Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. ...
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Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.
Tens of thousands of different animal species live on this planet, having survived for millions of years through adaptation and evolution, which has given them a vast variety of structures and functions. Biomechanical...
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ISBN:
(数字)9784431539513
ISBN:
(纸本)9784431222118;9784431679639
Tens of thousands of different animal species live on this planet, having survived for millions of years through adaptation and evolution, which has given them a vast variety of structures and functions. Biomechanical studies of animals swimming and flying can aid understanding of the mechanisms that enable them to move effectively and efficiently in fluids . Based on such understandings and analyses, we can aim to develop environmentally friendly machines that emulate these natu ral movements. The Earth Summit in Rio de Janeiro in 1992 agreed major treaties on biological diversity, addressing the comb ined issues of environmental protection and fair and equitable economic development. With regard to coastal environments, increasing biological diversity has begun to play an important role in reestablishing stable and sustainable ecosystems. This approach has begun to influence research into the behavior of aquatic species, as an understanding of the history of an individual aquatic species is indispensable in constructing an environmental assessment mod el that includes the physical, chemical, and biological effects of that species . From an engineering viewpoint, studying nature's biological diversity is an opportunity to reconsider mechanical systems that were systematically constructed in the wake of the Industrial Revolution. We have been accumulating knowledge of the sys tems inherent in biological creatures and using that knowledge to create new, envi ronmentally friendly technologies.
Recent years have witnessed the increasing prevalence of smart home applications, where digital twin (DT) is popularly employed for creating virtual models that interact with physical devices in real time. Empowered b...
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Recent years have witnessed the increasing prevalence of smart home applications, where digital twin (DT) is popularly employed for creating virtual models that interact with physical devices in real time. Empowered by artificial intelligence (AI), these DT-created virtual models have more intelligent decision-making capabilities to ensure reliable performance of a smart home system. In this paper, a DT based smart home framework is investigated. It is capable of achieving intelligent control, healthcare prediction and graphical monitoring. First, the human body and device are individually modeled, and then assembled into a DT system, and the corresponding model interfaces are provided for visual monitoring. Then, an intelligent algorithm fusing VGG, LSTM and attention mechanism is developed for healthcare monitoring, i.e., the screening out of the irregular ECG rhythms. The system results are provided, including various high-fidelity interactive DT interfaces as well as the effectiveness and advantages of the intelligent algorithms for arrhythmia detection.
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