Academic literature on the topic 'Autonomus driving'

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Journal articles on the topic "Autonomus driving"

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Knabl, Florian, and Lars Mesow. "Autonomes Fahren im Kleinformat Audi Autonomous Driving Cup." Sonderprojekte ATZ/MTZ 22, S2 (2017): 26–29. http://dx.doi.org/10.1007/s41491-017-0006-z.

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Hernández-Olivares, Zabulón Miguel, Alberto Jorge Rosales-Silva, Armando Adrián Miranda-González, et al. "Análisis de un sistema de conducción autónoma." Científica 27, no. 2 (2023): 1–15. http://dx.doi.org/10.46842/ipn.cien.v27n2a06.

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Debido al progreso tecnológico en automatización e inteligencia artificial aplicados a la autonomía de vehículos, ha surgido una mayor relevancia en los niveles de asistencia a la conducción autónoma, como lo indica un informe del INEGI que muestra una disminución en el número de muertes en accidentes de tránsito entre 2016 y 2020 [1]. Por esta razón, el Instituto Mexicano de Transporte (IMT) se basó en lo que la Sociedad de Ingenieros Automotrices (SAE, Society of Automotive Engineers) ha establecido, que es una clasificación de 6 niveles de asistencia a la conducción, que abarca desde la con
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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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Hurair, Mohammad, Jaeil Ju, and Junghee Han. "Environmental-Driven Approach towards Level 5 Self-Driving." Sensors 24, no. 2 (2024): 485. http://dx.doi.org/10.3390/s24020485.

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As technology advances in almost all areas of life, many companies and researchers are working to develop fully autonomous vehicles. Such level 5 autonomous driving, unlike levels 0 to 4, is a driverless vehicle stage and so the leap from level 4 to level 5 autonomous driving requires much more research and experimentation. For autonomous vehicles to safely drive in complex environments, autonomous cars should ensure end-to-end delay deadlines of sensor systems and car-controlling algorithms including machine learning modules, which are known to be very computationally intensive. To address th
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李建良, 李建良. "智慧駕駛的法學思辨──自動駕駛法學的試車之旅". 月旦法學雜誌 327, № 327 (2022): 94–114. http://dx.doi.org/10.53106/1025593132705.

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Ansari, Hashim Shakil, and Goutam R. "Autonomous Driving using Deep Reinforcement Learning in Urban Environment." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (2019): 1573–75. http://dx.doi.org/10.31142/ijtsrd23442.

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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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Dissertations / Theses on the topic "Autonomus driving"

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Becci, Simone. "Feasibility, requirements and preliminary assessment of e-horizon driven speed targeting algorithms, for enhanced Adaptive Cruise Control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23435/.

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According to the report from the EU,every year more than 40 000 lives are lost in a road accident. More than 90% of accidents are caused by human error. Advance Driver-Assistance Systems (ADAS) can help drivers to avoid these accidents or at least to reduce the consequence. Such systems use a multitude of sensors to perceive the environment around the vehicle,and act consequently. In the future, cars will be a part of an intelligent transport system where all players are connected. With these abilities, an electronic horizon is created pushing forward the sensor range. Information regarding tr
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Edvardsson, Felicia, and Therése Warberg. "Konceptuell utveckling av interiören hos en framtida fullt autonom bil." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12794.

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Målet med examensarbetet har varit att samla information åt ett tekniskt konsultföretag för att öka deras kunskap om autonoma system och fordonskommunikation. Statusen på arbetet kring dessa aktiva säkerhetssystem hos olika aktörer och hur systemen implementeras i dagens och framtidens fordon har undersökts genom omfattande litteraturstudier, intervjuer och marknadsanalyser. De autonoma systemen kan samla information från omgivningen genom sensorer och bidra till ett jämnare trafikflöde, ökad säkerhet, lättare bilar och bättre miljö. Genom fordonskommunikationen kan fordon kommunicera med vara
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Espada, Yoan. "Localisation visuelle par approche neuromimétique dans le contexte de la délégation de conduite." Thesis, CY Cergy Paris Université, 2020. http://www.theses.fr/2020CYUN1070.

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L'objectif premier de cette thèse est de tester et d'étendre des modèles de navigation initialement développés pour de la navigation en intérieur et de les appliquer à la délégation de conduite des véhicules autonomes. La particularité de ces architectures de contrôle pour robot mobile est qu'elles sont issus d'une approche bio-inspirée et sont dite neurorobotiques : elles reproduisent les mécanismes neuronaux impliqués dans les comportements de navigation observés chez les animaux.Ces travaux concourent à introduire auprès de la communauté du véhicule autonome les approches bio-inspirées et l
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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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Yevdokymenkova, Kateryna, and Катерина Андріївна Євдокименкова. "Autonomous transport of the future." Thesis, National Aviation University, 2021. https://er.nau.edu.ua/handle/NAU/50582.

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1. GEAR 2030 and Strategy 2018-2020 – Comparative analysis of the competitive position of the EU automotive industry and the impact of the introduction of autonomous vehicles [Electronic resource] // Publications Office of the EU. – 2020. – Access mode: https://cutt.ly/QcoLTmU. 2. Unmanned multi-purpose vehicles: modern technologies / O. Ya. Nikonov, L. E. Kulakova, T. O. Polosukhina, V. O. Chernyshov. // Automotive and Electronics. Modern technology.. – 2017. – №11. – С. 46–49. Scientific adviser - doctor of Economics, professor Yanchuk M.B.<br>The idea of autonomous car control has exist
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Sportillo, Daniele. "Préparation à la conduite automatisée en Réalité Mixte." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEM017/document.

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L'automatisation de la conduite est un processus en cours qui est en train de changer radicalement la façon dont les gens voyagent et passent du temps dans leur voiture pendant leurs déplacements. Les véhicules conditionnellement automatisés libèrent les conducteurs humains de la surveillance et de la supervision du système et de l'environnement de conduite, leur permettant d'effectuer des activités secondaires pendant la conduite, mais requièrent qu’ils puissent reprendre la tâche de conduite si nécessaire. Pour les conducteurs, il est essentiel de comprendre les capacités et les limites du s
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Olsson, Magnus. "Behavior Trees for decision-making in Autonomous Driving." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183060.

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This degree project investigates the suitability of using Behavior Trees (BT) as an architecture for the behavioral layer in autonomous driving. BTs originate from video game development but have received attention in robotics research the past couple of years. This project also includes implementation of a simulated traffic environment using the Unity3D engine, where the use of BTs is evaluated and compared to an implementation using finite-state machines (FSM). After the initial implementation, the simulation along with the control architectures were extended with additional behaviors in four
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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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Books on the topic "Autonomus 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 "Autonomus 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 "Autonomus driving"

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Rafique, Waleed, Jikai Wang, and Zhonghai Chen. "Robust Decision Making with Multi-modal Perception for Autonomus Driving in Hybrid Action Space." In 2024 IEEE 25th China Conference on System Simulation Technology and its Application (CCSSTA). IEEE, 2024. http://dx.doi.org/10.1109/ccssta62096.2024.10691718.

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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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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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Borgnino, Leandro E., Claudio A. Delrieux, Nicolás Salomón, and Damián A. Morero. "Computer Graphics-Driven 3D LiDAR Model for Autonomous Driving Systems." In 2025 Argentine Conference on Electronics (CAE). IEEE, 2025. https://doi.org/10.1109/cae64243.2025.10962084.

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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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Tian, Bin, Tingting Yao, I. Yisheng Lv, Shichao Chen, Yang Sun, and Ruiqi Song. "Parallel Data and Foundation Model Driven Closed-Loop of Autonomous Driving." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10919667.

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Liao, Haicheng, Shangqian Liu, Yongkang Li, et al. "Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611104.

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Binghong, Gan. "Automotive Radar Technology & Test Solution for Autonomus Driving." In 2019 IEEE MTT-S International Wireless Symposium (IWS). IEEE, 2019. http://dx.doi.org/10.1109/ieee-iws.2019.8803901.

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Petersen, Luke, Huajing Zhao, Dawn M. Tilbury, X. Jessie Yang, and Lionel P. Robert. "THE INFLUENCE OF RISK ON DRIVER TRUST IN AUTONOMOUS DRIVING SYSTEMS." In 2024 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium. National Defense Industrial Association, 2024. http://dx.doi.org/10.4271/2024-01-3748.

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&lt;title&gt;ABSTRACT&lt;/title&gt; &lt;p&gt;Autonomous driving systems (ADS) in autonomous and semi-autonomous vehicles have the potential to improve driving safety and enable drivers to perform non-driving tasks concurrently. Drivers sometimes fail to fully leverage a vehicle’s autonomy because of a lack of trust. To address this issue, the present study examined the influence of risk on drivers’ trust. Subject tests were conducted to evaluate the effects of combined internal and external risk, where participants drove a simulated semi-autonomous vehicle and completed a secondary task at the
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Liao, Haicheng, Zhenning Li, Chengyue Wang, et al. "A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environments." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/656.

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As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive insights into trajectory prediction, focusing on perceived safety and dynamic decision-making. Distinct from traditional approaches, our model excels in analyzing interactions and behavior patterns in mixed autonomy traffic scenarios. We introduce the Macao Connected Autonomous Driving (MoCAD) dataset as part of our contributions, which adds value to its complex urban driving scenarios. Our model represents a signific
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Reports on the topic "Autonomus 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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Hemphill, Jeff. Unsettled Issues in Drive-by-Wire and Automated Driving System Availability. SAE International, 2022. http://dx.doi.org/10.4271/epr2022002.

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While many observers think that autonomy is right around the corner, there many unsettled issues. One such issue is availability, or how the vehicle behaves in the event of a failure of one of its systems such as those with the latest “by-wire” technologies. Handling of failures at a technical actuation level could involve many aspects, including time of operation after first fault, function/performance after first fault, and exposure after first fault. All of these and other issues are affected by software and electronic and mechanical hardware. Drive-by-wire and Automated Driving System Avai
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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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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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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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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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Qin, Tong, Zhen Chen, John Jakeman, and Dongbin Xiu. Data-driven learning of non-autonomous systems. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1763550.

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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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