IET Intelligent Transport Systems最新文献_第10页

An improved transformer-based model for long-term 4D trajectory prediction in civil aviation 基于变压器的民用航空长期 4D 轨迹预测改进模型

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-06-24 DOI: 10.1049/itr2.12530

Aofeng Luo, Yuxing Luo, Hong Liu, Wenchao Du, Xiping Wu, Hu Chen, Hongyu Yang

{"title":"An improved transformer-based model for long-term 4D trajectory prediction in civil aviation","authors":"Aofeng Luo, Yuxing Luo, Hong Liu, Wenchao Du, Xiping Wu, Hu Chen, Hongyu Yang","doi":"10.1049/itr2.12530","DOIUrl":"https://doi.org/10.1049/itr2.12530","url":null,"abstract":"Four-dimensional trajectory prediction is a crucial component of air traffic management, and its accuracy is closely related to the efficiency and safety of air transportation. Although long short-term memory (LSTM) or its variants have been widely used in recent studies, they may produce unacceptable results in long-term prediction due to the iterative output that accumulates error. To address this issue, a transformer-based long-term trajectory prediction model is proposed here, which utilizes the self-attention mechanism to extract time series features from historical trajectory data. For long-term prediction scenarios, we a trajectory stabilization module is introduced to ensure the stationarity of the time series for better predictability. Additionally, the transformer output strategy is improved to generate the prediction sequence by a single step instead of serial dynamic decoding, thus effectively enhancing the precision and inference speed. The proposed model is validated using real data obtained from China's Southwest Air Traffic Management Bureau. The experimental results demonstrate that this model outperforms the benchmark model. Further ablation experiments and visualizations are performed to analyze the impact of trajectory stabilization and one-step inference strategy.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 9","pages":"1588-1598"},"PeriodicalIF":2.3,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards high-definition vector map construction based on multi-sensor integration for intelligent vehicles: Systems and error quantification 基于多传感器集成的智能车辆高清矢量地图构建：系统和误差量化

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-06-22 DOI: 10.1049/itr2.12524

Runzhi Hu, Shiyu Bai, Weisong Wen, Xin Xia, Li-Ta Hsu

{"title":"Towards high-definition vector map construction based on multi-sensor integration for intelligent vehicles: Systems and error quantification","authors":"Runzhi Hu, Shiyu Bai, Weisong Wen, Xin Xia, Li-Ta Hsu","doi":"10.1049/itr2.12524","DOIUrl":"https://doi.org/10.1049/itr2.12524","url":null,"abstract":"A lightweight, high-definition vector map (HDVM) enables fully autonomous vehicles. However, the generation of HDVM remains a challenging problem, especially in complex urban scenarios. Moreover, numerous factors in the urban environment can degrade the accuracy of HDVM, necessitating a reliable error quantification. To address these challenges, this paper presents an open-source and generic HDVM generation pipeline that integrates the global navigation satellite system (GNSS), inertial navigation system (INS), light detection and ranging (LiDAR), and camera. The pipeline begins by extracting semantic information from raw images using the Swin Transformer. The absolute 3D information of semantic objects is then retrieved using depth from the 3D LiDAR, and pose estimation from GNSS/INS integrated navigation system. Vector information (VI), such as lane lines, is extracted from the semantic information to construct the HDVM. To assess the potential error of the HDVM, this paper systematically quantifies the impacts of two key error sources, segmentation and LiDAR-camera extrinsic parameter error. An error propagation scheme is first formed to illustrate how these errors fundamentally influence the accuracy of the HDVM. The effectiveness of the proposed pipeline is demonstrated through our codeavailable at https://github.com/ebhrz/HDMap. The performance is verified using typical datasets, including indoor garages and complex urban scenarios.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 8","pages":"1477-1493"},"PeriodicalIF":2.3,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12524","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explicit coordinated signal control using soft actor–critic for cycle length determination 使用软行为批判器确定周期长度的显式协调信号控制

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-06-20 DOI: 10.1049/itr2.12519

Kun Zhang, Hongfeng Xu, Baofeng Pan, Qiming Zheng

{"title":"Explicit coordinated signal control using soft actor–critic for cycle length determination","authors":"Kun Zhang, Hongfeng Xu, Baofeng Pan, Qiming Zheng","doi":"10.1049/itr2.12519","DOIUrl":"https://doi.org/10.1049/itr2.12519","url":null,"abstract":"Explicit signal coordination carries prior knowledge of traffic engineering and is widely accepted for global implementation. With the recent popularity of reinforcement learning, numerous researchers have turned to implicit signal coordination. However, these methods inevitably require learning coordination from scratch. To maximize the use of prior knowledge, this study proposes an explicit coordinated signal control (ECSC) method using a soft actor–critic for cycle length determination. This method can fundamentally solve the challenges encountered by traditional methods in determining the cycle length. Soft actor–critic was selected among various reinforcement learning methods. A single agent was administered to the arterials. An action is defined as the selection of a cycle length from among the candidates. The state is represented as a feature vector, including the cycle length and features of each leg at every intersection. The reward is defined as departures that indirectly minimize system vehicle delays. Simulation results indicate that ECSC significantly outperforms the baseline methods, as evident in system vehicle delay across nearly all demand scenarios and throughput in high demand scenarios. The ECSC revitalizes explicit signal coordination and introduces new perspectives on the application of reinforcement learning methods in signal coordination.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 8","pages":"1396-1407"},"PeriodicalIF":2.3,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal placement of electric vehicle charging infrastructures utilizing deep learning 利用深度学习优化电动汽车充电基础设施的布局

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-06-20 DOI: 10.1049/itr2.12527

Mohamad Alansari, Ameena Saad Al-Sumaiti, Ahmed Abughali

{"title":"Optimal placement of electric vehicle charging infrastructures utilizing deep learning","authors":"Mohamad Alansari, Ameena Saad Al-Sumaiti, Ahmed Abughali","doi":"10.1049/itr2.12527","DOIUrl":"https://doi.org/10.1049/itr2.12527","url":null,"abstract":"The increasing level of air pollution caused by the transport sector necessitates countries to adopt Electric Vehicles (EVs). To espouse EVs, the charging infrastructures' location should be optimal to fulfill the mass-market consumer needs and reduce the governmental expenses. In this work, the placement of two categories of charging infrastructures, specifically Charging Station (CS) and Dynamic Wireless Charging (DWC) infrastructure is planned in Dubai, United Arab Emirates (UAE) as a case study. For this study, Dubai is divided into 14 districts as per its new addressing system, and the allocation of the two types of charging infrastructures is based on the projection of population growth, EVs adoption forecasting, and other factors with the objective of meeting the consumers' needs and minimizing the government's expenditure. The proposal introduces a novel hybrid model for forecasting, integrating the strengths of the Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) model for capturing time-series statistical characteristics, and the deep learning Attention-based Convolutional Neural Network (ACNN) for modeling nonlinear relationships in time-series data. The model's effectiveness was validated through comparative analyses against state-of-the-art (SOTA) models on standard benchmarks, showing significant improvements: 29.70% reduction in Mean Absolute Error (MAE), and 19.15% reduction in Root Mean Square Error (RMSE).","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 8","pages":"1529-1544"},"PeriodicalIF":2.3,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12527","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-supervised binocular depth estimation algorithm with self-rectification for autonomous driving 用于自动驾驶的具有自校正功能的自监督双目深度估计算法

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-06-19 DOI: 10.1049/itr2.12522

Jingyao Bao, Hongfei Yu, Yongjia Zou, Jin Lv, Wei Liu, Yang Cao

{"title":"Self-supervised binocular depth estimation algorithm with self-rectification for autonomous driving","authors":"Jingyao Bao, Hongfei Yu, Yongjia Zou, Jin Lv, Wei Liu, Yang Cao","doi":"10.1049/itr2.12522","DOIUrl":"https://doi.org/10.1049/itr2.12522","url":null,"abstract":"Aiming to address the challenge where existing methods struggle to predict accurate disparities for imperfectly rectified stereo images, and that supervised training requires a considerable amount of ground truth, a self-supervised binocular depth estimation algorithm with self-rectification for autonomous driving is proposed. Firstly, a subnetwork dedicated to stereo rectification, aiming to estimate the homography between stereo images is developed. This homography facilitates the transformation of stereo image pairs, aligning their corresponding pixels horizontally. Secondly, a foundational self-supervised framework primarily centred on minimizing errors in stereo image reconstruction, combined with the generative-adversarial strategy is introduced. Finally, a vertical offset prediction module (VOPM) is incorporated into the basic framework to further enhance the resistance of the stereo matching network to pixel-level vertical offset errors. Experimental results on the public KITTI dataset for autonomous driving demonstrate the effectiveness of this approach in improving the disparity prediction performance for imperfectly rectified stereo images. Moreover, the self-supervised training framework exhibits superiority over state-of-the-art methods.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 8","pages":"1445-1458"},"PeriodicalIF":2.3,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Driver behaviour recognition based on recursive all-pair field transform time series model 基于递归全对场变换时间序列模型的驾驶员行为识别

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-06-17 DOI: 10.1049/itr2.12528

HuiZhi Xu, ZhaoHao Xing, YongShuai Ge, DongSheng Hao, MengYing Chang

{"title":"Driver behaviour recognition based on recursive all-pair field transform time series model","authors":"HuiZhi Xu, ZhaoHao Xing, YongShuai Ge, DongSheng Hao, MengYing Chang","doi":"10.1049/itr2.12528","DOIUrl":"https://doi.org/10.1049/itr2.12528","url":null,"abstract":"To standardize driver behaviour and enhance transportation system safety, a dynamic driver behaviour recognition method based on the Recurrent All-Pairs Field Transforms (RAFT) temporal model is proposed. This study involves the creation of two datasets, namely, Driver-img and Driver-vid, including driver behaviour images and videos across various scenarios. These datasets are subject to preprocessing using RAFT optical flow techniques to enhance the cognitive process of the network. This approach employs a two-stage temporal model for driver behaviour recognition. In the initial stage, the MobileNet network is optimized and the GYY module is introduced, which includes residuals and global average pooling layers, thereby enhancing the network's feature extraction capabilities. In the subsequent stage, a bidirectional GRU network is constructed to learn driver behaviour video features with temporal information. Additionally, a method for compressing and padding video frames is proposed, which serves as input to the GRU network and enables intent prediction 0.2 s prior to driver actions. Model performance is assessed through accuracy, recall, and F1 score, with experimental results indicating that RAFT preprocessing enhances accuracy, reduces training time, and improves overall model stability, facilitating the recognition of driver behaviour intent.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 9","pages":"1559-1573"},"PeriodicalIF":2.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Practical aspects of measuring camera-based indicators of alcohol intoxication in manual and automated driving 测量手动和自动驾驶中基于摄像头的酒精中毒指标的实际问题

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-06-17 DOI: 10.1049/itr2.12520

Raimondas Zemblys, Christer Ahlström, Katja Kircher, Svitlana Finér

{"title":"Practical aspects of measuring camera-based indicators of alcohol intoxication in manual and automated driving","authors":"Raimondas Zemblys, Christer Ahlström, Katja Kircher, Svitlana Finér","doi":"10.1049/itr2.12520","DOIUrl":"https://doi.org/10.1049/itr2.12520","url":null,"abstract":"Camera-based Driver Monitoring Systems (DMS) have the potential to exploit eye tracking correlates of alcohol intoxication to detect drunk driving. This study investigates how glance, blink, saccade, and fixation metrics are affected by alcohol, and whether possible effects remain stable across three different camera setups, as well as when the driver is out-of-the-loop during level 4 automated driving (Wizard-of-Oz setup). Thirty-five participants drove on a test track first sober and then with increasing intoxication levels reaching a breath alcohol concentration (BrAC) of 1‰. Linear Mixed-Effects Regression analyses showed that with increasing intoxication levels, eye blinks became longer and slower, glances and fixations became fewer and longer, and more attention was directed to the road area, at the expense of more peripheral areas. Fixation and blink metrics were more robust to changes in automation mode, whereas glance-based metrics were highly context dependent. Not all effects of alcohol intoxication could be measured with all eye tracking setups, where one-camera systems showed lower data availability and higher noise levels compared to a five-camera system. This means that lab findings based on higher quality eye tracking data might not be directly applied to production settings because of hardware limitations.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 8","pages":"1408-1427"},"PeriodicalIF":2.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12520","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An emergency vehicle traffic signal preemption system considering queue spillbacks along routes and negative impacts on non-priority traffic 考虑到沿线队列回溢以及对非优先交通的负面影响的紧急车辆交通信号抢行系统

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-06-14 DOI: 10.1049/itr2.12518

Yen-Yu Chen, Jin-Yuan Wang, Shih-Ching Lo, Wei-Ting Sung

{"title":"An emergency vehicle traffic signal preemption system considering queue spillbacks along routes and negative impacts on non-priority traffic","authors":"Yen-Yu Chen, Jin-Yuan Wang, Shih-Ching Lo, Wei-Ting Sung","doi":"10.1049/itr2.12518","DOIUrl":"10.1049/itr2.12518","url":null,"abstract":"In urban areas, emergency vehicles (EVs) need efficient traffic signal preemption to ensure timely responses during peak hours. While emergency vehicle traffic signal preemption (EVTSP) has garnered significant attention in the literature, the issues of queue spillbacks and negative impacts on non-priority traffic have been relatively underreported. These issues are particularly critical during peak hours, notably in densely populated urban areas. This study presents an EVTSP system that considers queue spillbacks on approaches along the routes of EVs and the negative impacts of signal preemption on non-priority traffic. The proposed control system has four key components: (1) a queue length management algorithm to ensure that an EV will not be impeded by excessive queues, particularly on its initial approaches along its route; (2) a signal preemption algorithm to guarantee uninterrupted passage of an EV even for approaches experiencing queue spillbacks; (3) a traffic status recovery algorithm to alleviate the extra waiting time for non-priority vehicles after an EV crosses each intersection; and (4) a signal plan recovery algorithm to smoothly transit traffic signals to normal operation. The experimental results confirm that the proposed system considerably improves the travel time of an EV and mitigates the negative impacts on non-priority traffic.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 8","pages":"1385-1395"},"PeriodicalIF":2.3,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141338079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spread of parking difficulty in urban environments: A parking network perspective 城市环境中停车难的蔓延：停车网络视角

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-06-11 DOI: 10.1049/itr2.12525

Kangshuai Zhang, Yunduan Cui, Qi Liu, Hongfeng Shu, Lei Peng

引用次数: 0

Dynamic spatial-temporal network for traffic forecasting based on joint latent space representation 基于联合潜空间表示的交通预测动态时空网络

IF 2.3 4区工程技术

IET Intelligent Transport Systems Pub Date : 2024-05-14 DOI: 10.1049/itr2.12517

Qian Yu, Liang Ma, Pei Lai, Jin Guo

{"title":"Dynamic spatial-temporal network for traffic forecasting based on joint latent space representation","authors":"Qian Yu, Liang Ma, Pei Lai, Jin Guo","doi":"10.1049/itr2.12517","DOIUrl":"10.1049/itr2.12517","url":null,"abstract":"In the era of data-driven transportation development, traffic forecasting is crucial. Established studies either ignore the inherent spatial structure of the traffic network or ignore the global spatial correlation and may not capture the spatial relationships adequately. In this work, a Dynamic Spatial-Temporal Network (DSTN) based on Joint Latent Space Representation (JLSR) is proposed for traffic forecasting. Specifically, in the spatial dimension, a JLSR network is developed by integrating graph convolution and spatial attention operations to model complex spatial dependencies. Since it can adaptively fuse the representation information of local topological space and global dynamic space, a more comprehensive spatial dependency can be captured. In the temporal dimension, a Stacked Bidirectional Unidirectional Gated Recurrent Unit (SBUGRU) network is developed, which captures long-term temporal dependencies through both forward and backward computations and superimposed recurrent layers. On these bases, DSTN is developed in an encoder-decoder framework and periodicity is flexibly modeled by embedding branches. The performance of DSTN is validated on two types of real-world traffic flow datasets, and it improves over baselines.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"18 8","pages":"1369-1384"},"PeriodicalIF":2.3,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140982018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0