With the advancement of Industry 4.0 and the logistics industry, traditional manual handling is gradually phasing out, giving rise to the emergence of intelligent logistics handling robots. The robot in this study int...
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Distributed deep learning has become an essential technique for accelerating deep learning, but its performance is often influenced by the heterogeneous computing nodes and heterogeneous communication networks within ...
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For effective city planning and traffic control, it is now crucial to develop some effective monitoring systems for vehicle traffic in order to address this quickly growing tendency within a city. As a result, the Tra...
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The increasing adoption of electric vehicles necessitates intelligent charging systems to address challenges such as grid overload, renewable energy integration, and user prioritization. This paper presents a new fram...
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AI tool has become so useful and important for companies that it can either drive customers to a company or a company to a sale. This Recommendations Engine have become a crucial part of how to present the right recom...
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The development of artificial intelligence (AI) assistants has revolutionized human-machine interaction, but traditional systems often rely on singular input modalities such as voice commands. This project, AURA: AI R...
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Ramp merging areas on highways often serve as bottleneck areas, leading to frequent interactions and accidents between vehicles on the ramp and the arterial road. This results in severe congestion and reduced traffic ...
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ISBN:
(纸本)9783031705069;9783031705076
Ramp merging areas on highways often serve as bottleneck areas, leading to frequent interactions and accidents between vehicles on the ramp and the arterial road. This results in severe congestion and reduced traffic performance. The emergence of Connected Autonomous Vehicles (CAVs) offers advanced solutions to address these issues and improve traffic operations at ramp merging areas. While previous studies have explored CAV decision-making approaches such as optimization control, model predictive control, and reinforcement learning, they face difficulties in accurately modeling the complex and dynamic scenarios of ramp merging. To overcome these challenges, this paper proposes a collaborative decision-making and control model based on Multi-agent Reinforcement Learning (MARL) for mixed vehicles (CAV-HDV) in multi-lane ramp merging scenarios on arterial roads. The paper introduces three novel MARL algorithms and conducts simulations in six different scenarios to evaluate traffic performance under various lane numbers and traffic densities. The results demonstrate the effectiveness of the proposed collaborative model for ramp merging vehicles. The proposed algorithms significantly reduce collision rates and improve traffic efficiency.
This paper presents a satellite hyperspectral image processing method that utilizes a maximum abundance classifier to categorize different regions of hyperspectral images into ground truth classes. First, the class na...
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The integration of facial recognition technology with Industrial Internet of Things (IIoT) systems offers significant potential to enhance operational efficiency, security, and safety within industrial environments. A...
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This paper proposes an advanced approach to land cover classification by integrating hyperspectral and LiDAR data to leverage their complementary strengths. Hyperspectral sensors capture detailed reflectance across nu...
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