The multi-object tracking (MOT) tasks require algorithms to maintain the identity of objects over a long time period, making it necessary to maintain long-term features that describe the trajectories. However, current...
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Multiple Cross-Domain Few-Shot Learning (MCD-FSL) aims to improve the generalization ability of the model across unseen domains by utilizing the diverse knowledge of different teacher networks. Knowledge transferring ...
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In this paper, the formation tracking problem with obstacle avoidance is investigated for double-integrator systems. First, a baseline controller is provided to achieve the formation tracking mission. Then, a level-se...
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Wheeled mobile robots have been exten-sively deployed across various domains, particularly in the context of service robots for indoor applications. However, navigating through narrow indoor environments has significa...
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Recently, grasp transfer has gained popularity for 6-DOF grasp pose estimation. However, most methods are limited to training separately for each category, and network parameters cannot be shared among different categ...
For a class of second-order nonlinear leader-following multi-agent systems with actuator faults and integral quadratic constraints (IQCs) of followers, a fully distributed adaptive consensus control algorithm based on...
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Metal materials play an important role in modern industrial fields, such as aerospace, trans-portation and chemical engineering, et al. However, as metal materials are frequently exposed to challenging environments, t...
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This work studies the autonomous formation obstacle avoidance problem of multi-mobile robots in complex environment. An improved dynamic window approach (DWA) algorithm is proposed, aiming at the defects of traditiona...
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In the current field of computer vision, visible-infrared cross-modal person re-identification has become a research topic of great interest. This task aims to identify and match images of the same pedestrian from dif...
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The advent of connected vehicles, characterized by features such as internet connectivity, data sharing, and autonomous driving, represents a transformative shift in the automotive industry. This progression not only ...
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ISBN:
(纸本)9798350372113;9798350372106
The advent of connected vehicles, characterized by features such as internet connectivity, data sharing, and autonomous driving, represents a transformative shift in the automotive industry. This progression not only enhances user experiences but also signifies a pivotal advancement in vehicular technology. However, the escalating connectivity in these vehicles introduces substantial cybersecurity challenges, encompassing threats of unauthorized access, data breaches, and the potential for remote hijacking. These vulnerabilities underscore the imperative need for robust security measures to shield smart cars from cyber threats. This paper addresses these concerns through a comprehensive analysis of cybersecurity in connected vehicles, with a particular focus on identifying and mitigating risks. We advocate for a holistic approach that integrates efforts from industry, government, and academia to fortify vehicle security. Our methodology involves implementing UNEX hardware solutions in selected use cases, showcasing their effectiveness in securing communication between On-Board Units (OBU) and Roadside Units (RSU), while also enhancing intrusion detection systems. The experimental analysis specifically targets various cybersecurity aspects, employing real-world scenarios to validate the efficacy of the proposed solutions. The analysis revealed a vulnerability in OBU-RSU communication, as injected packets bypassed detection despite longer round-trip times (1.5 to 2.7 seconds). The paper establishes the high efficiency and accuracy of these solutions in safeguarding connected vehicles against cyber threats, offering a promising outlook for automotive cybersecurity.
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