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Articles de revues sur le sujet « Autonomous Driving »

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 26 juillet 2025

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1

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

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

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Autonomous vehicles will need de-escalation strategies to compensate when reaching system limitations. Car-driver handovers can be considered one possible method to deal with system boundaries. The authors suggest a bimodal (auditory and visual) handover assistant based on user preferences and design principles for automated systems. They conducted a driving simulator study with 30 participants to investigate the take-over performance of drivers. In particular, the authors examined the effect of different warning conditions (take-over request only with 4 and 6 seconds time budget vs. an additi

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Fuchs, Andreas. "Autonomous Driving." ATZoffhighway worldwide 11, no. 1 (2018): 3. http://dx.doi.org/10.1007/s41321-018-0013-3.

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

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

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To address the problems of inefficient agricultural production and labor shortages, there has been active research to develop autonomously driven agricultural machines, using advanced sensors and ICT technology. Autonomously driven speed sprayers can also reduce accidents such as the pesticide poisoning of farmers, and vehicle overturn that frequently occur during spraying work in orchards. To develop a commercial, autonomously driven speed sprayer, we developed a prototype of an autonomously driven agricultural vehicle, and conducted performance evaluations in an orchard environment. A protot

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Yang, Liangjun. "Autonomous Driving Control Strategy Based on Deep Reinforcement Learning." Applied and Computational Engineering 128, no. 1 (2025): 79–85. https://doi.org/10.54254/2755-2721/2025.20209.

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This paper discusses an autonomous driving control strategy based on Deep Reinforcement Learning (DRL), which aims to improve the decision-making ability of autonomous driving system in complex traffic environments. Deep reinforcement learning has a wide range of applications in many fields, such as robotics and medicine. Autonomous driving has emerged as a significant research focus in recent years. By combining deep learning and reinforcement learning, the model is able to autonomously learn and optimize driving behavior under dynamically changing road conditions. The DRL-based control strat

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Köster, Oliver. "Mandatory Autonomous Driving?" ATZelectronics worldwide 14, no. 3 (2019): 66. http://dx.doi.org/10.1007/s38314-019-0016-6.

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

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

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This paper aims to explore the current status and future development trends of artificial intelligence technology in the field of autonomous driving. By analyzing the application of artificial intelligence technologies such as computer vision, deep learning and reinforcement learning in autonomous driving, this paper shows that autonomous driving is currently a hot topic in society. At present, L2 and L3 autonomous driving systems have been launched. In the future, autonomous driving may develop in the direction of vehicle‒road collaboration and L4 unmanned delivery. In addition, we still face

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STAYTON, ERIK, MELISSA CEFKIN, and JINGYI ZHANG. "Autonomous Individuals in Autonomous Vehicles: The Multiple Autonomies of Self-Driving Cars." Ethnographic Praxis in Industry Conference Proceedings 2017, no. 1 (2017): 92–110. http://dx.doi.org/10.1111/1559-8918.2017.01140.

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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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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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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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Steck, Felix, Viktoriya Kolarova, Francisco Bahamonde-Birke, Stefan Trommer, and Barbara Lenz. "How Autonomous Driving May Affect the Value of Travel Time Savings for Commuting." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 46 (2018): 11–20. http://dx.doi.org/10.1177/0361198118757980.

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Autonomous driving is being discussed as a promising solution for transportation-related issues and might bring some improvement for users of the system. For instance, especially high mileage commuters might compensate for some of their time spent traveling as they will be able to undertake other activities while going to work. At the same time, there are still many uncertainties and little empirical data on the impact of autonomous driving on mode choices. This study addresses the impact of autonomous driving on value of travel time savings (VTTS) and mode choices for commuting trips using st

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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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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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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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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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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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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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Jain, Kavita, Sean Pereira, Vrishab Shetty, Lysandra D’Souza, and Akash Mathew. "Autonomous Vehicle." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1363–69. http://dx.doi.org/10.22214/ijraset.2023.56780.

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Abstract: Self-driving cars are a rapidly advancing technology that is poised to revolutionize the way people travel. The purpose of the self-driving car project is to create a vehicle that can operate autonomously, without the need for a human driver. This technology is being developed to increase safety on the road, reduce traffic congestion, and provide a more efficient and convenient mode of transportation. The project involves a combination of hardware and software development, including sensors, cameras, and machine learning algorithms. Self-driving cars have the potential to reduce the

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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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Kolarova, Viktoriya. "Exploring the Elements and Determinants of the Value of Time for Manual Driving and Autonomous Driving using a Qualitative Approach." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 12 (2020): 542–52. http://dx.doi.org/10.1177/0361198120953770.

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Autonomous driving is expected to change individual travel behavior significantly. The main reason postulated is an increase in comfort and feasibility of on-board activities which will potentially change the way people perceive time spent in a vehicle and consequently their mode preferences. Understanding how value of time (VoT) might change and what will determine such change can be crucial when assessing the impact of vehicle automation. Recent studies address potential changes that automation might have on VoT based on analyses of time use and perception in current modes of transport or fo

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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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Han, Joong-hee, Chi-ho Park, and Young Yoon Jang. "Development of Location-Data-Based Orchard Passage Map Generation Method." Sensors 24, no. 3 (2024): 795. http://dx.doi.org/10.3390/s24030795.

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Currently, pest control work using speed sprayers results in increasing numbers of safety accidents such as worker pesticide poisoning and rollover of vehicles during work. To address this, there is growing interest in autonomous driving technology for speed sprayers. To commercialize and rapidly expand the use of self-driving speed sprayers, an economically efficient self-driving speed sprayer using a minimum number of sensors is essential. This study developed an orchard passage map using location data acquired from positioning sensors to generate autonomous driving paths, without installing

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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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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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Zhang, Tingyu. "The application of deep learning in autonomous driving." Applied and Computational Engineering 50, no. 1 (2024): 144–48. http://dx.doi.org/10.54254/2755-2721/50/20241379.

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Autonomous driving technology is currently a globally prominent subject, with its relevance and impact readily apparent. Leveraging advanced sensors, sophisticated algorithms, and state-of-the-art computer vision techniques, autonomous vehicles can autonomously navigate, mitigate traffic accidents, and alleviate urban congestion. Furthermore, this technology is poised to accelerate innovation within the automotive sector, drive industrial advancement, and enhance people's convenience and safety in their travel experiences. The importance of autonomous driving technology is not only reflected i

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Chen, Yuanzhe. "World models for autonomous driving." Applied and Computational Engineering 75, no. 1 (2024): 14–18. http://dx.doi.org/10.54254/2755-2721/75/20240500.

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Advancements in autonomous driving have been achieved due to the increasing use of artificial intelligence. The existing autonomous driving technology is insufficient to handle intricate traffic conditions. In autonomous driving, the world model is a crucial and innovative technology. Utilizing sensors and pre-existing knowledge, a world model can enhance the autonomous driving system's comprehension of the surroundings, offer essential data for future judgments, and enhance the system's resilience. The paper employs literature analysis and review methods to investigate the research and implem

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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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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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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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Yang, Diange, Xinyu Jiao, Kun Jiang, and Zhong Cao. "Driving Space for Autonomous Vehicles." Automotive Innovation 2, no. 4 (2019): 241–53. http://dx.doi.org/10.1007/s42154-019-00081-1.

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AbstractDriving space for autonomous vehicles (AVs) is a simplified representation of real driving environments that helps facilitate driving decision processes. Existing literatures present numerous methods for constructing driving spaces, which is a fundamental step in AV development. This study reviews the existing researches to gain a more systematic understanding of driving space and focuses on two questions: how to reconstruct the driving environment, and how to make driving decisions within the constructed driving space. Furthermore, the advantages and disadvantages of different types o

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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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Rößger, Peter. "Autonomous Driving How Much Autonomy Driving Does Stand." ATZelektronik worldwide 10, no. 2 (2015): 26–29. http://dx.doi.org/10.1007/s38314-015-0514-0.

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Jin, Cance. "Artificial intelligence and autonomous driving." Applied and Computational Engineering 20, no. 1 (2023): 165–69. http://dx.doi.org/10.54254/2755-2721/20/20231092.

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With the rapid development of science and technology, artificial intelligence and autonomous driving technology are becoming a hot topic in today's society. The integration of artificial intelligence and driving systems satisfies this requirement effectively and produces a new driving technology for the sake of traffic safety and a better driving experience. To increase driving safety and comfort, computer calculations are utilized to aid the driver or eliminate the interference of human variables. First, this study introduce the basic concepts and principles of artificial intelligence and aut

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Zhao, Shixin, and Feng Pan. "Research Status of End-or-End Autonomous Driving Technology." Computer Life 12, no. 1 (2024): 21–23. http://dx.doi.org/10.54097/r64m6026.

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With the continuous development of autonomous driving technology, it holds significant potential in reducing the risk of traffic accidents, alleviating traffic congestion, and im-proving traffic efficiency. Traditional autonomous driving systems employ a modular deployment strategy, dividing the development into separate modules for perception, decision-making, planning, and control, which are then integrated into the vehicle. Currently, end-to-end autonomous driving methods have emerged as a research trend in the field of autonomous driving. This approach directly maps input data from the per

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Bai, Xinyan, Hongbing Chen, and Xintong Jiang. "Research on Market Prospect of High-Level Automatic Driving." BCP Business & Management 27 (September 6, 2022): 130–41. http://dx.doi.org/10.54691/bcpbm.v27i.1958.

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Autonomous driving is considered the future application in the world, but its market is still unknown. This article first examines the economic context in which autonomous driving occurs and uses statistical data to find that most of the population is receptive to autonomous driving as new and unconventional technology. This is good for the autonomous driving market. However, the survey indicates that there are still some threats to autonomous driving systems. Firstly, there are legal restrictions, and secondly, there are bottlenecks in path planning technology. It is also worth mentioning tha

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Ma, Xiaoyi, Xiaowei Hu, Dieter Schramm, and Shufen Wang. "Effects of Anthropomorphic Driving Vehicles on Traffic Flow." Applied Sciences 15, no. 2 (2025): 802. https://doi.org/10.3390/app15020802.

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Autonomous driving has many positive impacts, such as improving driver and passenger safety, comfort, and traffic efficiency, but all these advantages are based on people’s trust and acceptance of this mode of driving. Anthropomorphic driving can enhance the trust and comfort of drivers and passengers and is seen as a feasible measure to increase people’s acceptance of autonomous driving. This paper reports the microscopic traffic simulation of three scenarios around a frequently congested intersection, using non-automated vehicles, autonomous driving vehicles, and anthropomorphic driving vehi

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Xiang, Dantong. "Reinforcement learning in autonomous driving." Applied and Computational Engineering 48, no. 1 (2024): 17–23. http://dx.doi.org/10.54254/2755-2721/48/20241072.

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Automatic driving technology has become a highly researched field in recent years, aiming to achieve vehicle driving without human intervention. In this regard, reinforcement learning techniques have played a crucial role. This study discusses and analyses the use of reinforcement learning in automatic driving methods. The research begins with the process of reinforcement learning. In the architectural framework, there is a special emphasis on designing innovative reward functions to encourage safe and socially acceptable driving behaviour, while considering uncertainty factors through advance

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Yan, Lixin, Tao Zeng, Guangyang Deng, and Qiujie Xu. "Development Status and Hotspot Visualized Analysis of Autonomous Vehicles Based on CiteSpace." Journal of Advanced Transportation 2022 (March 31, 2022): 1–15. http://dx.doi.org/10.1155/2022/2116814.

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The safety of autonomous driving has been a constant concern, with autonomous driving becoming a research focus in the world today. This study collects the literature in the field of autonomous driving in the Web of Science core data collection from 2005 to 2020. The research status, hot research topics, and the development trend of autonomous driving are reviewed based on CiteSpace from the aspects of number of articles, national collaboration, institutions, scholars, research areas, literature co-citation, keywords, and trends. The results show that the quantitative development of autonomous

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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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Costa, Gonçalo, José Cecílio, and António Casimiro. "Cooperative Autonomous Driving in Simulation." ACM SIGAda Ada Letters 43, no. 1 (2023): 78–82. http://dx.doi.org/10.1145/3631483.3631496.

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Autonomous driving is an area that has been growing in recent years. However, cars are unprepared to cooperate with others nearby, wasting resources and computational power. Thus, cooperative autonomous driving reveals its importance in the future. In this work-in-progress paper, we define, implement and test an architecture for a simulation environment where cooperative autonomous driving protocols can be tested. Additionally, a Manoeuvre Negotiation Protocol is implemented. This protocol will make an existing autonomous driving (AD) stack more resilient in real driving scenarios, improving i

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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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