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Operational safety of automated and human driving in mixed traffic environments: A perspective of car-following behavior

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2023年第2期237卷 355-366页

作者： Li, Tao Han, Xu Ma, Jiaqi Ramos, Marilia Lee, Changju Univ Calif Los Angeles 420 Westwood Plaza4731G Boelter Hall Los Angeles CA 90095 USA Virginia Transportat Res Council Charlottesville VA USA

The advent of automated vehicles (AVs) will provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicle (HV) to a fully AV traffic environment, there will be a mixed traffic flow including both HVs and AVs. The impact of introducing AVs into existing traffic, however, has not yet been fully understood. In this paper, we advance this understanding by conducting mixed traffic safety evaluation from the perspective of car-following behavior using real-world AV operational data of mixed traffic. To understand how the AVs impact other vehicles on the road, we analyzed the operational behaviors of HV-following-HV, AV-following-HV, and HV-following-AV. A selected car-following model is calibrated, and results show that there are significant differences between the HV-following-HV and the other two groups, indicating safe AV behavior and changes in HV behavior (i.e. less aggressive, safer) after the introduction of AVs into the traffic. Additionally, to understand AV behavioral safety, we investigate behavior predictions (one of the most critical inputs for AVs to make car-following decisions) of AVs and their surrounding vehicles using a mature baseline model and a new conditional variational autoencoder (CVAE) framework. The result shows potential risks of inaccurate predictions of the baseline model and the necessity to consider additional factors, such as vehicle interactions and driver behavior, into the prediction for risk mitigation. Arterial vehicle trajectory data from the Lyft Level 5 Dataset is applied to test the proposed methodological framework to understand the car-following safety risks of HVs and AVs in the mixed traffic stream.

关键词： Automated vehicles operational safety trajectory prediction conditional variational autoencoder car following behavior risk

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Introducing synthetic pseudo panels: application to transport behaviour dynamics

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TRANSPORTATION 2021年第5期48卷 2493-2520页

作者： Borysov, Stanislav S. Rich, Jeppe Tech Univ Denmark DTU Dept Technol Management & Econ DK-2800 Lyngby Denmark

In this paper, a method to study travel behaviour dynamics by constructing detailed synthetic pseudo panels from repeated cross-sectional data is presented. The method is based on the modelling of a high-dimensional joint distribution of travel preferences conditional on detailed socio-economic profiles by using a conditional variational autoencoder (CVAE). The CVAE is a neural-network-based generative model which allows the modelling of very detailed joint and conditional distributions, potentially defined by dozens or even hundreds of attributes in a flexible non-parametric form. The proposed method is used to rank detailed cohorts of individuals into slow and fast movers with respect to the speed at which their travel behaviour change over time. This gives an interesting insight into the types of individuals who are easily motivated to change their behaviour as opposed to those who are less flexible. Specifically, we investigate the dynamics of transport preferences for a fixed pseudo panel of individuals from a large Danish cross-sectional data set covering the period from 2006 to 2016. The comparison of the travel preference distributions from 2006 and 2016 shows that the prototypical fast mover is a single young woman who lives in a large city, whereas the typical slow mover is a middle-aged man with high income from a nuclear family who lives in a detached house outside a city. However, given that it is possible to rank individuals across very detailed socio-economic classifications, many other relationships can be explored. Finally, the CVAE can be directly applied to the population synthesis problem in microsimulation by modelling the distribution of socio-economic profiles conditional on other variables.

关键词： Dynamics of behaviour Pseudo-panel analysis conditional variational autoencoder Deep learning Generative modelling Population synthesis

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Spherical Image Generation From a Few Normal-Field-of-View Images by Considering Scene Symmetry

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第5期45卷 6339-6353页

作者： Hara, Takayuki Mukuta, Yusuke Harada, Tatsuya Univ Tokyo Res Ctr Adv Sci & Technol Tokyo 1138654 Japan RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan

Spherical images taken in all directions (360 degrees by 180 degrees) can represent an entire space including the subject, providing free direction viewing and an immersive experience to viewers. It is convenient and expands the usage scenarios to generate a spherical image from a few normal-field-of-view (NFOV) images, which are partial observations. The primary challenge is generating a plausible image and controlling the high degree of freedom involved in generating a wide area that includes all directions. We focus on scene symmetry, which is a basic property of the global structure of spherical images, such as the rotational and plane symmetries. We propose a method for generating a spherical image from a few NFOV images and controlling the generated regions using scene symmetry. We incorporate the intensity of the symmetry as a latent variable into conditional variational autoencoders to estimate the possible range of symmetry and decode a spherical image whose features are represented through a combination of symmetric transformations of the NFOV image features. Our experiments show that the proposed method can generate various plausible spherical images controlled from asymmetrically to symmetrically, and can reduce the reconstruction errors of the generated images based on the estimated symmetry.

关键词： Image synthesis Image reconstruction Task analysis Gravity Cameras Rendering (computer graphics) Recording Spherical image image generation conditional variational autoencoder symmetry estimation symmetry control

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Few-shot learning with representative global prototype

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NEURAL NETWORKS 2024年 180卷 106600页

作者： Liu, Yukun Shi, Daming Lin, Hexiu Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

Few-shot learning is often challenged by low generalization performance due to the model is mostly learned with the base classes only. To mitigate the above issues, a few-shot learning method with representative global prototype is proposed in this paper. Specifically, to enhance generalization to novel class, we propose a strategy for jointly training base and novel classes. This process produces prototypes characterizing the class information called representative global prototypes. Additionally, to avoid the problem of data imbalance and prototype bias caused by newly added categories of sparse samples, a novel sample synthesis method is proposed for augmenting more representative samples of novel class. Finally, representative samples and non-representative samples with high uncertainty are selected to enhance the representational and discriminative abilities of the global prototype. Intensive experiments have been conducted on two popular benchmark datasets, and the experimental results show that this method significantly improves the classification ability of few-shot learning tasks and achieves state-of-the-art performance.

关键词： Few-shot learning Representative global prototype conditional variational autoencoder Sample synthesis

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FedBKD: Heterogenous Federated Learning via Bidirectional Knowledge Distillation for Modulation Classification in IoT-Edge System

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2023年第1期17卷 189-204页

作者： Qi, Peihan Zhou, Xiaoyu Ding, Yuanlei Zhang, Zhengyu Zheng, Shilian Li, Zan Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China 011 Res Ctr Sci & Technol Commun Informat Secur Control Lab Jiaxing 314033 Peoples R China

Benefit from the rapid evolution of artificial intelligence and wireless communication technology, diverse Internet of Things (IoT) devices with edge computing ability have widely penetrated every aspect of daily human life. However, the deviations of private datasets and the heterogeneity of local models caused by the difference in device composition and application scenarios have hampering the aggregation of global recognition model in modulation classification task, thus constraining the classification performance of intelligent IoT-edge devices severely. To address this problem, we propose a heterogenous Federated learning framework based on Bidirectional Knowledge Distillation (FedBKD) for IoT system, which integrates knowledge distillation into the local model upload (client-to-cloud) and global model download (cloud-to-client) steps of federated learning. The client-to-cloud distillation is regarded as a process of multi-teacher knowledge distillation and the global network is regarded as a student network that unifies the heterogeneous knowledge from multiple local teacher networks. A public dataset is generated by conditional variational autoencoder (CVAE) and stored in the cloud server for supporting the obtaining of heterogeneous knowledge without sharing the private data of IoT devices. The cloud-to-client distillation is single-teacher-multiple-students process, which distills the knowledge from the single global model back to multiple heterogeneous local networks and partial knowledge distillation is used in this process. We implement our FedBKD method in the modulation classification task and the simulation results have proven the effectiveness of our proposed method.

关键词： Data models Internet of Things Federated learning Modulation Knowledge engineering Adaptation models Training IoT federated learning model heterogeneity knowledge distillation conditional variational autoencoder

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An Emotional Dialogue System Using conditional Generative Adversarial Networks with a Sequence-to-Sequence Transformer Encoder

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2024年第12期23卷 1-23页

作者： Huang, Wen-chieh Hsueh, Yu-ling Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Minhsiung Chiayi Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Minhsiung Chiayi Taiwan

Understanding the expression of emotion and generating appropriate responses are key steps toward constructing emotional, conversational agents. In this article, we propose a framework for single-turn emotional conversation generation, and there are three main components in our model, namely, a sequence-to-sequence model with stacked encoders, a conditional variational autoencoder, and conditional generative adversarial networks. For the sequence-to-sequence model with stacked encoders, we designed a two-layer encoder by combining Transformer with gated recurrent units-based neural networks. Because of the flexibility of the sequence-to-sequence model, we adopted a conditional variational autoencoder in our framework, which uses latent variables to learn a distribution over potential responses and generates diverse responses. Furthermore, we regard a conditional variational autoencoder-based, sequence-to-sequence model as the generative model, and the training of the generative model is assisted by both a content discriminator and an emotion classifier, which assists our model in promoting content information and emotion expression. We use automated evaluation and human evaluation to evaluate our model and baselines on the NII Test Collections for IR Systems short-text conversation task Chinese emotional conversation generation Subtask dataset [44], and the experimental results demonstrate that our proposed framework can generate semantically reasonable and emotionally appropriate responses.

关键词： Chinese conversation generation emotional intelligence natural lan- guage processing deep learning gated recurrent units sequence-to-sequence attention mechanism trans- former model conditional adversarial generative nets conditional variational autoencoder

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Long-term prediction of safety parameters with uncertainty estimation in emergency situations at nuclear power plants

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第5期55卷 1630-1643页

作者： Kim, Hyojin Kim, Jonghyun Chosun Univ Dept Nucl Engn 10Chosundae 1 gil Gwangju South Korea

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP. (c) 2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Bidirectional long short-term memory Attention mechanism conditional variational autoencoder Long-term prediction Uncertainty estimation

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Learning a Probabilistic Model for Diffeomorphic Registration

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2019年第9期38卷 2165-2176页

作者： Krebs, Julian Delingette, Herve Mailhe, Boris Ayache, Nicholas Mansi, Tommaso Univ Cote Azur INRIA Epione Team F-06902 Sophia Antipolis France Siemens Healthineers Digital Serv Digital Technol & Innovat Princeton NJ 08540 USA

We propose to learn a low-dimensional probabilistic deformation model from data which can be used for the registration and the analysis of deformations. The latent variable model maps similar deformations close to each other in an encoding space. It enables to compare deformations, to generate normal or pathological deformations for any new image, or to transport deformations from one image pair to any other image. Our unsupervised method is based on the variational inference. In particular, we use a conditional variational autoencoder network and constrain transformations to be symmetric and diffeomorphic by applying a differentiable exponentiation layer with a symmetric loss function. We also present a formulation that includes spatial regularization such as the diffusion-based filters. In addition, our framework provides multi-scale velocity field estimations. We evaluated our method on 3-D intra-subject registration using 334 cardiac cine-MRIs. On this dataset, our method showed the state-of-the-art performance with a mean DICE score of 81.2% and a mean Hausdorff distance of 7.3 mm using 32 latent dimensions compared to three state-of-the-art methods while also demonstrating more regular deformation fields. The average time per registration was 0.32 s. Besides, we visualized the learned latent space and showed that the encoded deformations can be used to transport deformations and to cluster diseases with a classification accuracy of 83% after applying a linear projection.

关键词： Deformable registration probabilistic encoding deep learning conditional variational autoencoder latent variable model deformation transport

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A-CAVE: Network abnormal traffic detection algorithm based on variational autoencoder

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ICT EXPRESS 2023年第5期9卷 896-902页

作者： Dong, Shi Su, Huadong Liu, Yue Zhoukou Normal Univ Sch Comp Sci & Technol Zhoukou 466001 Peoples R China Zhoukou Normal Univ Inst Cybersecur Zhoukou 466001 Peoples R China Wuhan Text Univ Sch Comp & Artificial Intelligence Wuhan 430200 Peoples R China

With the rapid development of the Internet and the rapid popularity of the network. Increasing network traffic also leads to frequent abnormal attacks, which seriously affects user information security. Then intrusion detection is studied as an active defense technology. In order to increase the ability of network abnormal traffic detection, a network abnormal traffic detection algorithm based on variational autoencoder is proposed in this paper. In addition to normal data, the algorithm subdivides the intrusion attack types into 4 major categories, which contain 38 specific attacks, each specific attack category as an intrusion label, which will be used as the input of the decoder together with the hidden vector to obtain the classification results more accurately and efficiently and to recover the data features in the incomplete data set. Finally, the trained Deep Belief Nets (DBN) is used for further feature extraction and classification of the data. It is verified that the proposed variational autoencoder-based supervised classification scheme is simple and effective, which is able to identify new attacks well and outperforms other classification schemes in terms of data classification. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： conditional variational autoencoder Anomaly traffic Neural network Intrusion detection

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PREDICTING PEDESTRIAN TRAJECTORIES IN ARCHITECTURAL SPACES: A GRAPH NEURAL NETWORK APPROACH 29th

PREDICTING PEDESTRIAN TRAJECTORIES IN ARCHITECTURAL SPACES: ...

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29th International Conference of the Association-for-Computer-Aided-Architectural-Design-Research-in-Asia (CAADRIA)

作者： Yang, Runyu Wang, Weili Gui, Peng Glodon Co Ltd Beijing Peoples R China

ISBN: (纸本)9789887891819

This paper introduces a graph neural network-based model for predicting pedestrian trajectories in architectural spaces. Compared to traditional simulations based on physics-based models, this data-driven model has a stronger ability to learn and predict pedestrian behaviour patterns from real-world data. The model is pre-trained based on Hongqiao Railway Station Dataset, then trained and tested based on the ETH Dataset and the Stanford Drone Dataset, enabling comparisons with other AI models. By creating a more intelligent model, we can establish a digital replica of the real world that can predict pedestrian flow with higher accuracy in daily life or extreme situations such as sudden fires. Our results underscore the critical role of such models in comprehending how architectural spaces are utilized, and thus in improving architectural design and urban planning.

关键词： Multi-agent Simulation Trajectory Prediction Graph Neural Network conditional variational autoencoder Path-finding

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