Latest Publications on Autonomous Vehicles
This is our latest selection of worldwide publications on Autonomous Vehicles, between many scientific online journals, classified and focused on autonomous vehicle, self-driving, LIDAR, road mapping, fleet management, navigation algorithm and path planning.
Tip: Further to this selection on Autonomous Vehicles, you can search and filter our > publication database < by author, topic, keywords, date or journal.
Research on Mobile Robot Path Planning Based on MSIAR-GWO Algorithm
On 2025-02-01 by Danfeng Chen, Junlang Liu, Tengyun Li, Jun He, Yong Chen, Wenbo Zhu @MDPI
Abstract: Path planning is of great research significance as it is key to affecting the efficiency and safety of mobile robot autonomous navigation task execution. The traditional gray wolf optimization algorithm is widely used in the field of path planning due to its simple structure, few parameters, and easy implementation, but the algorithm still suffers from the disadvantages of slow convergence, ease of falling into the local optimum, and difficulty in effectively balancing exploration and exploitati[...]
AI summary: Research on Mobile Robot Path Planning Based on MSIAR-GWO Algorithm. MSIAR-GWO algorithm proposed for path planning optimization. Introduces nonlinear convergence factor and adaptive position update strategy. Outperforms other swarm intelligence algorithms in solution accuracy and convergence speed.
mobile robot, path planning, optimization algorithm, MSIAR-GWO, reinforcement learning
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A Novel Technique for Drone Path Planning Based on a Neighborhood Dragonfly Algorithm
On 2025-01-31 by Sameer Agrawal, Bhumeshwar K. Patle, Sudarshan Sanap @MDPI
Abstract: Autonomous aerial drone navigation is a rapidly growing topic of research due to its vast application in various indoor applications, including surveillance, search and rescue missions, and environmental monitoring. Current research focuses on the implementation of neighborhood dragonfly algorithms (NDAs) for path planning for single and multiple drones in various indoor environments containing stationary and moving obstacles. The collaborative behavior of dragonflies is a key concept in the cur[...]
AI summary: Rapid growth in research on drone navigation using NDA, collaborative behavior of dragonflies aids in exploring solution space efficiently, close agreement between simulation and experimental results, NDA outperforms other algorithms in smooth path planning and optimization.
drone path planning, neighborhood dragonfly algorithm, autonomous aerial navigation, indoor environments, simulation validation
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Microscopic Simulation of Heterogeneous Traffic Flow on Multi-Lane Ring Roads and Highways
On 2025-01-31 by Haizhen Li, Yongfeng Ju @MDPI
Abstract: In the connected and autonomous vehicle (CAV) environment, vehicles with different levels of automation are being deployed on public roads. Most research focuses on traffic flow simulation for a single vehicle type, while there are few studies on the interactions of mixed traffic involving CAVs, autonomous vehicles (AVs), and human-driven vehicles (HDVs). To fill this gap, this study investigates the traffic performance of heterogeneous traffic on multi-lane ring roads and highways with on-ramps[...]
AI summary: Investigating traffic performance of mixed traffic on multi-lane roads and highways. Optimizing interactions with JAD strategy. Assessing impact on efficiency, dynamics, safety, and environment. Advancing understanding of mixed traffic scenarios.
simulation, heterogeneous traffic flow, connected and autonomous vehicles, traffic efficiency, traffic safety
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Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition
On 2025-01-30 by Ramón Alberto Mena-Almonte, Ekaitz Zulueta, Ismael Etxeberria-Agiriano, Unai Fernandez-Gamiz @MDPI
Abstract: This paper introduces an efficient localization algorithm for robotic systems, utilizing deep learning to identify and exploit natural fiduciary patterns within the environment. Diverging from conventional localization techniques that depend on artificial markers, this method capitalizes on the inherent environmental features to enhance both accuracy and computational efficiency. By integrating advanced deep learning frameworks with natural scene analysis, the proposed algorithm facilitates robu[...]
AI summary: Efficient robot localization through deep learning-based natural fiduciary pattern recognition. Utilizing deep learning to identify and exploit natural fiduciary patterns within the environment for accurate and efficient robotic localization. Analysis of automotive manufacturing scenario using a convolutional neural network for real-time localization. Leveraging natural fiduciary patterns for enhanced adaptability and precision in robot localization.
Localization, Deep Learning, Natural Fiduciary Patterns, Robotics, Algorithm
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Collision Avoidance in Autonomous Vehicles Using the Control Lyapunov Function–Control Barrier Function–Quadratic Programming Approach with Deep Reinforcement Learning Decision-Making
On 2025-01-30 by Haochong Chen, Fengrui Zhang, Bilin Aksun-Guvenc @MDPI
Abstract: Collision avoidance and path planning are critical topics in autonomous vehicle development. This paper presents the progressive development of an optimization-based controller for autonomous vehicles using the Control Lyapunov Function&ndash;Control Barrier Function&ndash;Quadratic Programming (CLF-CBF-QP) approach. This framework enables a vehicle to navigate to its destination while avoiding obstacles. A unicycle model is utilized to incorporate vehicle dynamics. A series of s[...]
AI summary: Optimization-based controller for autonomous vehicles using CLF-CBF-QP approach. Unicycle model, simulations, real-time testing, obstacle avoidance. Hierarchical control framework with DRL for effective response to obstacles.
Collision Avoidance, Autonomous Vehicles, Control Lyapunov Function, Deep Reinforcement Learning, Quadratic Programming
Publication
Image Navigation System for Thoracoscopic Surgeries Driven by Nuclear Medicine Utilizing Channel R-CNN
On 2025-01-30 by Chuanwang Zhang, Yueyuan Chen, Dongyao Jia, Bo Zhang @MDPI
Abstract: Breast cancer, a prevalent and significant cause of cancer-related mortality in women, often necessitates precise detection through nuclear medicine techniques. Despite the utility of computer-aided navigation in thoracoscopic surgeries like mastectomy, challenges persist in accurately locating and tracking target tissues amidst intricate surgical scenarios. This study introduces a novel system employing a channel R-CNN model to automatically segment target regions in thoracoscopic images and pr[...]
AI summary: Image navigation system utilizes nuclear medicine for precise detection in thoracoscopic surgeries. Introduces a novel system with channel R-CNN model for automatic segmentation and cutting curve indications. Multi-channel framework outperforms single-task models, achieving high accuracy in region segmentation and cutting path planning. Operates at real-time speed, highlighting potential of AI-driven solutions in enhancing surgical precision.
navigation system, nuclear medicine, channel R-CNN, thoracoscopic surgeries, cutting curve indication
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