Academic literature on the topic 'Autonomous Driving'

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Journal articles on the topic "Autonomous Driving"

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Poledna, S., F. Eichler, and P. Schöggl. "Autonomous Driving." Sonderprojekte ATZ/MTZ 24, S1 (2019): 47. http://dx.doi.org/10.1007/s41491-019-0029-8.

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Walch, Marcel, Kristin Mühl, Martin Baumann, and Michael Weber. "Autonomous Driving." International Journal of Mobile Human Computer Interaction 9, no. 2 (2017): 58–74. http://dx.doi.org/10.4018/ijmhci.2017040104.

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Autonomous vehicles will need de-escalation strategies to compensate when reaching system limitations. Car-driver handovers can be considered one possible method to deal with system boundaries. The authors suggest a bimodal (auditory and visual) handover assistant based on user preferences and design principles for automated systems. They conducted a driving simulator study with 30 participants to investigate the take-over performance of drivers. In particular, the authors examined the effect of different warning conditions (take-over request only with 4 and 6 seconds time budget vs. an additi
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Fuchs, Andreas. "Autonomous Driving." ATZoffhighway worldwide 11, no. 1 (2018): 3. http://dx.doi.org/10.1007/s41321-018-0013-3.

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Salow, Holger. "Autonomous driving." ATZ worldwide 110, no. 1 (2008): 14–18. http://dx.doi.org/10.1007/bf03224976.

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Han, Joong-hee, Chi-ho Park, Young Yoon Jang, Ja Duck Gu, and Chan Young Kim. "Performance Evaluation of an Autonomously Driven Agricultural Vehicle in an Orchard Environment." Sensors 22, no. 1 (2021): 114. http://dx.doi.org/10.3390/s22010114.

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To address the problems of inefficient agricultural production and labor shortages, there has been active research to develop autonomously driven agricultural machines, using advanced sensors and ICT technology. Autonomously driven speed sprayers can also reduce accidents such as the pesticide poisoning of farmers, and vehicle overturn that frequently occur during spraying work in orchards. To develop a commercial, autonomously driven speed sprayer, we developed a prototype of an autonomously driven agricultural vehicle, and conducted performance evaluations in an orchard environment. A protot
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Yang, Liangjun. "Autonomous Driving Control Strategy Based on Deep Reinforcement Learning." Applied and Computational Engineering 128, no. 1 (2025): 79–85. https://doi.org/10.54254/2755-2721/2025.20209.

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This paper discusses an autonomous driving control strategy based on Deep Reinforcement Learning (DRL), which aims to improve the decision-making ability of autonomous driving system in complex traffic environments. Deep reinforcement learning has a wide range of applications in many fields, such as robotics and medicine. Autonomous driving has emerged as a significant research focus in recent years. By combining deep learning and reinforcement learning, the model is able to autonomously learn and optimize driving behavior under dynamically changing road conditions. The DRL-based control strat
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Köster, Oliver. "Mandatory Autonomous Driving?" ATZelectronics worldwide 14, no. 3 (2019): 66. http://dx.doi.org/10.1007/s38314-019-0016-6.

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Tyler, Neil. "Safer Autonomous Driving." New Electronics 51, no. 18 (2018): 8. http://dx.doi.org/10.12968/s0047-9624(23)60679-0.

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Geng, Lichao. "Autonomous Driving Driven by Artificial Intelligence: Development Status and Future Prospects." Computers and Artificial Intelligence 2, no. 2 (2025): 29–36. https://doi.org/10.70267/cai.25v2n2.2936.

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This paper aims to explore the current status and future development trends of artificial intelligence technology in the field of autonomous driving. By analyzing the application of artificial intelligence technologies such as computer vision, deep learning and reinforcement learning in autonomous driving, this paper shows that autonomous driving is currently a hot topic in society. At present, L2 and L3 autonomous driving systems have been launched. In the future, autonomous driving may develop in the direction of vehicle‒road collaboration and L4 unmanned delivery. In addition, we still face
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STAYTON, ERIK, MELISSA CEFKIN, and JINGYI ZHANG. "Autonomous Individuals in Autonomous Vehicles: The Multiple Autonomies of Self-Driving Cars." Ethnographic Praxis in Industry Conference Proceedings 2017, no. 1 (2017): 92–110. http://dx.doi.org/10.1111/1559-8918.2017.01140.

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Dissertations / Theses on the topic "Autonomous Driving"

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Tirumaladasu, Sai Subhakar, and Shirdi Manjunath Adigarla. "Autonomous Driving: Traffic Sign Classification." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17783.

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Autonomous Driving and Advance Driver Assistance Systems (ADAS) are revolutionizing the way we drive and the future of mobility. Among ADAS, Traffic Sign Classification is an important technique which assists the driver to easily interpret traffic signs on the road. In this thesis, we used the powerful combination of Image Processing and Deep Learning to pre-process and classify the traffic signs. Recent studies in Deep Learning show us how good a Convolutional Neural Network (CNN) is for image classification and there are several state-of-the-art models with classification accuracies over 99
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Ávila, Emanuel da Silva. "Servo-pilot for autonomous driving." Master's thesis, Universidade de Aveiro, 2010. http://hdl.handle.net/10773/2537.

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Mestrado em Engenharia Mecânica<br>Foram simulados numericamente jogos de recursos públicos em redes usando algoritmo de Monte Carlo. Foram usadas redes regulares unidimensionais em anel, redes regulares bidimensionais (rede quadrada) e redes scale-free. São apresentados os métodos seguidos, a teoria e os algoritmos usados. Estes jogos apresentam uma transição de fase entre uma fase dominada por oportunistas de uma fase dominada por cooperadores em função de um parâmetro de rendimento das contribuições. Foi encontrado um intervalo, dependente do número médio de vizinhos, para o qual a fracção
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Hernández, Juárez Daniel. "Embedded 3D Reconstruction for Autonomous Driving." Doctoral thesis, Universitat Autònoma de Barcelona, 2020. http://hdl.handle.net/10803/671166.

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L’objectiu d’aquesta tesi és estudiar algoritmes de reconstrucció 3D adequats per a la conducció autònoma. Per fer-ho, necessitem implementacions i representacions ràpides de l’entorn 3D que tinguin en compte la informació geomètrica i semàntica. L’ús de paral·lelització CUDA i GPU permet aprofitar maquinari flexible i programable d’alt rendiment per complir els requisits de temps exigents. La tesi presenta tres contribucions principals. En primer lloc, descrivim la paral·lelització del conegut algorisme d’estèreo basat en el Semi-Global Matching (SGM), que estima la profunditat a partir de
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Zivkovic, A. (Aleksandar). "Development of autonomous driving using ROS." Master's thesis, University of Oulu, 2018. http://urn.fi/URN:NBN:fi:oulu-201806062488.

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Autonomous driving, or self-driving, is the ability of a vehicle to drive itself without human input. To achieve this, the vehicle uses mechanical and electronic parts, sensors, actuators and an on-board computer. The on-board computer runs sophisticated software which allows the vehicle to perceive and understand its environment based on sensor input, localise itself in that environment and plan the optimal route from point A to point B. Autonomous driving is no longer a thing of the future, and to develop autonomous driving solutions is a highly valuable skill in today’s software engineering
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Liebenwein, Lucas. "Contract-based safety verification for autonomous driving." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120366.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 77-83).<br>The safe, successful deployment of autonomous systems under real-world conditions, in part, hinges upon providing rigorous performance and safety guarantees. This thesis considers the problem of establishing and veri
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Yin, Ji. "Trajectory Planning for Off-road Autonomous Driving." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233397.

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The thesis develops a trajectory planner which operates in a formula racing scenario. The proposed trajectory planner gives time-optimal off-road trajectory planning solutions and generates sequences of control signals for the vehicle to follow the trajectory. Outputs of the trajectory planner are time-optimal trajectory, steering angle, resultant force of brake and throttle. The trajectory planner is designed to have two modes, the Exploring mode which is based on Rapidly-exploring Random Tree (RRT), and the optimization mode which is built upon optimal Rapidly-exploring random tree (RRT*). T
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Jaritz, Maximilian. "2D-3D scene understanding for autonomous driving." Thesis, Université Paris sciences et lettres, 2020. https://pastel.archives-ouvertes.fr/tel-02921424.

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Dans cette thèse, nous abordons les défis de la rareté des annotations et la fusion de données hétérogènes tels que les nuages de points 3D et images 2D. D’abord, nous adoptons une stratégie de conduite de bout en bout où un réseau de neurones est entraîné pour directement traduire l'entrée capteur (image caméra) en contrôles-commandes, ce qui rend cette approche indépendante des annotations dans le domaine visuel. Nous utilisons l’apprentissage par renforcement profond où l'algorithme apprend de la récompense, obtenue par interaction avec un simulateur réaliste. Nous proposons de nouvelles st
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Oliveira, José Ricardo Marques de. "World representation for an autonomous driving robot." Master's thesis, Universidade de Aveiro, 2009. http://hdl.handle.net/10773/2121.

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Mestrado em Engenharia de Computadores e Telemática<br>Condução autónoma constitui a deslocação de um agente, robô ou veículo, de um qualquer ponto no espaço para um outro, sem qualquer intervenção humana, por forma a atingir objectivos pré-estabelecidos. Para conduzir de forma autónoma, usando planeamento de trajectória, é crucial que o agente consiga representar abstractamente tanto o conhecimento a priori acerca do mundo, como a informação que este vai adquirindo à medida que avança. Para alcançar este propósito, desenvolveu-se um sistema para ser usado na pista da Competição de Condução Au
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Sequeira, Miguel da Rosa Carvalhal. "Perception and intelligent localization for autonomous driving." Master's thesis, Universidade de Aveiro, 2009. http://hdl.handle.net/10773/2172.

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Mestrado em Engenharia de Computadores e Telemática<br>Visão por computador e fusão sensorial são temas relativamente recentes, no entanto largamente adoptados no desenvolvimento de robôs autónomos que exigem adaptabilidade ao seu ambiente envolvente. Esta dissertação foca-se numa abordagem a estes dois temas para alcançar percepção no contexto de condução autónoma. O uso de câmaras para atingir este fim é um processo bastante complexo. Ao contrário dos meios sensoriais clássicos que fornecem sempre o mesmo tipo de informação precisa e atingida de forma determinística, as sucessivas imagens a
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Wei, Junqing. "Autonomous Vehicle Social Behavior for Highway Driving." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/919.

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In recent years, autonomous driving has become an increasingly practical technology. With state-of-the-art computer and sensor engineering, autonomous vehicles may be produced and widely used for travel and logistics in the near future. They have great potential to reduce traffic accidents, improve transportation efficiency, and release people from driving tasks while commuting. Researchers have built autonomous vehicles that can drive on public roads and handle normal surrounding traffic and obstacles. However, in situations like lane changing and merging, the autonomous vehicle faces the cha
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Books on the topic "Autonomous Driving"

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Maurer, Markus, J. Christian Gerdes, Barbara Lenz, and Hermann Winner, eds. Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8.

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Fan, Rui, Sicen Guo, and Mohammud Junaid Bocus, eds. Autonomous Driving Perception. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4287-9.

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Jurgen, Ronald K., ed. Autonomous Vehicles for Safer Driving. SAE International, 2013. http://dx.doi.org/10.4271/0768080398.

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Jurgen, Ronald K. Autonomous Vehicles for Safer Driving. SAE International, 2013. http://dx.doi.org/10.4271/pt-158.

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Shi, Weisong, and Liangkai Liu. Computing Systems for Autonomous Driving. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81564-6.

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Chai, Zhanxiang, Tianxin Nie, and Jan Becker. Autonomous Driving Changes the Future. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6728-5.

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Langheim, Jochen, ed. Energy Consumption and Autonomous Driving. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19818-7.

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Kröger, Fabian. From Automated to Autonomous Driving. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49881-7.

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Gamba, Jonah. Radar Signal Processing for Autonomous Driving. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9193-4.

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Zhang, Xinyu, Jun Li, Zhiwei Li, et al. Multi-sensor Fusion for Autonomous Driving. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3280-1.

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Book chapters on the topic "Autonomous Driving"

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Maurer, Markus. "Introduction." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_1.

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Beiker, Sven. "Deployment Scenarios for Vehicles with Higher-Order Automation." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_10.

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Heinrichs, Dirk. "Autonomous Driving and Urban Land Use." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_11.

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Cyganski, Rita. "Automated Vehicles and Automated Driving from a Demand Modeling Perspective." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_12.

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Winner, Hermann, and Walther Wachenfeld. "Effects of Autonomous Driving on the Vehicle Concept." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_13.

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Beiker, Sven. "Implementation of an Automated Mobility-on-Demand System." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_14.

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Wagner, Peter. "Traffic Control and Traffic Management in a Transportation System with Autonomous Vehicles." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_15.

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Friedrich, Bernhard. "The Effect of Autonomous Vehicles on Traffic." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_16.

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Winkle, Thomas. "Safety Benefits of Automated Vehicles: Extended Findings from Accident Research for Development, Validation and Testing." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_17.

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Flämig, Heike. "Autonomous Vehicles and Autonomous Driving in Freight Transport." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_18.

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Conference papers on the topic "Autonomous Driving"

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Petrovic, Nenad, Krzysztof Lebioda, Vahid Zolfaghari, et al. "LLM-Driven Testing for Autonomous Driving Scenarios." In 2024 2nd International Conference on Foundation and Large Language Models (FLLM). IEEE, 2024. https://doi.org/10.1109/fllm63129.2024.10852505.

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Zhang, Jimuyang, Zanming Huang, Arijit Ray, and Eshed Ohn-Bar. "Feedback-Guided Autonomous Driving." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01421.

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Sidorenko, Galina, Johan Thunberg, and Alexey Vinel. "Cooperation for Ethical Autonomous Driving." In 2024 20th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE, 2024. https://doi.org/10.1109/wimob61911.2024.10770472.

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Gulzar, Mahir, Tambet Matiisen, and Naveed Muhammad. "Scenario Driven Development for Open Source Autonomous Driving Stack." In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2024. http://dx.doi.org/10.1109/etfa61755.2024.10710800.

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Walch, Marcel, Kristin Lange, Martin Baumann, and Michael Weber. "Autonomous driving." In AutomotiveUI '15: The 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, 2015. http://dx.doi.org/10.1145/2799250.2799268.

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Alexandra, Popa, Toderean Bianca, Toderean Liana-Maria, Ulici Ioana-Anamaria, Rusu-Both Roxana, and Miclea Liviu-Cristian. "Intelligent Autonomous Driving." In 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2018. http://dx.doi.org/10.1109/icstcc.2018.8540757.

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Zhang, Ya-Qin. "Towards Autonomous Driving." In WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining. ACM, 2023. http://dx.doi.org/10.1145/3539597.3572331.

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Ghosh, Prof Ankush. "AI driven Sustainability for Autonomous Driving." In 3rd World Conference on Engineering, Technology and Applied Science. Eurasia Conferences, 2024. https://doi.org/10.62422/978-81-974314-7-0-002.

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Self-driving technology is poised to revolutionize transportation infrastructure globally, offering a unique opportunity to enhance the quality of life. As urban areas face challenges such as rapid growth, avoidable collisions, vehicle emissions, and congestion from single-occupant commuters, autonomous vehicles promise to transform transportation systems by delivering significant environmental, social, and economic benefits. However, autonomous ground vehicles (AGVs) must overcome various challenges to navigate safely from origin to destination. In this lecture, we will explore these challeng
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Lienen, Christian, Mathis Brede, Daniel Karger, et al. "AutonomROS: A ReconROS-based Autonomous Driving Unit." In 2023 Seventh IEEE International Conference on Robotic Computing (IRC). IEEE, 2023. http://dx.doi.org/10.1109/irc59093.2023.00056.

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Biral, Francesco, Enrico Bertolazzi, Daniele Bortoluzzi, and Paolo Bosetti. "Development and Testing of an Autonomous Driving Module for Critical Driving Conditions." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-68487.

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In the last years a great effort has been devoted to the development of autonomous vehicles able to drive in a high range of speeds in semi-structured and unstructured environments. This article presents and discusses the software framework for Hardware-In-the-Loop (HIL) and Software-In-the-Loop (SIL) analysis that has been designed for developing and testing of control laws and mission functionalities of semi-autonomous and autonomous vehicles. The ultimate goal of this project is to develop a robotic system, named RUMBy, able to autonomously plan and execute accurate optimal manoeuvres both
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Reports on the topic "Autonomous Driving"

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Albus, James, John Evans, Craig Schlenoff, Tony Barbera, Elena Messina, and Stephen Balakirsky. Achieving intelligent performance in autonomous driving. National Institute of Standards and Technology, 2003. http://dx.doi.org/10.6028/nist.ir.7166.

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Chen, Guang. Multi-agent Collaborative Perception for Autonomous Driving: Unsettled Aspects. SAE International, 2023. http://dx.doi.org/10.4271/epr2023017.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;This report delves into the field of multi-agent collaborative perception (MCP) for autonomous driving: an area that remains unresolved. Current single-agent perception systems suffer from limitations, such as occlusion and sparse sensor observation at a far distance.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;b&gt;Multi-agent Collaborative Perception for Autonomous Driving: Unsettled Aspects&lt;/b&gt; addresses three unsettled topics that demand immediate attention: &lt;ul class="list disc"&gt;&lt;li class="li
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Ferreira Da Silva, Rafael, Rob Moore II, Ben Mintz, et al. Shaping the Future of Self-Driving Autonomous Laboratories Workshop. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2481197.

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Wang, Shenlong, and David Forsyth. Safely Test Autonomous Vehicles with Augmented Reality. Illinois Center for Transportation, 2022. http://dx.doi.org/10.36501/0197-9191/22-015.

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This work exploits augmented reality to safely train and validate autonomous vehicles’ performance in the real world under safety-critical scenarios. Toward this goal, we first develop algorithms that create virtual traffic participants with risky behaviors and seamlessly insert the virtual events into real images perceived from the physical world. The resulting composed images are photorealistic and physically grounded. The manipulated images are fed into the autonomous vehicle during testing, allowing the self-driving vehicle to react to such virtual events within either a photorealistic sim
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Joress, Howie. Driving U.S. Innovation in Materials and Manufacturing Using AI and Autonomous Labs. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.sp.1320.

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Quinn, Brian, Jordan Bates, Michael Parker, and Sally Shoop. A detailed approach to autonomous vehicle control through Ros and Pixhawk controllers. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42460.

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A Polaris MRZR military utility vehicle was used as a testing platform to develop a novel, low cost yet feature-rich, approach to adding remote operation and autonomous driving capability to a military vehicle. The main concept of operation adapts steering and throttle output from a low cost commercially available Pixhawk autopilot controller and translates the signal into the necessary inputs for the Robot Operating System (ROS) based drive by wire system integrated into the MRZR. With minimal modification these enhancements could be applied to any vehicle with similar ROS integration. This p
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Favarò, Francesca M. Impact of Smart Phones’ Interaction Modality on Driving Performance for Conventional and Autonomous Vehicles. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1813.

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Mukherjee, Amitangshu. Semantic Domain Adaptation for Deep Networks via GAN-based Data Augmentation for Autonomous Driving. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-1273.

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Porcel Magnusson, Cristina. Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles. SAE International, 2021. http://dx.doi.org/10.4271/epr2021002.

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Autonomous vehicles (AVs) utilize multiple devices, like high-resolution cameras and radar sensors, to interpret the driving environment and achieve full autonomy. One of these instruments—the light detection and ranging (LiDAR) sensor—utilizes pulsed infrared (IR) light, typically at wavelengths of 905 nm or 1,550 nm, to calculate object distance and position. Exterior automotive paint covers an area larger than any other exterior material. Therefore, understanding how LiDAR wavelengths interact with vehicle coatings is extremely important for the safety of future automated driving technologi
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Frydman, Roman, Søren Johansen, Anders Rahbek, and Morten Nyboe Tabor. Asset Prices Under Knightian Uncertainty. Institute for New Economic Thinking Working Paper Series, 2021. http://dx.doi.org/10.36687/inetwp172.

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We extend Lucas’s classic asset-price model by opening the stochastic process driving dividends to Knightian uncertainty arising from unforeseeable change. Implementing Muth’s hypothesis, we represent participants’ expectations as being consistent with our model’s predictions and formalize their ambiguity-averse decisions with maximization of intertemporal multiple-priors utility. We characterize the asset-price function with a stochastic Euler equation and derive a novel prediction that the relationship between prices and dividends undergoes unforeseeable change. Our approach accords particip
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