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Articles de revues sur le sujet « Autonomous Driving Systems »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 19 juillet 2025

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1

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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Lee, Heung-Gu, Dong-Hyun Kang, and Deok-Hwan Kim. "Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles." Electronics 10, no. 19 (2021): 2405. http://dx.doi.org/10.3390/electronics10192405.

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Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver’s situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver’s situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is

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Li, Jing, Jingyuan Li, Guo Yang, Lie Yang, Haozhuang Chi, and Lichao Yang. "Applications of Large Language Models and Multimodal Large Models in Autonomous Driving: A Comprehensive Review." Drones 9, no. 4 (2025): 238. https://doi.org/10.3390/drones9040238.

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The rapid development of large language models (LLMs) and multimodal large models (MLMs) has introduced transformative opportunities for autonomous driving systems. These advanced models provide robust support for the realization of more intelligent, safer, and efficient autonomous driving. In this paper, we present a systematic review on the integration of LLMs and MLMs in autonomous driving systems. First, we provide an overview of the evolution of LLMs and MLMs, along with a detailed analysis of the architecture of autonomous driving systems. Next, we explore the applications of LLMs and ML

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Mu, Juncheng, Linglin Zhou, and Chun Yang. "Research on the Behavior Influence Mechanism of Users’ Continuous Usage of Autonomous Driving Systems Based on the Extended Technology Acceptance Model and External Factors." Sustainability 16, no. 22 (2024): 9696. http://dx.doi.org/10.3390/su16229696.

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In recent years, with the advancement of urbanization and the increase in traffic congestion, the demand for autonomous driving has been steadily growing in order to promote sustainable urban development. The evolution of automotive autonomous driving systems significantly influences the progress of sustainable urban development. As these systems advance, user evaluations of their performance vary widely. Autonomous driving systems present both technological advantages and controversies, along with challenges. To foster the development of autonomous driving systems and facilitate transformativ

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LIM, Kyung-Il. "Fifth-Generation Technology in Autonomous Driving Systems." Physics and High Technology 29, no. 3 (2020): 21–26. http://dx.doi.org/10.3938/phit.29.009.

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Blasinski, Henryk, Joyce Farrell, Trisha Lian, Zhenyi Liu, and Brian Wandell. "Optimizing Image Acquisition Systems for Autonomous Driving." Electronic Imaging 2018, no. 5 (2018): 161–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.05.pmii-161.

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Bhat, Anand, Shunsuke Aoki, and Ragunathan Rajkumar. "Tools and Methodologies for Autonomous Driving Systems." Proceedings of the IEEE 106, no. 9 (2018): 1700–1716. http://dx.doi.org/10.1109/jproc.2018.2841339.

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Vitas, Dijana, Martina Tomic, and Matko Burul. "Traffic Light Detection in Autonomous Driving Systems." IEEE Consumer Electronics Magazine 9, no. 4 (2020): 90–96. http://dx.doi.org/10.1109/mce.2020.2969156.

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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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Kong, Boyuan. "A survey on the use of artificial intelligence in autonomous driving." Highlights in Science, Engineering and Technology 124 (February 18, 2025): 146–51. https://doi.org/10.54097/v0gjay68.

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Autonomous driving and artificial intelligence are the most popular research projects in the field of technology today. As the high technology, autonomous driving relies on perception, decision-making, and control systems, and the performance of these systems largely depends on the application of artificial intelligence nowadays. Fortunately, there are plenty of applications of artificial intelligence in several aspects of autonomous driving. This paper aims to introduce the relationship between autonomous driving and artificial intelligence by reviewing several literatures and analyzing the a

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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, Hao. "Review of Autonomous Driving Technology in Intelligent Transportation Systems." Theoretical and Natural Science 83, no. 1 (2025): 20–26. https://doi.org/10.54254/2753-8818/2025.19925.

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The main problems in transportation are traffic accidents, increasingly slow traffic flow, and pollution. It requires huge infrastructure investments in traditional transportation systems to solve. The advent of autonomous driving techniques with intelligent transportation systems (ITS) can overcome these problems. This paper investigates the integration of autonomous driving technology with intelligent transportation systems (ITS) and explores the latest case studies and research findings on this integration. The purpose is to emphasize the crucial role of merging autonomous driving technolog

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Zhai, Haoan. "Intelligent vehicle navigation systems and autonomous driving technology: A comprehensive analysis." Applied and Computational Engineering 41, no. 1 (2024): 119–23. http://dx.doi.org/10.54254/2755-2721/41/20230725.

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This paper conducts a comprehensive study and analysis of intelligent vehicle navigation systems and autonomous driving technology. We review the historical development of autonomous driving technology, discuss key concepts such as perception, decision-making, and control, explore various types of autonomous vehicles, and examine various aspects of intelligent vehicle navigation systems. Additionally, we investigate safety, reliability, legal frameworks in the field of autonomous driving, as well as future trends and ethical considerations. Finally, we summarize the main findings of the resear

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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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Echenim, Jennifer Ifeoma. "Integration of Artificial Intelligence and Blockchain for Intelligent Autonomous Systems." International Journal of Future Engineering Innovations 2, no. 3 (2025): 31–37. https://doi.org/10.54660/ijfei.2025.2.3.31-37.

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The integration of Artificial Intelligence (AI) and Blockchain offers a transformative approach to the development of Intelligent Autonomous Systems (IAS). Autonomous systems, which include self-driving cars, drones, and robots, require advanced decision-making, real-time data processing, and secure communication. AI provides the intelligence necessary for these systems to operate autonomously, while Blockchain introduces decentralized control, transparency, and enhanced security. This paper explores the potential of combining AI and Blockchain technologies to create more secure, transparent,

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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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Sivaparvathi, Mandalapu, Dr J. Dillibabu, Nelaturi Sandhya Rani, and Shaik Reshma. "Vehicular Ad-Hoc Networks (VANETs) for Autonomous Driving Systems." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2087–93. https://doi.org/10.22214/ijraset.2025.70672.

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Abstract: Vehicular Ad-Hoc Networks (VANETs) represent a cornerstone technology in the advancement of autonomous driving systems. By enabling vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, VANETs facilitate real-time data sharing essential for the dynamic decision-making required in autonomous navigation. This paper investigates the architecture, protocols, and applications of VANETs, particularly focusing on their integration into autonomous driving systems. Through a review of literature, case studies, and analysis of current methodologies, the research identifie

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Mohammed, Dilshad, and Balázs Horváth. "Steady-Speed Traffic Capacity Analysis for Autonomous and Human-Driven Vehicles." Applied Sciences 14, no. 1 (2023): 337. http://dx.doi.org/10.3390/app14010337.

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As the automotive industry transitions towards the era of autonomous vehicles, it is imperative to assess and compare the following distances maintained by vehicles equipped with adaptive cruise control (ACC) systems against those of traditional human-driven vehicles. This study aims to provide insights into the future use of autonomous vehicles by empirically examining the following distances achieved under different driving conditions. Controlled experiments were conducted using three vehicles equipped with various types of ACC sensors, and comparable scenarios were replicated with human dri

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Zhang, Yuyang, Junliang Feng, Mingni Huang, Qinghao Liu, and Wenxuan Fu. "Development and Key Technologies of the Cognition system of Autonomous Driving." Highlights in Science, Engineering and Technology 56 (July 14, 2023): 685–96. http://dx.doi.org/10.54097/hset.v56i.10835.

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With the rapid development of automotive electronics technology and artificial intelligence, autonomous driving has become an important solution for future transportation challenges. As an integral component of autonomous driving systems, the intelligent cognitive system has emerged as a prominent area of technological research. This paper focuses on the development and key technologies of autonomous driving cognitive systems, based on published literature. The architecture of cognitive systems for autonomous driving is typically divided into two subsystems: the environmental perception system

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Gan, Haofeng. "Integrating Multi-Sensor Fusion, AI, and 5G Communication for Advancing Autonomous Driving and Overcoming Key Challenges." Highlights in Science, Engineering and Technology 124 (February 18, 2025): 1–6. https://doi.org/10.54097/hdhjkf76.

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Autonomous driving technology has evolved significantly, transitioning from early experimental stages to a central pillar of future transportation systems. Initially driven by advancements in computer vision and robotics, autonomous vehicles (AVs) are now on the brink of revolutionizing urban mobility, enhancing road safety, and improving traffic efficiency through the integration of sophisticated AI, multi-sensor fusion, and advanced communication networks. This paper explores the development and key components of autonomous driving, including multi-sensor fusion, intelligent power systems, v

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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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Lu, Jiayi, Shichun Yang, Yuan Ma, et al. "Modeling Driver’s Real-Time Confidence in Autonomous Vehicles." Applied Sciences 13, no. 7 (2023): 4099. http://dx.doi.org/10.3390/app13074099.

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Autonomous vehicle technology has developed at an unprecedented rate in recent years. An increasing number of vehicles are equipped with different levels of driving assist systems to reduce the human driver’s burden. However, because of the conservative design of its programming framework, there is still a large gap between the performance of current autonomous driving systems and experienced veteran drivers. This gap can cause drivers to distrust decisions or behaviors made by autonomous vehicles, thus affecting the effectiveness of drivers’ use of auto-driving systems. To further estimate th

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Lee, Joey, Benedikt Groß, and Raphael Reimann. "Who wants to be a self-driving car?" Information Design Journal 25, no. 1 (2019): 21–27. http://dx.doi.org/10.1075/idj.25.1.02lee.

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Abstract Self-driving cars and autonomous transportation systems are projected to create radical societal changes, yet public understanding and trust of self-driving cars and autonomous systems is limited. The authors present a new mixed-reality experience designed to provide its users with insights into the ways that self-driving cars operate. A single-person vehicle equipped with sensors provides its users with data driven visual feedback in a virtual reality headset to navigate in physical space. The authors explore how immersive experiences might provide ‘conceptual affordances’ that lower

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Zhang, Yixin, and Ming Liu. "Risk Prediction and Safety Driving in Automated Driving: A Review from the Perspective of Embedded Systems." Applied and Computational Engineering 149, no. 1 (2025): 209–20. https://doi.org/10.54254/2755-2721/2025.kl22728.

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In recent years, automated driving technology is booming and reshaping the pattern of transportation, but its safety has attracted wide attention. In the embedded system, it is necessary to solve the security risks caused by sensor failures, complex road environments and emergencies. To enhance autonomous driving safety, risk detection, a crucial part, must accurately spot potential hazards. This review focuses on the risk detection and safe driving field of autonomous driving, and analyzes related cutting-edge technologies in depth. In terms of risk prediction, it explains the key technology

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Oliveira, Luis, Karl Proctor, Christopher G. Burns, and Stewart Birrell. "Driving Style: How Should an Automated Vehicle Behave?" Information 10, no. 6 (2019): 219. http://dx.doi.org/10.3390/info10060219.

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This article reports on a study to investigate how the driving behaviour of autonomous vehicles influences trust and acceptance. Two different designs were presented to two groups of participants (n = 22/21), using actual autonomously driving vehicles. The first was a vehicle programmed to drive similarly to a human, “peeking” when approaching road junctions as if it was looking before proceeding. The second design had a vehicle programmed to convey the impression that it was communicating with other vehicles and infrastructure and “knew” if the junction was clear so could proceed without ever

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Li, Yuze. "A Review of Research on Deep Learning-based Target Detection Technology for Automated Vehicle Driving Systems." Highlights in Science, Engineering and Technology 27 (December 27, 2022): 19–24. http://dx.doi.org/10.54097/hset.v27i.3716.

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In recent years, with the rapid development of artificial intelligence technology, major technology companies around the world have strategically shifted their attention to the field of autonomous driving, momentarily pushing the research on autonomous driving to a climax. Target detection is one of the core technologies in the field of autonomous driving. For this reason, this paper provides a research review on driverless technology, deep learning target detection algorithms, and briefly summarizes the difficulties faced by autonomous driving target detection, and then introduces five common

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Panarin, Oleg, and Igor Zacharov. "Monitoring Mobile Information Processing Systems." Russian Digital Libraries Journal 23, no. 4 (2020): 835–47. http://dx.doi.org/10.26907/1562-5419-2020-23-4-835-847.

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We describe the implementation of the monitoring for the IT systems at the core of the autonomous driving vehicle. The role of the monitoring is to assist in decision to start the driving cycle and continuous assessment for the fitness to drive the vehicle. The requirements for the monitoring system with the increased resiliency and data replication make it sufficiently different from standard monitoring systems and warrant a unique implementation tuned for the autonomous driving requirements. The monitoring system combines the OS events and real-time measurements of sensor data. The informati

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Vadaszffy, Karl. "Systems Specialists." Electric and Hybrid Vehicle Technology International 2018, no. 1 (2018): 155–56. http://dx.doi.org/10.12968/s1467-5560(22)60338-5.

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Bhat, Shaman, and Ashwin Kavasseri. "Multi-Source Data Integration for Navigation in GPS-Denied Autonomous Driving Environments." International Journal of Electrical and Electronics Research 12, no. 3 (2024): 863–69. http://dx.doi.org/10.37391/ijeer.120317.

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Autonomous driving is making rapid advances, and the future of driverless cars is close to fruition. The biggest hurdle for autonomous driving currently is the reliability and dependability of navigation systems. Navigation systems are predominantly based on GPS signals and despite it being highly available there are scenarios where GPS is either not present or unavailable such as in tunnels, indoor environments, and urban areas with high signal interference. This paper proposes an adaptive decision-making algorithm that leverages multi source data source integration for navigation in GPS-deni

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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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Xu, Juncheng. "SLAM Technology in Autonomous Driving." Compendium of Engineering 工程學輯要 1, no. 1 (2025): 11. https://doi.org/10.63313/engineering.8002.

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With the rapid development of transportation systems, autonomous driving technology has emerged, in which Simultaneous Localization and Mapping (SLAM) plays a crucial role. To gain an in-depth understanding of SLAM technology in autonomous driving, this paper provides a comprehensive review of its research progress and applications. First, the fundamental princi-ples of SLAM are introduced, and SLAM techniques are categorized into different types. Next, the practical applications of SLAM in autonomous driving are analyzed, along with a de-tailed exami-nation of the advantages and disadvantages

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Almusawi, Ali, Mustafa Albdairi, and Syed Shah Sultan Mohiuddin Qadri. "Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control." Applied Sciences 14, no. 19 (2024): 8851. http://dx.doi.org/10.3390/app14198851.

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The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various au

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Islam, Md Mafiqul. "Autonomous Systems Revolution: Exploring the Future of Self-Driving Technology." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 16–23. http://dx.doi.org/10.60087/jaigs.v3i1.61.

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The rise of autonomous systems, particularly in the realm of self-driving technology, heralds a transformative era in transportation and beyond. This paper delves into the multifaceted landscape of autonomous systems, examining their evolution, current state, and future potential. By exploring the intricate workings of self-driving technology, we unravel the complexities and implications of this burgeoning field. From the underlying algorithms to the societal impacts, we navigate through the promises and challenges of autonomous systems. Through a comprehensive analysis, we shed light on the t

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Buzdugan, Ioana-Diana, Silviu Butnariu, Ioana-Alexandra Roșu, Andrei-Cristian Pridie, and Csaba Antonya. "Personalized Driving Styles in Safety-Critical Scenarios for Autonomous Vehicles: An Approach Using Driver-in-the-Loop Simulations." Vehicles 5, no. 3 (2023): 1149–66. http://dx.doi.org/10.3390/vehicles5030064.

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This paper explores the use of driver-in-the-loop simulations to detect personalized driving styles in autonomous vehicles. The driving simulator used in this study is modular and adaptable, allowing for the testing and validation of control and data-collecting systems, as well as the incorporation and proof of car models. The selected scenario is a double lane change maneuver to overtake a stationary obstacle at a relatively high speed. The user’s behavior was recorded, and lateral accelerations during the maneuver were used as criteria to compare the user-driven vehicle and the autonomous on

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Wang, Baoming, Han Lei, Zuwei Shui, Zhou Chen, and Peiyuan Yang. "Current State of Autonomous Driving Applications Based on Distributed Perception and Decision-Making." World Journal of Innovation and Modern Technology 7, no. 3 (2024): 15–22. http://dx.doi.org/10.53469/wjimt.2024.07(03).03.

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This article reviews the key role of distributed cloud architecture in autonomous driving systems and its integration with intelligent computing networks. By spreading computing resources across multiple geographic locations, the distributed cloud enables localized processing and storage of data, reducing latency and improving real-time decision making in autonomous vehicles. The article points out that the combination of distributed cloud technology and intelligent computing network provides a powerful solution to meet the challenges of autonomous driving technology. By dynamically allocating

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Nine, Julkar. "Towards Autonomous Driving Using Vision Based Intelligent Systems." Embedded Selforganising Systems 8, no. 2 (2021): 1–2. http://dx.doi.org/10.14464/ess.v8i2.496.

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Vision Based systems have become an integral part when it comes to autonomous driving. The autonomous industry has seen a made large progress in the perception of environment as a result of the improvements done towards vision based systems. As the industry moves up the ladder of automation, safety features are coming more and more into the focus. Different safety measurements have to be taken into consideration based on different driving situations. One of the major concerns of the highest level of autonomy is to obtain the ability of understanding both internal and external situations. Most

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Zhao, Ruitian. "Application of image sensors in autonomous driving systems." IET Conference Proceedings 2024, no. 19 (2025): 647–50. https://doi.org/10.1049/icp.2024.4059.

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Minnerup, Pascal, David Lenz, Tobias Kessler, and Alois Knoll. "Debugging Autonomous Driving Systems Using Serialized Software Components." IFAC-PapersOnLine 49, no. 15 (2016): 44–49. http://dx.doi.org/10.1016/j.ifacol.2016.07.612.

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Wang, Renzhi, Mingfei Cheng, Xiaofei Xie, Yuan Zhou, and Lei Ma. "MoDitector: Module-Directed Testing for Autonomous Driving Systems." Proceedings of the ACM on Software Engineering 2, ISSTA (2025): 137–58. https://doi.org/10.1145/3728876.

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Testing Autonomous Driving Systems (ADSs) is crucial for ensuring their safety, reliability, and performance. Despite numerous testing methods available that can generate diverse and challenging scenarios to uncover potential vulnerabilities, these methods often treat ADS as a black-box, primarily focusing on identifying system-level failures like collisions or near-misses without pinpointing the specific modules responsible for these failures. This lack of root causes understanding for the failures hinders effective debugging and subsequent system repair. Furthermore, current approaches often

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Zhan, Hao, and Dan Wan. "Ethical Considerations of the Trolley Problem in Autonomous Driving: A Philosophical and Technological Analysis." World Electric Vehicle Journal 15, no. 9 (2024): 404. http://dx.doi.org/10.3390/wevj15090404.

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The trolley problem has long posed a complex ethical challenge in the field of autonomous driving technology. By constructing a general trolley problem model, this paper demonstrates that the default loss assumption is a necessary condition for the occurrence of trolley problems. However, an analysis of the differences between classical trolley problems and autonomous driving scenarios reveals that this assumption is not supported in the design of autonomous driving systems. This paper first provides a detailed definition of the trolley problem within the context of autonomous driving technolo

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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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