2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)最新文献_第7页

The Judgments and Effects of Truck Barriers on Diverging Area in Connected and Automated Environment 联网自动化环境下汽车障碍物对分流区的判断及影响

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101471

Lishan Sun, S. Wen, Dewen Kong, Yina Liu, Zhe Cheng

{"title":"The Judgments and Effects of Truck Barriers on Diverging Area in Connected and Automated Environment","authors":"Lishan Sun, S. Wen, Dewen Kong, Yina Liu, Zhe Cheng","doi":"10.1109/ICITE56321.2022.10101471","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101471","url":null,"abstract":"Connected and automated technology enables multiple trucks to platoon with a short spacing, promoting the formation of the truck barrier effect in the diverging area, thus having a significant impact on traffic flow. In the connected and automated environment, quantifying the discriminatory conditions of the truck barrier effect and exploring the traffic impact are important to improve the level of service in diverging areas and guide the development of platooning technology. Based on the Simulation of Urban Mobility (SUMO) simulation platform, the influence mechanisms of macro parameters such as arriving flow rate of departing vehicles (AD), arriving flow rate of going-straight vehicles (AS), and truck proportion (TP) and micro parameters such as time headway (TH) and platoon length (PL) on the truck barrier effect are analyzed. Macroscopically, different combinations of AD, AS and TP are derived for the truck barrier in the diverging area to form and as the TP increases, the departing demand and going-straight demand required for truck barrier effect occurrence decreases. Microscopically, TH ≤2s and PL>5 are the conditions for the truck barrier effect in the diverging area to appear. In addition, the truck barrier will increase the lane change rate and speed standard deviation, which will negatively affect the safety and stability of traffic flow. With the increase in traffic demand, the negative impact of the truck barrier becomes more significant.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124298802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Examination of the Influential Factors for Intention to Use Shared Autonomous Vehicles Based on Discrete Choice Models 基于离散选择模型的共享自动驾驶汽车使用意愿影响因素研究

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101459

Yixuan Shen, Guohua Deng

引用次数: 1

Multi-vehicle Cooperative Merging Control Strategy for Expressway under New Mixed Traffic Environment 新型混合交通环境下高速公路多车协同归并控制策略

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101440

Ying Chen, W. E, Xiang Wang, Qixing Wan, Cheng Wang, Na Yang

{"title":"Multi-vehicle Cooperative Merging Control Strategy for Expressway under New Mixed Traffic Environment","authors":"Ying Chen, W. E, Xiang Wang, Qixing Wan, Cheng Wang, Na Yang","doi":"10.1109/ICITE56321.2022.10101440","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101440","url":null,"abstract":"With the in-depth application of cooperative vehicle infrastructure system (CVIS) and automatic driving technology, a new traffic situation of hybrid driving of human driving vehicles (HDVs) and connected and automated Vehicles (CAVs) will certainly appear on the road in the future. Facing the problem of multi-vehicles confluence in the merging area of the expressway under the new mixed traffic environment, a cooperative merging control strategy is proposed to optimize the driving trajectories for the CAVs and reduce the traffic congestion in the merging area. First, on the basis of setting a dedicated CAVs lane on the mainline, different merging scenes are divided according to the traffic situations that the CAVs may encounter. Then, the vehicle cooperative merging model in the merging area is constructed to control the speed of CAVs. Finally, the model is analyzed and verified by simulation experiments. The simulation results show that the multi-vehicle cooperative merging control strategy proposed in this paper can make the speed distribution of the vehicles more uniform in the process of driving, and effectively improve passenger comfort and traffic efficiency in the merging area. At the same time, when the CAVs permeability is 50%, the total travel time in the system is also increased by about 8.09%.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125422380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Urban Road Mean Speed Prediction Method Based on LSTM-CNN Model 基于LSTM-CNN模型的城市道路平均速度预测方法研究

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101481

Ke Zhao, Shaoxin Yuan, Zhuanzhuan Wang, Jiaxuan Wang

{"title":"Research on Urban Road Mean Speed Prediction Method Based on LSTM-CNN Model","authors":"Ke Zhao, Shaoxin Yuan, Zhuanzhuan Wang, Jiaxuan Wang","doi":"10.1109/ICITE56321.2022.10101481","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101481","url":null,"abstract":"Urban road mean travel speed prediction is an important component of Intelligent Transportation System (ITS). The mean road travel speeds are affected by the factors such as the time-varying traffic demand and the weather with fluctuation at the microscopic level. How to measure the influences of these factors and quantify them is a problem that needs to be solved. Hence, a combined model based on a Long Short-Term Memory (LSTM) network and a Convolutional Neural Network (CNN) was proposed to predict the hourly road mean speed, and a multivariate dataset containing multidimensional features that impact the mean travel speed is also used. While the LSTM is employed to learn the temporal dependence of the road mean speed sequence, the CNN is used to capture the spatial influences arising from the cross-sectional data. For improving the prediction accuracy, the Median Absolute Deviation (MAD) method was used to deal with the outliers representing the abnormal traffic condition among the cab data in city of Xi’an. The results of the comparative experiments show that a more accurate prediction of the hourly road mean speed can be achieved after removing the outliers and using multivariate dataset, and the proposed model outperforms the LSTM model in prediction accuracy measured by the R-Square, the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE), and the ConvLSTM model which has a spatio-temporal feature extraction capability.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129953893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UAVDome - Safe UAV Transportation UAVDome -安全无人机运输

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101441

Shay Horovitz, Israel Zexer, Ben Ganot

{"title":"UAVDome - Safe UAV Transportation","authors":"Shay Horovitz, Israel Zexer, Ben Ganot","doi":"10.1109/ICITE56321.2022.10101441","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101441","url":null,"abstract":"With the rise of UAVs/drones in modern transportation and logistics, their security is being challenged and threatened both in frequency and sophistication. This creates the need for better secure drones in the commercial and military sectors. Several solutions are available, mainly relying on encryption or modified antennas, but those are often expensive or require extensive modification to the communication infrastructure, increasing the total cost and not applicable for commercial drones. Moreover, software based solutions are either limited to attack detection and rely on external or limited data sets. Other solutions require a camera which again can be attacked easily and limited during nighttime. This paper presents UAVDome, a machine learning-based solution that detects GPS attacks on drones and provides a unique return-to-home functionality without relying on GPS or camera sensors. The solution implements a virtual GPS that predicts the drone’s relative position using internal sensors only preventing the effect of external attacks without relying on any external data or communication. Experiments present the solution feasibility with minimal loss.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130005775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Traffic Sign Recognition Algorithm Based on Improved YOLOv5 基于改进YOLOv5的交通标志识别算法

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101379

Zhengxiao Sang, Fuming Xia, Han Huang, Z. Shi

{"title":"Traffic Sign Recognition Algorithm Based on Improved YOLOv5","authors":"Zhengxiao Sang, Fuming Xia, Han Huang, Z. Shi","doi":"10.1109/ICITE56321.2022.10101379","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101379","url":null,"abstract":"Due to the low detection accuracy of the existing YOLOv5s recognition algorithm for traffic sign recognition, it cannot meet the application requirements of actual scenarios. This paper makes some improvements on the basis of the original YOLOv5s model. First, a detection layer is added to the original three detection layers to obtain more location information of the target in the underlying feature map. After that, a convolution extraction module with CBAM attention mechanism is designed in the backbone of the model, so that the network pays more attention to the feature information of traffic signs. Then, in the Neck part of the model, a lightweight neural network C3Ghost is designed to replace the original convolutional network, which reduces the network model parameters, and finally integrates more semantic features by adding a cross-layer connection structure. The experiment uses the traffic sign dataset jointly released by Tsinghua and Tencent, and trains 45 types of traffic signs with a large number. The test results show that the optimized network model reduces parameters, and the detection accuracy is improved by 5.3% compared with the original network, which meets the needs of actual scene detection accuracy.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121083532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Speed Guidance Strategy at Intersections Based on Multiple Regression Analysis in the Connected Vehicles Environment 基于多元回归分析的网联车辆环境下交叉口速度引导策略

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101465

Chuanxiang Ren, Li Wang, Jinbo Wang, Jiandong Qu

引用次数: 0

Research on Real-Time Ship Detection Using Deep Learning 基于深度学习的实时船舶检测研究

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101400

Jin Yu, Jie Wang, Rong Ren, Qiuyu Lai, Xinpeng Luo, Huajie Lu

引用次数: 0

Research on Traction Performance of Intelligent Wheelchair Based on Ground Mechanics 基于地面力学的智能轮椅牵引性能研究

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101383

Xiangfeng Kong, Haitao Luo, Xuan Cao, Jiaze Sun

引用次数: 0

Mixed Criticality Function Performance Optimization Based on Vehicle-Cloud Joint Scheduling 基于车云联合调度的混合临界函数性能优化

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101428

Tianyu Wang

{"title":"Mixed Criticality Function Performance Optimization Based on Vehicle-Cloud Joint Scheduling","authors":"Tianyu Wang","doi":"10.1109/ICITE56321.2022.10101428","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101428","url":null,"abstract":"The optimization method of task scheduling has received increasingly extensive attention as the high level autonomous vehicle has been recognized as the typical mixed-criticality cyber-physical system. The low criticality functions are often degraded or abandoned at high system criticality level, thus decrease service satisfaction when the traditional mixed-criticality theory is applied to solve the scheduling problem. Under the scenario of the future prospect of intelligent transportation and the new electronic/electrical information architecture of network connection, this paper proposes an optimization method of vehicle-cloud joint scheduling, using edge computing to improve the performance of low criticality functions on the premise that high criticality functions can meet the deadline requirements. Since the previous DAG models used to describe vehicle functions are not specific enough, this paper also gives three detailed mixed-criticality function models to better fit the industrial reality. For the lack of evaluation of passengers’ perception when vehicle soft real-time DAG functions are not able to meet the deadline, the function completion evaluation coefficient is proposed to measure the scheduling effect of this situation, which also quantifies the degree of improvement of vehicle-cloud joint scheduling over the traditional local scheduling method.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114706252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0