Relevant bibliographies by topics / Autonomous Driving Systems

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Autonomous Driving Systems'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Autonomous Driving Systems"

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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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Maeng, Joon Young. "Autonomous Vehicle and Civil Liability Standard ―Legalization of Autonomous Driving and Evaluation Thereof―." Korean Association of Civil Law 110 (March 31, 2025): 407–48. https://doi.org/10.52554/kjcl.2025.110.407.

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With the introduction of autonomous vehicles, various aspects of autonomous driving are being legislated in laws and subordinate rules in our legal system. Motor Vehicle Management Act broadly defines autonomous vehicles, while the Road Traffic Act broadly divides autonomous driving systems into fully autonomous driving systems and partial autonomous driving systems. Regarding the autonomous driving stage, autonomous vehicles are defined as partially autonomous vehicles and fully autonomous vehicles, and in the subordinate rule of the Motor Vehicle Management Act, autonomous driving systems is
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Yaakub, Salma, and Mohammed Hayyan Alsibai. "A Review on Autonomous Driving Systems." International Journal of Engineering Technology and Sciences 5, no. 1 (2018): 1–16. http://dx.doi.org/10.15282/ijets.v5i1.2800.

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Autonomous vehicles are one of the promising solutions to reduce traffic crashes and improve mobility and traffic system. An autonomous vehicle is preferable because it helps in reducing the need for redesigning the infrastructure and because it improves the vehicle power efficiency in terms of cost and time taken to reach the destination. Autonomous vehicles can be divided into 3 types: Aerial vehicles, ground vehicles and underwater vehicles. General, four basic subsystems are integrated to enable a vehicle to move by itself which are: Position identifying and navigation system, surrounding
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Cai, Lipeng. "Key Sensing Systems in Autonomous Driving." Highlights in Science, Engineering and Technology 119 (December 11, 2024): 242–48. https://doi.org/10.54097/ws6xrd83.

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With the rapid and dynamic evolution of autonomous driving technology. The escalating demand for transportation that is not only safer but also more efficient has spurred an intense exploration of advanced sensing technologies. This article centers on the sensor system within the domain of autonomous driving. The principal methods encompass an in-depth study of various distinct sensors such as lidar, cameras, radar, and ultrasonic sensors. The research findings reveal that these sensor systems can synergistically collaborate to furnish highly precise environmental perception. Specifically, lid
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Henschke, Adam. "Trust and resilient autonomous driving systems." Ethics and Information Technology 22, no. 1 (2019): 81–92. http://dx.doi.org/10.1007/s10676-019-09517-y.

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Zheng, Yiwen. "Application of a Multifunctional Image Processing System Based on C in Autonomous Driving." Applied and Computational Engineering 160, no. 1 (2025): 120–27. https://doi.org/10.54254/2755-2721/2025.tj23501.

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Autonomous driving technology is a current research hotspot in the fields of artificial intelligence and computer vision. Its core relies on environmental information obtained from sensors such as cameras and radars. Image processing technology plays a crucial role in autonomous driving, including tasks such as lane detection, obstacle recognition, and environmental perception. With the rapid development of autonomous driving technology, the demand for image processing systems has significantly increased, especially in terms of real-time performance, accuracy, and multifunctionality. Existing
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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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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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V S, Amar. "Autonomous Driving using CNN." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3633–36. http://dx.doi.org/10.22214/ijraset.2021.35771.

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Human beings are currently addicted to automation and robotics technologies. The state-of-the-art in deep learning technologies and AI is the subject of this autonomous driving. Driving with automated driving systems promises to be safe, enjoyable, and efficient.. It is preferable to train in a virtual environment first and then move to a real-world one. Its goal is to enable a vehicle to recognise its surroundings and navigate without the need for human intervention. The raw pixels from a single front-facing camera were directly transferred to driving commands using a convolution neural netwo
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Fu, Zichen. "The Current Development and Future Prospects of Autonomous Driving Driven by Artificial<b> </b>Intelligence." Computers and Artificial Intelligence 2, no. 1 (2025): 8–15. https://doi.org/10.70267/cai.25v2n1.0815.

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This paper explores the application and development of artificial intelligence in autonomous driving and analyses its current status, challenges, and future trends. Autonomous driving systems integrate multiple core technologies in vehicle perception and driving decision-making, achieving a leap from assisted driving to commercial deployment. Leveraging emerging methods such as machine learning, deep learning, and reinforcement learning, autonomous driving systems have significantly improved perception accuracy, decision-making capabilities, and environmental adaptability. However, current aut
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Dissertations / Theses on the topic "Autonomous Driving Systems"

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Al-Khoury, Fadi. "Safety of Machine Learning Systems in Autonomous Driving." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218020.

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Machine Learning, and in particular Deep Learning, are extremely capable tools for solving problems which are difficult, or intractable to tackle analytically. Application areas include pattern recognition, computer vision, speech and natural language processing. With the automotive industry aiming for increasing amount of automation in driving, the problems to solve become increasingly complex, which appeals to the use of supervised learning methods from Machine Learning and Deep Learning. With this approach, solutions to the problems are learned implicitly from training data, and inspecting
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Agha, Jafari Wolde Bahareh. "A systematic Mapping study of ADAS and Autonomous Driving." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-42754.

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Nowadays, autonomous driving revolution is getting closer to reality. To achieve the Autonomous driving the first step is to develop the Advanced Driver Assistance System (ADAS). Driver-assistance systems are one of the fastest-growing segments in automotive electronics since already there are many forms of ADAS available. To investigate state of art of development of ADAS towards Autonomous Driving, we develop Systematic Mapping Study (SMS). SMS methodology is used to collect, classify, and analyze the relevant publications. A classification is introduced based on the developments carried out
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Villalonga, Pineda Gabriel. "Leveraging Synthetic Data to Create Autonomous Driving Perception Systems." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/671739.

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L’anotació manual d’imatges per desenvolupar sistemes basats en visió per computador ha estat un dels punts més problemàtics des que s’utilitza aprenentatge automàtic per a això. Aquesta tesi es centra en aprofitar les dades sintètiques per alleujar el cost de les anotacions manuals en tres tasques de percepció relacionades amb l’assistència a la conducció i la conducció autònoma. En tot moment assumim l’ús de xarxes neuronals convolucionals per al desenvolupament dels nostres models profunds de percepció. La primera tasca planteja el reconeixement de senyals de trànsit, un problema de classif
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Sharma, Devendra. "Evaluation and Analysis of Perception Systems for Autonomous Driving." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291423.

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For safe mobility, an autonomous vehicle must perceive the surroundings accurately. There are many perception tasks associated with understanding the local environment such as object detection, localization, and lane analysis. Object detection, in particular, plays a vital role in determining an object’s location and classifying it correctly and is one of the challenging tasks in the self-driving research area. Before employing an object detection module in autonomous vehicle testing, an organization needs to have a precise analysis of the module. Hence, it becomes crucial for a company to hav
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Behere, Sagar. "Architecting Autonomous Automotive Systems : With an emphasis on Cooperative Driving." Licentiate thesis, KTH, Inbyggda styrsystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-120595.

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The increasing usage of electronics and software in a modern automobile enables realization of many advanced features. One such feature is autonomous driving. Autonomous driving means that a human driver’s intervention is not required to drive the automobile; rather, theautomobile is capable of driving itself. Achieving automobile autonomyrequires research in several areas, one of which is the area of automotive electrical/electronics (E/E) architectures. These architectures deal with the design of the computer hardware and software present inside various subsystems of the vehicle, with partic
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Behere, Sagar. "Reference Architectures for Highly Automated Driving." Doctoral thesis, KTH, Inbyggda styrsystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179306.

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Highly automated driving systems promise increased road traffic safety, as well as positive impacts on sustainable transportation by means of increased traffic efficiency and environmental friendliness. The design and development of such systems require scientific advances in a number of areas. One area is the vehicle's electrical/electronic (E/E) architecture. The E/E architecture can be presented using a number of views, of which an important one is the functional view. The functional view describes the decomposition of the system into its main logical components, along with the hierarchical
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Argui, Imane. "A vision-based mixed-reality framework for testing autonomous driving systems." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR37.

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Cette thèse explore le développement et la validation des systèmes de navigation autonome dans un environnement de réalité mixte (RM), avec pour objectif de combler l’écart entre la simulation virtuelle et les tests en conditions réelles. Les travaux mettent l’accent sur le potentiel des environnements en réalité mixte pour tester les systèmes autonomes de manière sûre, efficace et économique. La thèse est structurée en plusieurs parties, et commence par une revue des technologies de pointe dans la navigation autonome et les applications en réalité mixte. En utilisant des modèles à base de règ
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Chi, Lijun. "Security and Robustness of Autonomous Driving Systems Against Physical Adversarial Attack." Electronic Thesis or Diss., Institut polytechnique de Paris, 2025. http://www.theses.fr/2025IPPAT009.

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Grâce à des mises à jour matérielles itératives et aux avancées dans les réseaux neuronaux profonds (DNN), les systèmes de conduite autonome (ADS) sont de plus en plus intégrés à la vie quotidienne. Cependant, avant que cette technologie ne se généralise, un problème de sécurité qui doit être résolu est celui des attaques adversariales physiques. Ces attaques peuvent manipuler des objets réels pour perturber la perception des ADS et provoquer des accidents de la route. De plus, la diversité des attaques physiques complique la tâche des défenseurs passifs.Cette étude aborde ces défis en analysa
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Veeramani, Lekamani Sarangi. "Model Based Systems Engineering Approach to Autonomous Driving : Application of SysML for trajectory planning of autonomous vehicle." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254891.

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Model Based Systems Engineering (MBSE) approach aims at implementing various processes of Systems Engineering (SE) through diagrams that provide different perspectives of the same underlying system. This approach provides a basis that helps develop a complex system in a systematic manner. Thus, this thesis aims at deriving a system model through this approach for the purpose of autonomous driving, specifically focusing on developing the subsystem responsible for generating a feasible trajectory for a miniature vehicle, called AutoCar, to enable it to move towards a goal. The report provides a
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Perez, Cervantes Marcus Sebastian. "Issues of Control with Older Drivers and Future Automated Driving Systems." Research Showcase @ CMU, 2011. http://repository.cmu.edu/theses/21.

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It is inevitable that as a person ages they will encounter different physical and cognitive impairments as well as dynamic social issues. We started this project under the assumption that autonomous driving would greatly benefit the fastest growing population in developed countries, the elderly. However, the larger question at hand was how are older drivers going to interact with future automated driving systems? It was through the qualitative research we conducted that we were able to uncover the answer to this question; older drivers are not willing to give up “control” to autonomous cars. A
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Books on the topic "Autonomous Driving Systems"

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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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Joseph, Lentin, and Amit Kumar Mondal. Autonomous Driving and Advanced Driver-Assistance Systems (ADAS). CRC Press, 2021. http://dx.doi.org/10.1201/9781003048381.

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Waschl, Harald, Ilya Kolmanovsky, and Frank Willems, eds. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91569-2.

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Trimble, Tammy E., Stephanie Baker, Jason Wagner, et al. Implications of Connected and Automated Driving Systems, Vol. 4: Autonomous Vehicle Action Plan. Transportation Research Board, 2018. http://dx.doi.org/10.17226/25292.

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Zuev, Sergey, Ruslan Maleev, and Aleksandr Chernov. Energy efficiency of electrical equipment systems of autonomous objects. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1740252.

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When considering the main trends in the development of modern autonomous objects (aircraft, combat vehicles, motor vehicles, floating vehicles, agricultural machines, etc.) in recent decades, two key areas can be identified. The first direction is associated with the improvement of traditional designs of autonomous objects (AO) with an internal combustion engine (ICE) or a gas turbine engine (GTD). The second direction is connected with the creation of new types of joint-stock companies, namely electric joint-stock companies( EAO), joint-stock companies with combined power plants (AOKEU).&#x0D
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Trimble, Tammy E., Stephanie Baker, Jason Wagner, et al. Implications of Connected and Automated Driving Systems, Vol. 5: Developing the Autonomous Vehicle Action Plan. Transportation Research Board, 2018. http://dx.doi.org/10.17226/25291.

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Kyoko, Okino, Sunamura Michinari, Ishibashi Jun-ichiro, Okino Kyoko, and Sunamura Michinari, eds. Subseafloor Biosphere Linked to Hydrothermal Systems: TAIGA Concept. Springer Nature, 2015.

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Shi, Weisong, and Liangkai Liu. Computing Systems for Autonomous Driving. Springer International Publishing AG, 2022.

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Shi, Weisong, and Liangkai Liu. Computing Systems for Autonomous Driving. Springer International Publishing AG, 2021.

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Nitsch, Tobias. Sensor Systems and Communication Technologies in Autonomous Driving. GRIN Verlag GmbH, 2018.

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Book chapters on the topic "Autonomous Driving Systems"

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Matthaei, Richard, Andreas Reschka, Jens Rieken, et al. "Autonomous Driving." In Handbook of Driver Assistance Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12352-3_61.

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Matthaei, Richard, Andreas Reschka, Jens Rieken, et al. "Autonomous Driving." In Handbook of Driver Assistance Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09840-1_61-1.

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Pavone, Marco. "Autonomous Mobility-on-Demand Systems for Future Urban Mobility." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_19.

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Iclodean, Călin, Bogdan Ovidiu Varga, and Nicolae Cordoș. "Autonomous Driving Systems." In Autonomous Vehicles for Public Transportation. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14678-7_3.

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Shi, Weisong, and Liangkai Liu. "Autonomous Driving Landscape." In Computing Systems for Autonomous Driving. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81564-6_1.

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Shi, Weisong, and Liangkai Liu. "Autonomous Driving Simulators." In Computing Systems for Autonomous Driving. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81564-6_6.

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Behere, Sagar, and Martin Törngren. "Systems Engineering and Architecting for Intelligent Autonomous Systems." In Automated Driving. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31895-0_13.

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Ren, Jianfeng, and Dong Xia. "Autonomous Driving Operating Systems." In Autonomous driving algorithms and Its IC Design. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2897-2_11.

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Hammoud, Ahmad, Azzam Mourad, Hadi Otrok, and Zbigniew Dziong. "Data-Driven Federated Autonomous Driving." In Mobile Web and Intelligent Information Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14391-5_6.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Perception in Autonomous Driving." In Creating Autonomous Vehicle Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01802-2_3.

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Conference papers on the topic "Autonomous Driving Systems"

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S, Vignesh, K. Karunya, Vijaya Sankar K. P, S. L. Jany Shabu, and D. Poornima. "Obstacle Detection on Autonomous Driving Systems." In 2025 International Conference on Advanced Computing Technologies (ICoACT). IEEE, 2025. https://doi.org/10.1109/icoact63339.2025.11005028.

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Tang, Wenbing, Mingfei Cheng, Yuan Zhou, and Yang Liu. "Moral Testing of Autonomous Driving Systems." In 2025 IEEE/ACM 1st International Workshop on Software Engineering for Autonomous Driving Systems (SE4ADS). IEEE, 2025. https://doi.org/10.1109/se4ads66461.2025.00011.

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Zhang, Qi, Siyuan Gou, and Wenbin Li. "Visual Perception System for Autonomous Driving." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802028.

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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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Murali, Varun, Rosman Guy, Karaman Sertac, and Daniela Rus. "Learning autonomous driving from aerial imagery." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801752.

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Abeideh, Ahmad, Abdul Rahman Zamrik, and Wisam Elmasry. "Autonomous Driving Robot for Indoor 2D Mapping." In 2024 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2024. https://doi.org/10.1109/asyu62119.2024.10757050.

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Seo, Seongdeok, Judy Lee, and Mijung Kim. "Testing Diverse Geographical Features of Autonomous Driving Systems." In 2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE). IEEE, 2024. https://doi.org/10.1109/issre62328.2024.00049.

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Ben-Aoun, Sameh, Meriem Belguidoum, and Ahmed Hadj-Kacem. "Improving Autonomous Driving via Recommendation Systems: A Review." In 2024 IEEE/ACS 21st International Conference on Computer Systems and Applications (AICCSA). IEEE, 2024. https://doi.org/10.1109/aiccsa63423.2024.10912546.

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Song, Zhicao, Jinhui Fang, Ziyi Yang, Shuai Wang, and Gaofeng Pan. "Autonomous Driving Simulation Platform for Hybrid Traffic." In 2024 13th International Conference on Communications, Circuits and Systems (ICCCAS). IEEE, 2024. http://dx.doi.org/10.1109/icccas62034.2024.10652809.

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Zhang, Zhihe, Hongtao Nie, Yichi Zhang, et al. "MSDAD:A Multi-Sensor Dataset for Autonomous Driving." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10919939.

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Reports on the topic "Autonomous Driving Systems"

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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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Razdan, Rahul. Unsettled Topics Concerning Human and Autonomous Vehicle Interaction. SAE International, 2020. http://dx.doi.org/10.4271/epr2020025.

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This report examines the current interaction points between humans and autonomous systems, with a particular focus on advanced driver assistance systems (ADAS), the requirements for human-machine interfaces as imposed by human perception, and finally, the progress being made to close the gap. Autonomous technology has the potential to benefit personal transportation, last-mile delivery, logistics, and many other mobility applications enormously. In many of these applications, the mobility infrastructure is a shared resource in which all the players must cooperate. In fact, the driving task has
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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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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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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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Shen, Shiyu, Yuhui Zhai, and Yanfeng Ouyang. Planning and Dynamic Management of Autonomous Modular Mobility Services. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-029.

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As we enter the next era of autonomous driving, robo-vehicles (which serve as low-cost and fully compliant drivers) are replacing conventional chauffeured services in the mobility market. During just the last few years, companies like Waymo Inc. and Cruise Inc. have already offered fully driverless robo-taxi services to the general public in cities like Phoenix and San Francisco. The rapid evolution of autonomous vehicles is anticipated to reshape the shared mobility market very soon. This project aims to address the following open questions. At the operational level, how should modular units
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Pasupuleti, Murali Krishna. Neuromorphic Nanotech: 2D Materials for Energy-Efficient Edge Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr325.

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Abstract The demand for energy-efficient, real-time computing is driving the evolution of neuromorphic computing and edge AI systems. Traditional silicon-based processors struggle with power inefficiencies, memory bottlenecks, and scalability limitations, making them unsuitable for next-generation low-power AI applications. This research report explores how 2D materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus, and MXenes, are enabling the development of neuromorphic architectures that mimic biological neural networks for high-speed, ultra-low-power computat
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Abdul Hamid, Umar Zakir. Responder-to-Vehicle Technologies for Connected and Autonomous Vehicles. SAE International, 2023. http://dx.doi.org/10.4271/epr2023010.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Recently, there has been a slight increase in interest in the use of responder-to-vehicle (R2V) technology for emergency vehicles, such as ambulances, fire trucks, and police cars. R2V technology allows for the exchange of information between different types of responder vehicles, including connected and automated vehicles (CAVs). It can be used in collision avoidance or emergency situations involving CAV responder vehicles. The benefits of R2V are not limited to fully autonomous vehicles (e.g., SAE Level 4), but can als
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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes
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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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