought-leaders in automation, planning, policymaking, transportation, and aviation. Five significant findings emerged from this thesis: (1) the impacts of vehicle automation differ depending on one’s visions of what automation means, how it is implemented, what the automation does, and where it operates; (2) current limitations of vehicle automation to perform all aspects of the dynamic driving task in all driving conditions make it difficult to move from level-4 to level-5 automation; (3) level-5 automation is required to have any effect on carsharing, mobility, and quality of life; (4) assuming effective planning and policymaking techniques, housing preferences, urban growth, and increases in total VMT will likely not be significantly impacted by vehicle automation; (5) human drivers may never be allowed to disengage their attention from a partially-automated vehicle, specifically in applications where drivers are expected to reengage their attention in safety-critical situations. From the perspective of understanding the bigger picture, this thesis developed a proposed future scenario of vehicle automation in the next five to ten years that is used to suggest guiding principles for policymakers, and key recommendations for planners, engineers, and researchers.

Den här studien använder sig av TORCS (The Open Racing Car Simulator) som är ett intressant spel att skapa självkörande bilar i då det finns nitton olika typer av sensorer som beskriver omgivningen för agenten. Problemet för denna studie har varit att identifiera vilka av alla dessa sensorer som kan användas i en belöningsfunktion och hur denna sedan skall implementeras. Studien har anammat en kvantitativa experimentell studie där forskningsfrågan är: Hur kan en belöningsfunktion utformas så att agenten klarar av att manövrera i spelet TORCS utan att krocka och med ett konsekvent resultat Den kvantitativ experimentell studien valdes då författarna behövde designa, implementera, utföra experiment och utvärdera resultatet för respektive belöningsfunktion. Det har utförts totalt femton experiment över tolv olika belöningsfunktioner i spelet TORCS på två olika banor E-Track 5(E-5) och Aalborg. De tolv belöningsfunktionerna utförde varsitt experiment på E-5 där de tre som fick bäst resultat: Charlie, Foxtrot och Juliette utförde ett experiment på Aalborg, då denna är en svårare bana. Detta för att kunna styrka om den kan köra på mer än en bana och om belöningsfunktionen då är generell. Juliette är den belöningsfunktion som var ensam med att klara både E-5 och Aalborg utan att krocka. Genom de utförda experimenten drogs slutsatsen att Juliette uppfyller forskningsfrågan då den klarar bägge banorna utan att krocka och när den lyckas får den ett konsekvent resultat. Studien har därför lyckats designa och implementera en belöningsfunktion som uppfyller forskningsfrågan. / For this study TORCS (The Open Racing Car Simulator) have been used, since it is an interesting game to create self-driving cars in. This is due to the fact there is nineteen different sensors available that describes the environment for the agent. The problem for this study has been to identify what sensor can be used in a reward function and how should this reward function be implemented. The study have been utilizing a quantitative experimental method where the research questions have been: How can a reward function be designed so that an Agent can maneuver in TORCS without crashing and at the same time have a consistent result The quantitative experimental method was picked since the writer’s hade to design, implement, conduct experiment and evaluate the result for each reward function. Fifteen experiments have been conducted over twelve reward functions on two different maps: E-Track 5 (E-5) and Aalborg. Each of the twelve reward function conducted an experiment on E-5, where the three once with the best result: Charlie, Foxtrot and Juliette conducted an additional experiment on Aalborg. The test on Aalborg was conducted in order to prove if the reward function can maneuver on more than one map. Juliette was the only reward function that managed to complete a lap on both E-5 and Aalborg without crashing. Based on the conducted experiment the conclusion that Juliette fulfills the research question was made, due to it being capable of completing both maps without crashing and if it succeeded it gets a consistent result. Therefor this study has succeeded in answering the research question.

Self-driving cars are here. Once an advanced technology that seemed futuristic, they are now closer than most believe. Many of the largest automobile manufacturers are working on autonomous vehicle technology of their own. Perhaps most well-known, though, are the cars being developed by Tesla and Google. Both companies have well-developed prototypes of fully autonomous vehicles, meaning they require no human input or supervision, and Tesla has promised widespread, consumer availability of this technology in the next one to two years. Along with the availability of this technology to the public and transportation companies like Uber and Lyft, comes a need to establish a regulatory environment. Regulators need to contemplate a new, yet complex, technology with far-reaching implications and determine how best to regulate necessary components. In this paper, I plan to explicate and analyze the ethical impact of the proliferation of self-driving cars that regulators should consider when determining how they ought to regulate. I will do this by first clarifying any technical terms one might need to be familiar with as well as discussing some of the requisite considerations. Then, I plan to explore a some of the pitfalls regulators might be subject to as they navigate the associated complex issues. Finally, I will explain and analyze the likely benefits and potential risks resulting from roadways filled with autonomous vehicles.

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.

In recent years, the level of technology in heavy duty vehicles has increased significantly. Progress has been made towards autonomous driving, with increaseddriver comfort and safety, partly by use of advanced driver assistance systems (ADAS). In this thesis the possibilities to detect and predict lane changes for the preceding vehicle are studied. This important information will help to improve the decision-making for safety systems. Some suitable approaches to solving the problem are presented, along with an evaluation of their related accuracies. The modelling of human perceptions and actions is a challenging task. Several thousand kilometers of driving data was available, and a reasonable course of action was to let the system learn from this off-line. For the thesis it was therefore decided to review the possibility to utilize a branch within the area of artificial intelligence, called supervised learning. The study of driving intentions was formulatedas a binary classification problem. To distinguish between lane-change and lane-keep actions, four machine learning-techniques were evaluated, namely naive Bayes, artificial neural networks, support vector machines and Gaussian processes. As input to the classifiers, fused sensor signals from today commercially accessible systems in Scania vehicles were used. The project was carried out within the boundaries of a Master’s Thesis projectin collaboration between Linköping University and Scania CV AB. Scania CV AB is a leading manufacturer of heavy trucks, buses and coaches, alongside industrialand marine engines.

Sensors, as a crucial part of autonomous driving, are primarily used for perceiving the environment. The recent deep learning development of different sensors has demonstrated the ability of machines recognizing and understanding their surroundings. Automotive radar, as a primary sensor for self-driving vehicles, is well-known for its robustness against variable lighting and weather conditions. Compared with camera-based deep learning development, Object detection using automotive radars has not been explored to its full extent. This can be attributed to the lack of public radar datasets. In this thesis, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-EyeView range map. To build the dataset, we propose an instance-wise auto-annotation algorithm. Furthermore, a novel Range-Azimuth-Doppler based multi-class object detection deep learning model is proposed. The algorithm is a one-stage anchor-based detector that generates both 3D bounding boxes and 2D bounding boxes on Range-AzimuthDoppler and Cartesian domains, respectively. Our proposed algorithm achieves 56.3% AP with IOU of 0.3 on 3D bounding box predictions, and 51.6% with IOU of 0.5 on 2D bounding box predictions. Our dataset and the code can be found at https://github.com/ZhangAoCanada/RADDet.git.

The diploma thesis âImpact of modern technology in logistics and their role in companyâ is focused to analysis impact of modern technology in logistics and their role in company DHL. The goal of this thesis is analysis of use self-driving vehicles on linehauls in company DHL that company will keep their leader role in the market and show other companies technological future in logistics.