An autonomous vehicle, also known as a self-driving car, is a vehicle capable of sensing its environment and operating without human involvement. It uses a variety of sensors, cameras, lidar, radar, AI, and machine learning algorithms to perceive its surroundings, make decisions, and navigate roads safely.
Researchers explored the application of distributed learning, particularly Federated Learning (FL), for Internet of Things (IoT) services in the context of emerging 6G networks. They discussed the advantages and challenges of distributed learning in IoT domains, emphasizing its potential for enhancing IoT services while addressing privacy concerns and the need for ongoing research in areas such as security and communication efficiency.
This study introduces a novel approach to autonomous vehicle navigation by leveraging machine vision, machine learning, and artificial intelligence. The research demonstrates that it's possible for vehicles to navigate unmarked roads using economical webcam-based sensing systems and deep learning, offering practical insights into enhancing autonomous driving in real-world scenarios.
This research presents a novel bi-level programming model aimed at improving Transit Signal Priority (TSP) systems to reduce delays for private vehicles. By considering both public and private transportation, utilizing a game theory approach and genetic algorithms, the study offers a comprehensive solution for optimizing urban traffic flow.
Researchers present MGB-YOLO, an advanced deep learning model designed for real-time road manhole cover detection. Through a combination of MobileNet-V3, GAM, and BottleneckCSP, this model offers superior precision and computational efficiency compared to existing methods, with promising applications in traffic safety and infrastructure maintenance.
Researchers have developed a groundbreaking framework for training privacy-preserving models that anonymize license plates and faces captured on fisheye camera images used in autonomous vehicles. This innovation addresses growing data privacy concerns and ensures compliance with data protection regulations while improving the adaptability of models for fisheye data.
Researchers introduce NUMERLA, an algorithm that combines meta-reinforcement learning and symbolic logic-based constraints to enable real-time policy adjustments for self-driving cars while maintaining safety. Experiments in simulated urban driving scenarios demonstrate NUMERLA's ability to handle varying traffic conditions and unpredictable pedestrians, highlighting its potential to enhance the development of safe and adaptable autonomous vehicles.
Researchers present an AI-driven solution for autonomous cars, leveraging neural networks and computer vision algorithms to achieve successful autonomous driving in a simulated environment and real-world competition, marking a significant step toward safer and efficient self-driving technology.
Researchers explore the innovative concept of Qualitative eXplainable Graphs (QXGs) for spatiotemporal reasoning in automated driving scenes. Learn how QXGs efficiently capture complex relationships, enhance transparency, and contribute to the trustworthy development of autonomous vehicles. This groundbreaking approach revolutionizes automated driving interpretation and sets a new standard for dependable AI systems.
Researchers delve into the vulnerabilities of machine learning (ML) systems, specifically concerning adversarial attacks. Despite the remarkable strides made by deep learning in various tasks, this study uncovers how ML models are susceptible to adversarial examples—subtle input modifications that mislead models' predictions. The research emphasizes the critical need for understanding these vulnerabilities as ML systems are increasingly integrated into real-world applications.
Researchers have introduced a groundbreaking solution, the Class Attention Map-Based Flare Removal Network (CAM-FRN), to tackle the challenge of lens flare artifacts in autonomous driving scenarios. This innovative approach leverages computer vision and artificial intelligence technologies to accurately detect and remove lens flare, significantly improving object detection and semantic segmentation accuracy.
Video-FocalNets present an innovative architecture that combines the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for efficient and accurate video action recognition. By leveraging the spatio-temporal focal modulation technique, Video-FocalNets capture both local and global contexts, offering superior performance and computational efficiency compared to previous methods.
A groundbreaking mathematical model, the FSTSP-DR-MP, has been proposed to transform last-mile logistics into a more sustainable and efficient process. With the surge in online shopping and the subsequent rise in carbon emissions, this innovative approach integrates both delivery and return services using a combination of trucks and drones. The model optimizes routes, considering multiple payloads and customers, to minimize service time.
The study demonstrates the use of text mining to identify emerging ML/AI technologies in the Korean semiconductor industry, enabling SMEs to establish an R&D roadmap and enhance competitiveness. Deep neural networks and AI technology applications in semiconductor R&D and manufacturing processes were found to be crucial, with potential for improved reasoning, learning abilities, and process optimization.
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