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Personal Value Priorities in Autonomous Vehicle DiscourseTomas, Patrick 09 November 2022 (has links)
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.
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Möjligheter för automatiserade godstransporter I Västerbotten : Vilken påverkan har väginfrastruktur för potentialen till automatiserade transportsystem i region Västerbotten?Törnell, Axel January 2021 (has links)
The technology behind self-driving trucks in currently under development for deployment on public road. The objective of this study is to explore and understand the number of industries that could be reached by self-driving trucks with the limits of Västerbottens current road network. The effect from implementing self-driving trucks is an emerging research field. We do not understand to what extent the physical infrastructure affect which industries may be able to use self-driving trucks since there is a lack of exploration and research within the scientific literature. The thesis has been conducted by a literature study, interviews, and a spatial analysis. The spatial analysis examined the potential for self-driving trucks to access industries depending on what road infrastructure self-driving trucks are assumed to be able to operate. The number of industries that self-driving trucks can access was calculated for four different subsets of the road networks: The European highway network, National highway network, Functional priority road network and roads with driving lane with over 5m. This resulted in the conclusion that the European highway network had the lowest number of industries within the search radiuses, a considerate amount of all industries (49%) was still within a 1 km radius. The road type “Driving lane with over 5m” having a clear majority of the industries with a total of 94% of the industries within a 1 km radius. The findings of this study suggest that self-driving trucks which are capable of operating only at a limited part of the road network still potentially could be used for a relatively large number of industrial freight transport flows in Västerbotten. This could indicate that self-driving trucks with a limited operating design domain could address a substantial share of the freight transport market.
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A study on lane detection methods for autonomous drivingCudrano, Paolo January 2019 (has links)
Machine perception is a key element for the research on autonomous driving vehicles. In particular, we focus on the problem of lane detection with a single camera. Many lane detection systems have been developed and many algorithms have been published over the years. However, while they are already commercially available to deliver lane departure warnings, their reliability is still unsatisfactory for fully autonomous scenarios.
In this work, we questioned the reasons for such limitations. After examining the state of the art and the relevant literature, we identified the key methodologies adopted. We present a self-standing discussion of bird’s eye view (BEV) warping and common image preprocessing techniques, followed by gradient-based and color-based feature extraction and selection. Line fitting algorithms are then described, including least squares methods, Hough transform and random sample consensus (RANSAC). Polynomial and spline models are considered. As a result, a general processing pipeline emerged. We further analyzed each key technique by implementing it and performing experiments using data we previously collected. At the end of our evaluation, we designed and developed an overall system, finally studying its behavior.
This analysis allowed us on one hand to gain insight into the reasons holding back present systems, and on the other to propose future developments in those directions. / Thesis / Master of Science (MSc)
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Decision Making in Alternative Modes of Transportation: Two Essays on Ridesharing and Self-Driving VehiclesAmirkiaee, Seyede Yasaman 05 1900 (has links)
This manuscript includes an investigation of decision making in alternative modes of transportation in order to understand consumers' decision in different contexts. In essay 1 of this study, the motives for participation in situated ridesharing is investigated. The study proposes a theoretical model that includes economic benefits, time benefits, transportation anxiety, trust, and reciprocity either as direct antecedents of ridesharing participation intention, or mediated through attitude towards ridesharing. Essay 2 of this study, focuses on self-driving vehicles as one of the recent innovations in transportation industry. Using a survey approach, the study develops a conceptual model of consumers' anticipated motives. Both essays use partial least square- structural equation modeling for assessing the proposed theoretical models.
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Optimal False Data Injection (FDI) In Simulated Cooperative Adaptive Cruise Control (CACC) SystemsDukic, Lovro 01 June 2024 (has links) (PDF)
In the rapidly advancing field of autonomous vehicles, ensuring the security and reliability of self-driving systems is crucial. Autonomous vehicle systems, such as cooperative adaptive cruise control (CACC), must undergo significant research and testing before their integration into commercial intelligent transportation systems. CACC considers multiple vehicles in close proximity as a single entity, or platoon, with each vehicle equipped with a controller that uses sensor-based measurements and vehicle-to-vehicle (V2V) communication to control inter-vehicle spacing. While this system offers numerous potential benefits for traffic safety and efficiency, it is also susceptible to False Data Injection (FDI) attacks, which can cause the system to behave in potentially life-threatening ways. Testing these scenarios in the real world is infeasible due to expense, safety concerns, and the use of theoretical technologies.
This study presents an implementation of a vehicle platoon in a simulated environment where the vehicles' controllers were tuned to maintain desired inter-vehicle spacing. Various FDI signals were then implemented to demonstrate the feasibility of malicious attacks, including a novel parameterized sinusoidal FDI signal. Furthermore, acknowledging the necessity for future anomaly detection schemes and noise filtration, a theoretical optimal attack—generated using a model of the sinusoidal FDI attack and identification of optimal FDI values—was also evaluated.
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Designförslag på belöningsfunktioner för självkörande bilar i TORCS som inte krockar / Design suggestion on reward functions for self-driving cars in TORCS that do not crashAndersson, Björn, Eriksson, Felix January 2018 (has links)
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.
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Ethical Considerations Facing the Regulation of Self-Driving Cars in the United StatesMancuso, Richard 01 January 2016 (has links)
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.
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Future Outlook of Highway Operations with Implementation of Innovative Technologies Like AV, CV, IoT and Big DataAzmat, Muhammad, Kummer, Sebastian, T. Moura, Lara, Di Gennaro, Federico, Moser, Rene January 2019 (has links) (PDF)
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.
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Lane Change Intent Analysis for Preceding Vehicles : a Study Using Various Machine Learning Techniques / Analys av framförvarande fordons filbytesintentioner : En studie utnyttjande koncept från maskininlärningFredrik, Ljungberg January 2017 (has links)
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.
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Object Detection from FMCW Radar Using Deep LearningZhang, Ao 10 August 2021 (has links)
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.
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