Predicting pedestrian behavior is a crucial task for intelligent driving systems. Accurate predictions require a deep understanding of various contextual elements that could impact the way pedestrians behave. To addre...
Predicting pedestrian behavior is a crucial task for intelligent driving systems. Accurate predictions require a deep understanding of various contextual elements that could impact the way pedestrians behave. To address this challenge, we propose a novel framework that relies on different data modalities to predict future trajectories and crossing actions of pedestrians from an egocentric perspective. Specifically, our model utilizes a cross-modal Transformer architecture to capture dependencies between different data types. The output of the Transformer is augmented with representations of interactions between pedestrians and other traffic agents conditioned on the pedestrian and ego-vehicle dynamics that are generated via a semantic attentive interaction module. Lastly, the context encodings are fed into a multi-stream decoder framework using a gated-shared network. We evaluate our algorithm on public pedestrian behavior benchmarks, PIE and JAAD, and show that our model improves state-of-the-art in trajectory and action prediction by up to 22% and 13% respectively on various metrics. The advantages of the proposed components are investigated via extensive ablation studies.
This paper studies the synchronization problem of two-player multiagent systems through reinforcement learning methods. A Nash-minmax strategy is formulated, where the interactions of two players in the same agent are...
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ISBN:
(数字)9798350363012
ISBN:
(纸本)9798350363029
This paper studies the synchronization problem of two-player multiagent systems through reinforcement learning methods. A Nash-minmax strategy is formulated, where the interactions of two players in the same agent are non-zero-sum, while interactions of players between agents are zero-sum games. We propose an offline model-based reinforcement learning algorithm to identify Nash solutions for players within each agent, as well as the worst control solutions for players in neighboring antagonistic agents. On this basis, a data-driven off-policy algorithm is provided to alleviate the requirement for accurate system dynamics in the offline algorithm. Besides, the convergence of the proposed algorithms is analyzed. Finally, simulation results verify the effectiveness of the designed algorithms.
Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively elim...
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Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.
A new deep learning (DL) network called Generative Perturbation Network (GPN) is proposed. The GPNs are capable of learning to modify inputs to deep neural network (DNN) models trained to classify images in impercepti...
A new deep learning (DL) network called Generative Perturbation Network (GPN) is proposed. The GPNs are capable of learning to modify inputs to deep neural network (DNN) models trained to classify images in imperceptible ways leading to misclassification. Unlike previous approaches to generate such adversarial samples to DNN classifiers, the proposed GPN does not need to know the architecture of the neural network that it is trying to attack nor have access to the data used to trained it. It is shown that with just being able to observe the final output labels from the trained classifier to any given input image, the GPNs are able to learn to minimally perturb the images to achieve misclassification. Simulation results show that a proposed GPN can easily degrade the 98.65% accuracy of a trained CNN on the MNIST hand-written digit dataset down to about 52%. Simulation results also show that different regularizations can be used in the generator loss function to achieve various visually desirable characteristics in the generated adversarially perturbed images.
In this paper, we study a class of stochastic bilevel optimization problems, also known as stochastic simple bilevel optimization, where we minimize a smooth stochastic objective function over the optimal solution set...
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Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we impro...
—In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses sig...
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We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challe...
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We study the problem of distributed and rateadaptive feature compression for linear regression. A set of distributed sensors collect disjoint features of regressor data. A fusion center is assumed to contain a pretrai...
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This paper presents the creation of an innovative autonomous security robot designed to perform security functions with efficiency and reliability. The robot boasts mapping capabilities, which it utilizes to facilitat...
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ISBN:
(数字)9798350330649
ISBN:
(纸本)9798350330656
This paper presents the creation of an innovative autonomous security robot designed to perform security functions with efficiency and reliability. The robot boasts mapping capabilities, which it utilizes to facilitate autonomous patrol in designated areas. Its primary operations involve the use of computer vision to detect violence, identify weapons and dangerous items, and recognize individuals. Critical incidents are met with an immediate alarm and the subsequent transmission of data to a central security server, which then generates comprehensive reports displayed through a web application for security personnel. The application itself features remote control of the robot, incident report management, status updates, and incident analytics. The robot demonstrates substantial real-world application potential, particularly in crowded environments where it could outperform conventional surveillance. The project combines concepts of engineering, computer science, and cybersecurity, functioning per design but with considerable potential for future refinement and expansion, embodying the concept of an evolving technological solution.
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