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Journal articles on the topic 'Autonomus driving'

Author: Grafiati

Published: 25 June 2021

Last updated: 29 July 2025

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1

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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2

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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6

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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7

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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8

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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10

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

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11

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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Wang Yue, 王越, та 曹家乐 Cao Jiale. "基于任务特征解耦的自动驾驶视觉联合感知模型". Laser & Optoelectronics Progress 61, № 22 (2024): 2215007. http://dx.doi.org/10.3788/lop240559.

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13

Vamshi Krishna Reddy, Attla. "Training and Testing Autonomous Driving Software using Udacity Car Simulator." International Journal of Science and Research (IJSR) 12, no. 5 (2023): 2206–10. http://dx.doi.org/10.21275/sr23501114821.

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QIU, Xiaoyang. "论人工智能驾驶技术视域下交通肇事罪的新展开". Academic Frontiers Publishing Group 2, № 3 (2025): 45–53. https://doi.org/10.62989/jcls.2025.2.3.45.

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人工智能驱使的智能驾驶技术发展迅速，催生了可能成为交通肇事的新因素。在传统领域刑法中，交通肇事罪的主体界定与客体归罪的相关理论在智能驾驶领域可能不再完全适用。为探讨智能驾驶系统的刑法可罚性，本文对智能驾驶技术作为工具抑或犯罪主体进行了区分，通过对传统交通肇事犯罪的重新构建来探讨强弱人工智能作为参与此类犯罪主体的情形。最后回归客观归罪视野，对“人机交互阶段”与“完全自动化”不同人工智能干预下的交通肇事责任进行重新分配，为明晰智能驾驶技术对于交通肇事罪不同责任主体刑事责任的议题提供了一定的理论优势和参考价值。

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Shweta, S. Darekar, and Anandhi Giri Dr. "Problems in Autonomous Driving System of Smart Cities in IoT." International Journal of Trend in Scientific Research and Development 4, no. 2 (2020): 945–49. https://doi.org/10.5281/zenodo.3843231.

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This paper focuses on the problems and challenges during self driving. In the modern era, technologies are getting advanced day by day. The field of smart city has introduced a new technology called "Autonomous Driving". Autonomous driving can be defined as Self Driving, Automated Vehicle. Google has started working on this type of system since 2010 and still in the phase of making changes in this technology to take it to a higher level. Any technology can reach up to an advanced level but it cannot provide a full fledged result. This paper facilitates the researchers to understand t

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Ren, Weixi, Bo Yu, Yuren Chen, and Kun Gao. "Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach." International Journal of Environmental Research and Public Health 19, no. 18 (2022): 11358. http://dx.doi.org/10.3390/ijerph191811358.

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Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical

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Mühl, Kristin, Christoph Strauch, Christoph Grabmaier, Susanne Reithinger, Anke Huckauf, and Martin Baumann. "Get Ready for Being Chauffeured." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 8 (2019): 1322–38. http://dx.doi.org/10.1177/0018720819872893.

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Objective We investigated passenger’s trust and preferences using subjective, qualitative, and psychophysiological measures while being driven either by human or automation in a field study and a driving simulator experiment. Background The passenger’s perspective has largely been neglected in autonomous driving research, although the change of roles from an active driver to a passive passenger is incontrovertible. Investigations of passenger’s appraisals on self-driving vehicles often seem convoluted with active manual driving experiences instead of comparisons with being driven by humans. Me

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Li, W., C. W. Pan, R. Zhang, et al. "AADS: Augmented autonomous driving simulation using data-driven algorithms." Science Robotics 4, no. 28 (2019): eaaw0863. http://dx.doi.org/10.1126/scirobotics.aaw0863.

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Simulation systems have become essential to the development and validation of autonomous driving (AD) technologies. The prevailing state-of-the-art approach for simulation uses game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (the assets for simulation) remain manual tasks that can be costly and time consuming. In addition, CG images still lack the richness and authenticity of real-world images, and using CG images for training leads to degraded performance. Here, we present our augmented autonomous drivi

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Wang, Yong, Shuqian Du, Qi Xin, Yuhang He, and Wenpin Qian. "Autonomous Driving System Driven by Artificial Intelligence Perception Fusion." Academic Journal of Science and Technology 9, no. 2 (2024): 193–98. http://dx.doi.org/10.54097/e0b9ak47.

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Perception, as the information input module of the automatic driving system, determines the lower limit of the entire automatic driving system. Both autonomous driving perception and robot perception are constantly approaching the real physical world through digital methods, and this real physical world is only limited to the scope of human perception, such as lane lines, traffic lights, driving obstacles, and so on. The main premise of this process is that humans already know the categories or properties of the physical world, and only allow machines and systems to replicate human responses.

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20

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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21

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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22

Manawadu, Udara Eshan, Masaaki Ishikawa, Mitsuhiro Kamezaki, and Shigeki Sugano. "Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator." Journal of Robotics and Mechatronics 27, no. 6 (2015): 660–70. http://dx.doi.org/10.20965/jrm.2015.p0660.

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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the prefer

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23

Shin, Seok-San, Ho-Joon Kang, and Seong-Jin Kwon. "A Study on Data Analysis for Improving Driving Safety in Field Operational Test (FOT) of Autonomous Vehicles." Machines 10, no. 9 (2022): 784. http://dx.doi.org/10.3390/machines10090784.

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In this study, an autonomous driving test was conducted from the perspective of FOT (field operational test). For data analysis and improvement methods, scenarios for FOT were classified and defined by considering autonomous driving level (SAE J3016) and the viewpoints of the vehicle, driver, road, environment, etc. To obtain data from FOT, performance indicators were selected, a data collection environment was implemented in the test cases, and driving roads were selected to obtain driving data from the vehicle while it was driven on an actual road. In the pilot FOT course, data were collecte

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Kruse, E. "Geräusch- und Vibrationsbekämpfung in modernen Fahrzeugen/Noise and vibration control in modern vehicles and new challenges due to the rise of e-mobility." Lärmbekämpfung 15, no. 06 (2020): 194–98. http://dx.doi.org/10.37544/1863-4672-2020-06-24.

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Zusammenfassung Weniger Kraftstoffverbrauch bei besseren Fahrleistungen, mehr Komfort und Fahrsicherheit ohne Mehrkosten sowie der Wandel zur Elektromobilität und zum autonomen Fahren: All diese Herausforderungen halten die Automobilindustrie konstant in Bewegung. Neben der technischen Umsetzung haben die Veränderungen Einfluss auf erzeugte Geräusche und Vibrationen und so auf den Komfort und das Wohlbefinden aller Fahrzeuginsassen. Automobilhersteller stehen daher gleich vor mehreren Zielkonflikten. Hier sind neue Lösungsansätze der automobilen Schwingungstechnik gefragt, um auch in der elekt

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Schockenhoff, Ferdinand, Hannes Nehse, and Markus Lienkamp. "Maneuver-Based Objectification of User Comfort Affecting Aspects of Driving Style of Autonomous Vehicle Concepts." Applied Sciences 10, no. 11 (2020): 3946. http://dx.doi.org/10.3390/app10113946.

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Driving maneuvers try to objectify user needs regarding the driving dynamics for a vehicle concept. As autonomous vehicles will not be driven by people, the driving style that merges the individual aspects of driving dynamics, like user comfort, will be part of the vehicle concept itself. New driving maneuvers are, therefore, necessary to objectify the driving style of autonomous vehicle concepts with all its interdependencies relating to the individual aspects. This paper presents a methodology to design such driving maneuvers and includes a pilot study and a user study. As an example, the me

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Zhang, Tingting. "The Identification of Infringement Liability of Artificial Intelligence Products - Taking Self-driving Cars as An Example." Frontiers in Humanities and Social Sciences 4, no. 11 (2024): 150–55. https://doi.org/10.54691/gqkt9580.

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With the progress of artificial intelligence technology, autonomous vehicles have begun to get research and development support and promotion applications worldwide. Compared with traditional artificially driven vehicles, the advantage of autonomous vehicles is that they can use intelligent driving systems to sense road information in real time, and the overall driving safety is higher. However, as an emerging technology, the laws, regulations and management system matching automatic driving technology are not perfect. When an autonomous vehicle has a traffic accident, there is a huge dispute

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王, 俊香. "Accident Data-Driven Autonomous Driving Scenario Use Cases Generalization Generation." Modeling and Simulation 12, no. 03 (2023): 1926–38. http://dx.doi.org/10.12677/mos.2023.123176.

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Ramesh, G., and J. Praveen. "Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles." E3S Web of Conferences 309 (2021): 01167. http://dx.doi.org/10.1051/e3sconf/202130901167.

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An electric vehicle with autonomous driving is a possibility provided technology innovations in multi-disciplinary approach. Electric vehicles leverage environmental conditions and are much desired in the contemporary world. Another great possibility is to strive for making the vehicle to drive itself (autonomous driving) provided instructions. When the two are combined, it leads to a different dimension of environmental safety and technology driven driving that has many pros and cons as well. It is still in its infancy and there is much research to be carried out. In this context, this paper

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Xie, Han, Kehong Li, and Juanxiu Zhu. "Analysis of the Relationship between Vehicle Behaviors of Changing Lane and Volume of Traffic under Different Operating Ratios of Autonomous Vehicles." Journal of Advanced Transportation 2022 (September 26, 2022): 1–23. http://dx.doi.org/10.1155/2022/3142483.

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The lane-changing behavior is one of the important causes in traffic accident in congest traffic, and many behaviors of change lane affect volume of traffic. When autonomous driving vehicles are running on road with human-driven vehicles, the effects of change lane on traffic are different. In all human-driven vehicles traffic, the vehicle behaviors of changing lane are more competent. When autonomous driving vehicles are running in mixed traffic, the behaviors of changing lane decrease and the volume of traffic increases. However, a few studies have involved the relationship between traffic v

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Baber, J., J. Kolodko, T. Noel, M. Parent, and L. Vlacic. "Cooperative autonomous driving - Intelligent vehicles sharing city roads cooperative autonomous driving." IEEE Robotics & Automation Magazine 12, no. 1 (2005): 44–49. http://dx.doi.org/10.1109/mra.2005.1411418.

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31

Jaehong, Park, and Yun Dukgeun. "THE METHOD OF ROAD FACILITY EXTRACTION FROM LIDAR POINT CLOUDS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 12 (2017): 10–14. https://doi.org/10.5281/zenodo.1098309.

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Various researches and technologies are being developed and utilized to improve the function of autonomous vehicle. These systems have been developed from simple systems that assisted drivers for safe driving. More recently, an autonomous vehicle, which can be driven without driver with control the steering wheel through the vehicle object recognition, has passed a test driving and is driving on real roads. To travel on the public road, it is necessary to recognize road facilities and have positioning technology, which are the basic technologies for the safe driving of autonomous vehicle. The

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Kwon, Yonghun, Woojae Kim, and Inbum Jung. "Neural Network Models for Driving Control of Indoor Autonomous Vehicles in Mobile Edge Computing." Sensors 23, no. 5 (2023): 2575. http://dx.doi.org/10.3390/s23052575.

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Mobile edge computing has been proposed as a solution for solving the latency problem of traditional cloud computing. In particular, mobile edge computing is needed in areas such as autonomous driving, which requires large amounts of data to be processed without latency for safety. Indoor autonomous driving is attracting attention as one of the mobile edge computing services. Furthermore, it relies on its sensors for location recognition because indoor autonomous driving cannot use a GPS device, as is the case with outdoor driving. However, while the autonomous vehicle is being driven, the rea

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D., Divya Kalpana, Keerthi K., Vishnu Priya B., Mutyalanna Ch., and Malleswari B. "Lane Detection and Traffic Sign for Autonomous Cars." Recent Innovations in Wireless Network Security 4, no. 3 (2023): 1–9. https://doi.org/10.5281/zenodo.7541116.

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Autonomous Vehicles are another term for self-driving cars. This vehicle can detect its surroundings. These perceived limits processed, and also varied actuators within the automobile operate with none human intervention. These perceived limits are managed, therefore numerous actuators within automotive operate with none human intervention. All Automated Functions are performed by autonomous vehicles using sensors, actuators, machine learning algorithms, and software. Autonomous vehicles are heavily reliant on software. The software architecture connects the hardware to the application. AUTOSA

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Panagiotakis, Spyros, and Evangelos K. Markakis. "An Editorial for the Special Issue “Pervasive Computing in IoT”." Information 15, no. 6 (2024): 320. http://dx.doi.org/10.3390/info15060320.

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In the era of Internet of Things (IoT) we have entered, the “Monitoring–Decision–Execution” cycle of typical autonomic and automation systems is extended, so it includes distributed developments that might scale from a smart home or greenhouse to a smart city and from autonomous driving to emergency management [...]

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35

Zhang, Ruixuan. "Autonomous Vehicles: Legal Governance of Civil Liability Risks." Lecture Notes in Education Psychology and Public Media 96, no. 1 (2025): 108–14. https://doi.org/10.54254/2753-7048/2025.bo24130.

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The advancement of artificial intelligence (AI) technologies has driven transformative shifts in transportation, evidenced by the accelerated transition of autonomous vehicles from controlled trials to large-scale public implementation. However, this technological evolution has exposed unprecedented civil liability risks that surpass the governance capacity of conventional traffic legal frameworks. This urgency calls for establishing a systematic legal governance framework focused on liability allocation rules, designed to address the unique challenges of civil liability risks in autonomous dr

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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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Hwang, Kitae, In Hwan Jung, and Jae Moon Lee. "Implementation of Autonomous Driving on RC-CAR with Raspberry PI and AI Server." Webology 19, no. 1 (2022): 4444–58. http://dx.doi.org/10.14704/web/v19i1/web19293.

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A lot of research is being done on autonomous driving vehicles or robots that recognize objects and drive themselves without human intervention. In order to develop autonomous driving technology, there is a fundamental difficulty in securing expensive real cars equipped with various sensors. In this paper, an autonomous driving system development platform was developed using an inexpensive RC-Car, and a test system that can test various algorithms related to autonomous driving was introduced. In the system developed in this study, the single board computer Raspberry PI was mounted on the RC-Ca

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Abhishek Dixit. "Trajectory Data Driven Driving Style Recognition for Autonomous Vehicles Using Unsupervised Clustering." Communications on Applied Nonlinear Analysis 31, no. 6s (2024): 715–23. http://dx.doi.org/10.52783/cana.v31.1314.

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Introduction: This research focuses on enhancing the understanding and classification of vehicle driving styles by analyzing extensive trajectory data. Recognizing and categorizing different driving behaviours is crucial, particularly for the development of autonomous vehicles, which must predict and respond to the diverse actions of human drivers. Understanding these driving styles is essential for improving road safety and ensuring that autonomous systems can navigate mixed traffic environments effectively. Objectives: The primary objective of this study is to classify vehicle driving styles

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Wielitzka, Mark, Tim Ahrenhold, Jonas Schrader, and Jonas Rawitzer. "Smart Driving Dynamics - Ready for Autonomous Driving." ATZelectronics worldwide 20, no. 3-4 (2025): 18–23. https://doi.org/10.1007/s38314-025-1969-2.

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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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M R, Prajwal. "Self-Driving Autonomous Car." International Journal for Research in Applied Science and Engineering Technology 8, no. 8 (2020): 260–63. http://dx.doi.org/10.22214/ijraset.2020.30866.

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Franke, U., D. Gavrila, S. Gorzig, F. Lindner, F. Puetzold, and C. Wohler. "Autonomous driving goes downtown." IEEE Intelligent Systems 13, no. 6 (1998): 40–48. http://dx.doi.org/10.1109/5254.736001.

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Chen, Shu-Ching. "Multimedia for Autonomous Driving." IEEE MultiMedia 26, no. 3 (2019): 5–8. http://dx.doi.org/10.1109/mmul.2019.2935397.

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AL - SMADI, Adnan, Sara AL - ESSA, Shaima MOMANI, and Haneen BANI - SALAMEH. "Vehicle Autonomous Driving System." Eurasia Proceedings of Science Technology Engineering and Mathematics 28 (August 15, 2024): 408–16. http://dx.doi.org/10.55549/epstem.1523618.

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Autonomous vehicles or self-driving cars industry and technology have played an important role in research industry and automotive industry. Autonomous cars are those vehicles have the ability to sense there surrounding and navigate and drive themselves on roads through traffic without human interventions. In other words, they can move from one location to another one without human interaction. In this paper, an autonomous vehicle system prototype is proposed. The vehicle is able to sense its surrounding and keep going on the road on its own through traffic and other barriers such as people an

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Tian, Jilei, Alvin Chin, and Halim Yanikomeroglu. "Connected and Autonomous Driving." IT Professional 20, no. 6 (2018): 31–34. http://dx.doi.org/10.1109/mitp.2018.2876928.

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Reichenbach, Michael. "Autonomous Driving and Digitization." ATZ worldwide 120, no. 1 (2018): 16–17. http://dx.doi.org/10.1007/s38311-017-0175-0.

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47

Perez, Manuel. "Autonomous driving in NMR." Magnetic Resonance in Chemistry 55, no. 1 (2016): 15–21. http://dx.doi.org/10.1002/mrc.4546.

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Hussain, Kamal, Catarina Moreira, João Pereira, Sandra Jardim, and Joaquim Jorge. "A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios." Smart Cities 8, no. 3 (2025): 79. https://doi.org/10.3390/smartcities8030079.

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Autonomous driving technology is advancing rapidly, driven by integrating advanced intelligent systems. Autonomous vehicles typically follow a modular structure, organized into perception, planning, and control components. Unlike previous surveys, which often focus on specific modular system components or single driving environments, our review uniquely compares both settings, highlighting how deep learning and reinforcement learning methods address the challenges specific to each. We present an in-depth analysis of local and global planning methods, including the integration of benchmarks, si

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Arshad, Saba, Muhammad Sualeh, Dohyeong Kim, Dinh Van Nam, and Gon-Woo Kim. "Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment." Sensors 20, no. 18 (2020): 5053. http://dx.doi.org/10.3390/s20185053.

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In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur d

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Aravind, Ravi. "Optimizing ADAS and Autonomous Driving Systems with Advanced Ethernet Protocols and Machine Learning." International Journal of Science and Research (IJSR) 12, no. 10 (2023): 2147–55. http://dx.doi.org/10.21275/es24611084428.

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