Journal of Intelligent and Connected Vehicles最新文献_第4页

Prospects of eVTOL and Modular Flying Cars in China Urban Settings eVTOL 和模块化飞行汽车在中国城市环境中的应用前景

Journal of Intelligent and Connected Vehicles Pub Date : 2023-12-19 DOI: 10.26599/JICV.2023.9210029

Chunlei Zheng;Yiping Yan;Yang Liu

{"title":"Prospects of eVTOL and Modular Flying Cars in China Urban Settings","authors":"Chunlei Zheng;Yiping Yan;Yang Liu","doi":"10.26599/JICV.2023.9210029","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210029","url":null,"abstract":"Throughout much of human history, the vast majority of people lived in small communities. However, in the last few centuries, and particularly in recent decades, there has been a dramatic shift. A massive migration has moved populations from rural to urban areas. United Nations reports state that over 4.3 billion individuals now inhabit urban regions, which accounts for more than half (55% as of 2017) of the global population. In most high-income nations, including Western Europe, the Americas, Australia, Japan, and the Middle East, over 80% of people live in urban areas. This figure ranges from 50% to 80% in upper-middle-income countries like Eastern Europe, East Asia, North Africa, South Africa, and South America (United Nations, Department of Economic and Social Affairs, Population Division, 2019). The urban population is anticipated to rise across all countries in the coming decades, albeit at different rates. By 2050, the global population is expected to reach approximately 9.8 billion, with about 6.7 billion residing in cities and 3.1 billion in rural areas. Despite this rapid urbanization, only around 1% of the Earth's land is allocated for urban and infrastructure development. While urbanization has spurred socio-economic growth, it has also led to significant challenges such as traffic congestion and air pollution. In China, the swift growth of cities has notably expanded urban areas and extended the commuting times of residents. The “2022 Commuting Monitoring Report of Major Chinese Cities” reveals that in 2022, over 14 million people in 44 major Chinese cities experienced extreme commuting, with upwards of 13% spending over an hour in transit (Baidu Maps, 2023). Beijing recorded the highest rate, where 26% of commuters faced this issue.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"187-189"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10365716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Vehicle Speed Control Strategies 车速控制策略评述

Journal of Intelligent and Connected Vehicles Pub Date : 2023-12-01 DOI: 10.26599/JICV.2023.9210010

Changxi Ma;Yuanping Li;Wei Meng

{"title":"A Review of Vehicle Speed Control Strategies","authors":"Changxi Ma;Yuanping Li;Wei Meng","doi":"10.26599/JICV.2023.9210010","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210010","url":null,"abstract":"Currently, traffic problems in urban road traffic environments remain severe, traffic pollution and congestion have not been effectively improved, and traffic accidents are still frequent. Traditional traffic signal control methods have little effect on these problems. With the continuous improvement of communication technology and network connections, vehicle speed guidance provides a new idea for solving the above problems and has gradually become a popular topic in academic research. However, its generalization has shortcomings. Therefore, this paper summarizes the research on vehicle speed control strategies in urban road environments and provides suggestions for future research. In this paper, we summarize the existing research in four parts. First, we categorize existing research based on vehicle type. Second, the vehicle speed guidance problem is divided according to the problem research scene. Third, we summarize the existing literature regarding vehicle speed. Finally, we summarize the methods used for speed guidance. Through an analysis of the existing literature, it is concluded that there is a deficiency in the existing research, and suggestions for the future of vehicle speed guidance research are suggested.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"190-201"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Target Tracking Algorithm for Autonomous Driving Based on Visible and Infrared Image Fusion 基于可见光和红外图像融合的增强型自动驾驶目标跟踪算法

Journal of Intelligent and Connected Vehicles Pub Date : 2023-12-01 DOI: 10.26599/JICV.2023.9210018

Quan Yuan;Haixu Shi;Ashton Tan Yu Xuan;Ming Gao;Qing Xu;Jianqiang Wang

{"title":"Enhanced Target Tracking Algorithm for Autonomous Driving Based on Visible and Infrared Image Fusion","authors":"Quan Yuan;Haixu Shi;Ashton Tan Yu Xuan;Ming Gao;Qing Xu;Jianqiang Wang","doi":"10.26599/JICV.2023.9210018","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210018","url":null,"abstract":"In autonomous driving, target tracking is essential to environmental perception. The study of target tracking algorithms can improve the accuracy of an autonomous driving vehicle's perception, which is of great significance in ensuring the safety of autonomous driving and promoting the landing of technical applications. This study focuses on the fusion tracking algorithm based on visible and infrared images. The proposed approach utilizes a feature-level image fusion method, dividing the tracking process into two components: image fusion and target tracking. An unsupervised network, Visible and Infrared image Fusion Network (VIF-net), is employed for visible and infrared image fusion in the image fusion part. In the target tracking part, Siamese Region Proposal Network (SiamRPN), based on deep learning, tracks the target with fused images. The fusion tracking algorithm is trained and evaluated on the visible infrared image dataset RGBT234. Experimental results demonstrate that the algorithm outperforms training networks solely based on visible images, proving that the fusion of visible and infrared images in the target tracking algorithm can improve the accuracy of the target tracking even if it is like tracking-based visual images. This improvement is also attributed to the algorithm's ability to extract infrared image features, augmenting the target tracking accuracy.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"237-249"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Micro-Simulation Insights into the Safety and Operational Benefits of Autonomous Vehicles 通过微观模拟深入了解自动驾驶汽车的安全和运营优势

Journal of Intelligent and Connected Vehicles Pub Date : 2023-12-01 DOI: 10.26599/JICV.2023.9210007

Nalin Kumar Sekar;Vinayak Malaghan;Digvijay S. Pawar

{"title":"Micro-Simulation Insights into the Safety and Operational Benefits of Autonomous Vehicles","authors":"Nalin Kumar Sekar;Vinayak Malaghan;Digvijay S. Pawar","doi":"10.26599/JICV.2023.9210007","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210007","url":null,"abstract":"Several past studies showed that Autonomous Vehicles (AVs) can reduce crash risk, stop-and-go traffic, and travel time. To analyze the safety benefits of AVs, most of the researchers proposed algorithms and simulation-based techniques. However, these studies have not assessed the safety benefits of AVs for different vehicle types under heterogeneous conditions. With this opportunity, this study focuses on the benefits of AVs in terms of safety for different penetration rates under heterogeneous conditions. This study considered three driving logics during peak hour conditions to assess the performance of AVs in terms of safety. In VISSIM, default driving behavior models for AVs were adopted to consider cautious and all-knowing driving logic and the third driving logic (Atkins) was modeled in VISSIM using parameters adopted from the previous studies. To this end, using VISSIM, the travel time output results were obtained. Also, using Surrogate Safety Assessment Model (SSAM), conflicts were extracted from output trajectory files (VISSIM). The results suggest that “cautious driving logic” reduced travel time and crash risk significantly when compared to the other two driving logics during peak hour conditions. Furthermore, the statistical analysis clearly demonstrated that “cautious driving logic” differs significantly from the other two driving logics. When Market Penetration Rates (MPR) were 50% or greater, the “cautious driving logic” significantly outperforms the other two driving logics. The results highlight that adopting “cautious driving logic” at an expressway may significantly increase safety at higher AV penetration rates (above 50%).","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"202-210"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Private or On-Demand Autonomous Vehicles? Modeling Public Interest Using a Multivariate Model 私人自动驾驶汽车还是按需自动驾驶汽车？使用多变量模型模拟公众利益

Journal of Intelligent and Connected Vehicles Pub Date : 2023-12-01 DOI: 10.26599/JICV.2023.9210015

Sailesh Acharya

{"title":"Private or On-Demand Autonomous Vehicles? Modeling Public Interest Using a Multivariate Model","authors":"Sailesh Acharya","doi":"10.26599/JICV.2023.9210015","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210015","url":null,"abstract":"With the likely future of autonomous vehicles (AVs) as private, ride-hailing, and pooled vehicles, it is important to consider all forms of AVs when estimating the impacts of automation on travel behavior. To aid this, this study jointly models the public interest in three forms of AVs (owning, ride-hailing, and using pooled services) and compares the interests in owning versus ride-hailing AVs using a combination of structural equation modeling and multivariate ordered probit modeling frameworks. Using the 2019 California Vehicle Survey data, we estimate the impacts of several exogenous and latent variables on all forms of AV adoption. We find that the individual, household, travel-related, and built-environment factors are related to different forms of AV adoption directly and indirectly through attitudes toward human and automated driving. We also report that human and automated driving sentiments have the highest impact on interest in owning an AV compared to interest in ride-hailing and using pooled AVs. We discuss several policy implications by calculating the pseudo-elasticity effects of exogenous variables and the sensitivities of the impacts on latent variables on different forms of AV adoption. For example, public interest in owning private AVs can be increased by more than 7% by making them familiar with autonomous technology.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"211-226"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scale Variant Vehicle Object Recognition by CNN Module of Multi-Pooling-PCA Process 利用多池化-PCA 过程的 CNN 模块识别比例变化的车辆目标

Journal of Intelligent and Connected Vehicles Pub Date : 2023-12-01 DOI: 10.26599/JICV.2023.9210017

Yuxiang Guo;Itsuo Kumazawa;Chuyo Kaku

{"title":"Scale Variant Vehicle Object Recognition by CNN Module of Multi-Pooling-PCA Process","authors":"Yuxiang Guo;Itsuo Kumazawa;Chuyo Kaku","doi":"10.26599/JICV.2023.9210017","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210017","url":null,"abstract":"The moving vehicles present different scales in the image due to the perspective effect of different viewpoint distances. The premise of advanced driver assistance system (ADAS) system for safety surveillance and safe driving is early identification of vehicle targets in front of the ego vehicle. The recognition of the same vehicle at different scales requires feature learning with scale invariance. Unlike existing feature vector methods, the normalized PCA eigenvalues calculated from feature maps are used to extract scale-invariant features. This study proposed a convolutional neural network (CNN) structure embedded with the module of multi-pooling-PCA for scale variant object recognition. The validation of the proposed network structure is verified by scale variant vehicle image dataset. Compared with scale invariant network algorithms of Scale-invariant feature transform (SIFT) and FSAF as well as miscellaneous networks, the proposed network can achieve the best recognition accuracy tested by the vehicle scale variant dataset. To testify the practicality of this modified network, the testing of public dataset ImageNet is done and the comparable results proved its effectiveness in general purpose of applications.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"227-236"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Level Objective Control of AVs at a Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach 采用多代理深度强化学习方法，在饱和信号灯路口对 AV 进行多层次目标控制

Journal of Intelligent and Connected Vehicles Pub Date : 2023-12-01 DOI: 10.26599/JICV.2023.9210021

Wenfeng Lin;Xiaowei Hu;Jian Wang

{"title":"Multi-Level Objective Control of AVs at a Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach","authors":"Wenfeng Lin;Xiaowei Hu;Jian Wang","doi":"10.26599/JICV.2023.9210021","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210021","url":null,"abstract":"Reinforcement learning (RL) can free automated vehicles (AVs) from the car-following constraints and provide more possible explorations for mixed behavior. This study uses deep RL as AVs' longitudinal control and designs a multi-level objectives framework for AVs' trajectory decision-making based on multi-agent DRL. The saturated signalized intersection is taken as the research object to seek the upper limit of traffic efficiency and realize the specific target control. The simulation results demonstrate the convergence of the proposed framework in complex scenarios. When prioritizing throughputs as the primary objective and emissions as the secondary objective, both indicators exhibit a linear growth pattern with increasing market penetration rate (MPR). Compared with MPR is 0%, the throughputs can be increased by 69.2% when MPR is 100%. Compared with linear adaptive cruise control (LACC) under the same MPR, the emissions can also be reduced by up to 78.8%. Under the control of the fixed throughputs, compared with LACC, the emission benefits grow nearly linearly as MPR increases, it can reach 79.4% at 80% MPR. This study employs experimental results to analyze the behavioral changes of mixed flow and the mechanism of mixed autonomy to improve traffic efficiency. The proposed method is flexible and serves as a valuable tool for exploring and studying the behavior of mixed flow behavior and the patterns of mixed autonomy.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"250-263"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SceGAN: A Method for Generating Autonomous Vehicle Cut-In Scenarios on Highways Based on Deep Learning SceGAN：基于深度学习的高速公路自动驾驶车辆切入场景生成方法

Journal of Intelligent and Connected Vehicles Pub Date : 2023-12-01 DOI: 10.26599/JICV.2023.9210023

Lan Yang;Jiaqi Yuan;Xiangmo Zhao;Shan Fang;Zeyu He;Jiahao Zhan;Zhiqiang Hu;Xia Li

{"title":"SceGAN: A Method for Generating Autonomous Vehicle Cut-In Scenarios on Highways Based on Deep Learning","authors":"Lan Yang;Jiaqi Yuan;Xiangmo Zhao;Shan Fang;Zeyu He;Jiahao Zhan;Zhiqiang Hu;Xia Li","doi":"10.26599/JICV.2023.9210023","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210023","url":null,"abstract":"With the increasing level of automation of autonomous vehicles, it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market. Traditional public road and closed-field testing failed to meet the requirements of high testing efficiency and scenario coverage. Therefore, scenario-based autonomous vehicle simulation testing has emerged. Many scenarios form the basis of simulation testing. Generating additional scenarios from an existing scenario library is a significant problem. Taking the scenarios of a proceeding vehicle cutting into an adjacent lane on highways as an example, based on an autoencoder and a generative adversarial network (GAN), a method that combines Transformer to capture the features of a long-time series, called SceGAN, is proposed to model and generate scenarios of autonomous vehicles on highways. An evaluation system is established to analyze the reliability of SceGAN using discriminative and predictive scores and further evaluate the effect of scenario generation in terms of similarity and coverage. Experiments showed that compared with TimeGAN and AEGAN, SceGAN is superior in data fidelity and availability, and their similarity increased by 27.22% and 21.39%, respectively. The coverage increased from 79.84% to 93.98% as generated scenarios increased from 2,547 to 50,000, indicating that the proposed method has a strong generalization capability for generating multiple trajectories, providing a basis for generating test scenarios and promoting autonomous vehicle testing.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"264-274"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Learning Method for Traffic Light Status Recognition 一种用于识别交通信号灯状态的深度学习方法

Journal of Intelligent and Connected Vehicles Pub Date : 2023-09-01 DOI: 10.26599/JICV.2023.9210022

Lan Yang;Zeyu He;Xiangmo Zhao;Shan Fang;Jiaqi Yuan;Yixu He;Shijie Li;Songyan Liu

{"title":"A Deep Learning Method for Traffic Light Status Recognition","authors":"Lan Yang;Zeyu He;Xiangmo Zhao;Shan Fang;Jiaqi Yuan;Yixu He;Shijie Li;Songyan Liu","doi":"10.26599/JICV.2023.9210022","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210022","url":null,"abstract":"Real-time and accurate traffic light status recognition can provide reliable data support for autonomous vehicle decision-making and control systems. To address potential problems such as the minor component of traffic lights in the perceptual domain of visual sensors and the complexity of recognition scenarios, we propose an end-to-end traffic light status recognition method, ResNeSt50-CBAM-DINO (RC-DINO). First, we performed data cleaning on the Tsinghua-Tencent traffic lights (TTTL) and fused it with the Shanghai Jiao Tong University's traffic light dataset (S2TLD) to form a Chinese urban traffic light dataset (CUTLD). Second, we combined residual network with split-attention module-50 (ResNeSt50) and the convolutional block attention module (CBAM) to extract more significant traffic light features. Finally, the proposed RC-DINO and mainstream recognition algorithms were trained and analyzed using CUTLD. The experimental results show that, compared to the original DINO, RC-DINO improved the average precision (AP), AP at intersection over union (IOU) = 0.5 (AP\u0000<inf>50</inf>\u0000), AP for small objects (APs), average recall (AR), and balanced F score (F1-Score) by 3.1 %, 1.6%, 3.4%, 0.9%, and 0.9%, respectively, and had a certain capability to recognize the partially covered traffic light status. The above results indicate that the proposed RC-DINO improved recognition performance and robustness, making it more suitable for traffic light status recognition tasks.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 3","pages":"173-182"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vehicle Sideslip Trajectory Prediction Based on Time-Series Analysis and Multi-Physical Model Fusion 基于时间序列分析和多物理模型融合的车辆侧滑轨迹预测

Journal of Intelligent and Connected Vehicles Pub Date : 2023-09-01 DOI: 10.26599/JICV.2023.9210016

Lipeng Cao;Yugong Luo;Yongsheng Wang;Jian Chen;Yansong He

{"title":"Vehicle Sideslip Trajectory Prediction Based on Time-Series Analysis and Multi-Physical Model Fusion","authors":"Lipeng Cao;Yugong Luo;Yongsheng Wang;Jian Chen;Yansong He","doi":"10.26599/JICV.2023.9210016","DOIUrl":"10.26599/JICV.2023.9210016","url":null,"abstract":"On highways, vehicles that swerve out of their lane due to sideslip can pose a serious threat to the safety of autonomous vehicles. To ensure their safety, predicting the sideslip trajectories of such vehicles is crucial. However, the scarcity of data on vehicle sideslip scenarios makes it challenging to apply data-driven methods for prediction. Hence, this study uses a physical model-based approach to predict vehicle sideslip trajectories. Nevertheless, the traditional physical model-based method relies on constant input assumption, making its long-term prediction accuracy poor. To address this challenge, this study presents the time-series analysis and interacting multiple model-based (IMM) sideslip trajectory prediction (TSIMMSTP) method, which encompasses time-series analysis and multi-physical model fusion, for the prediction of vehicle sideslip trajectories. Firstly, we use the proposed adaptive quadratic exponential smoothing method with damping (AQESD) in the time-series analysis module to predict the input state sequence required by kinematic models. Then, we employ an IMM approach to fuse the prediction results of various physical models. The implementation of these two methods allows us to significantly enhance the long-term predictive accuracy and reduce the uncertainty of sideslip trajectories. The proposed method is evaluated through numerical simulations in vehicle sideslip scenarios, and the results clearly demonstrate that it improves the long-term prediction accuracy and reduces the uncertainty compared to other model-based methods.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 3","pages":"161-172"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10310059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135503821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0