Journal of Intelligent and Connected Vehicles最新文献_第2页

Application of the Traffic Fundamental Diagram to Assess Detector Performance 交通基本图在评估检测器性能中的应用

Journal of Intelligent and Connected Vehicles Pub Date : 2024-12-01 DOI: 10.26599/JICV.2023.9210050

Katherine Riffle;Edward J. Smaglik;Steven Procaccio;Steven R. Gehrke;Brendan J. Russo;David Hurwitz

引用次数: 0

Coordinated Optimization of Signal Timing for Intersections with Dynamic Shared Through- and Right-Turn Lanes 动态共享直行和右转车道交叉口信号配时的协调优化

Journal of Intelligent and Connected Vehicles Pub Date : 2024-09-26 DOI: 10.26599/JICV.2023.9210038

Zhe Zheng;Jian Yuan;Kun An;Nan Zheng;Wanjing Ma

{"title":"Coordinated Optimization of Signal Timing for Intersections with Dynamic Shared Through- and Right-Turn Lanes","authors":"Zhe Zheng;Jian Yuan;Kun An;Nan Zheng;Wanjing Ma","doi":"10.26599/JICV.2023.9210038","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210038","url":null,"abstract":"Through and right-turn shared lanes are widely designed to increase the capacity of through traffic, but they can also cause delays for right-turn vehicles. This study presents a dynamic control method for a shared lane that prioritizes right-turn vehicles at the beginning of the cycle and subsequently allows through traffic to queue in the shared lane for saturated discharge. The traffic wave model is employed to reveal the dynamics of the traffic flow under this control and to derive the relationships among major traffic parameters. Constrained by the major relationship, a linear programming approach to minimize the total queue length is developed to determine the proper values of control parameters, including the shared area length, subordinate signal time lag, and shared or exclusive duration. A sensitivity analysis of the control parameters for different arrival rates and flow ratios is performed. Comparisons are conducted among the dynamic shared lane, the fixed exclusive lane, and the fixed shared lane. The results show that the dynamic control method results in a lower delay for both through and total traffic.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"219-228"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324273","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

Convergence of Emerging Transportation Trends: A Comprehensive Review of Shared Autonomous Vehicles 新兴交通趋势的融合：共享型自动驾驶汽车综述

Journal of Intelligent and Connected Vehicles Pub Date : 2024-09-26 DOI: 10.26599/JICV.2023.9210043

Deema Almaskati;Sharareh Kermanshachi;Apurva Pamidimukkala

{"title":"Convergence of Emerging Transportation Trends: A Comprehensive Review of Shared Autonomous Vehicles","authors":"Deema Almaskati;Sharareh Kermanshachi;Apurva Pamidimukkala","doi":"10.26599/JICV.2023.9210043","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210043","url":null,"abstract":"The mobility landscape is experiencing major changes due to two emerging transportation trends, autonomous vehicles (AVs) and on-demand transportation, and the convergence of these smart mobility innovations as shared autonomous vehicles (SAVs) can considerably alter travel behavior and consequently the ecological and societal aspects of the transportation sector. On-demand autonomous mobility is a promising transportation mode, but further research is necessary to evaluate its various aspects and implications prior to widespread adoption. Thus, this study investigates the effects of integrating automation and on-demand mobility by analyzing the effects on the environment, public transportation, land use, vehicle ownership, and public acceptance. A comprehensive literature review was performed, and through a detailed review of 210 articles, the impacts of each of these categories were determined and classified according to their causes, and the number of publications with which they were cited in the literature was determined. The review showed that SAVs can either positively or negatively impact categories and have the potential to minimize mobility obstacles and transportation inequity if legislators use technology to develop a better transportation system by initiating effective policies that govern the four impacted areas. A list of 22 policy recommendations designed to avoid the negative consequences of SAVs by maximizing the benefits of the technology while limiting the associated risks was also identified. The findings of this review will be beneficial to AV manufacturers, transportation professionals, and especially policymakers, who play an integral role in shaping how society benefits from SAV technology.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"177-189"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324349","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

Development of Deep-Learning-Based Autonomous Agents for Low-Speed Maneuvering in Unity 在 Unity 中开发基于深度学习的低速操纵自主机器人

Journal of Intelligent and Connected Vehicles Pub Date : 2024-09-26 DOI: 10.26599/JICV.2023.9210039

Riccardo Berta;Luca Lazzaroni;Alessio Capello;Marianna Cossu;Luca Forneris;Alessandro Pighetti;Francesco Bellotti

{"title":"Development of Deep-Learning-Based Autonomous Agents for Low-Speed Maneuvering in Unity","authors":"Riccardo Berta;Luca Lazzaroni;Alessio Capello;Marianna Cossu;Luca Forneris;Alessandro Pighetti;Francesco Bellotti","doi":"10.26599/JICV.2023.9210039","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210039","url":null,"abstract":"This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating an obstacle-dense area. Our analysis involves training a path-planning agent with real-time-only sensor information. This study addresses research questions insufficiently covered in the literature, exploring curriculum learning (CL), agent generalization (knowledge transfer), computation distribution (CPU vs. GPU), and mapless navigation. CL proved necessary for the garage scenario and beneficial for obstacle avoidance. It involved adjustments at different stages, including terminal conditions, environment complexity, and reward function hyperparameters, guided by their evolution in multiple training attempts. Fine-tuning the simulation tick and decision period parameters was crucial for effective training. The abstraction of high-level concepts (e.g., obstacle avoidance) necessitates training the agent in sufficiently complex environments in terms of the number of obstacles. While blogs and forums discuss training machine learning models in Unity, a lack of scientific articles on DRL agents for AD persists. However, since agent development requires considerable training time and difficult procedures, there is a growing need to support such research through scientific means. In addition to our findings, we contribute to the R&D community by providing our environment with open sources.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"229-244"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324272","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

Spectrum Quantification-Based Safety Efficiency Evaluation of Autonomous Vehicle Under Random Cut-in Scenarios 基于频谱量化的随机切入场景下自动驾驶汽车安全效率评估

Journal of Intelligent and Connected Vehicles Pub Date : 2024-09-26 DOI: 10.26599/JICV.2023.9210035

Jiang Chen;Weiwei Zhang;Miao Liu;Xiaolan Wang;Jun Gong;Jun Li;Boqi Li;Jiejie Xu

{"title":"Spectrum Quantification-Based Safety Efficiency Evaluation of Autonomous Vehicle Under Random Cut-in Scenarios","authors":"Jiang Chen;Weiwei Zhang;Miao Liu;Xiaolan Wang;Jun Gong;Jun Li;Boqi Li;Jiejie Xu","doi":"10.26599/JICV.2023.9210035","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210035","url":null,"abstract":"Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence. While the UN R157 Regulation evaluates automated lane-keeping system (ALKS) performance baselines through safe collision plots (SCPs) in various scenario clusters, quantifying the specific ALKS safety efficiency remains challenging. We propose a spectrum quantification approach to evaluate the safety efficiency of autonomous vehicles in cut-in scenarios. First, we collected speed-distance data under different cut-in scenarios and extracted essential spectral features to indicate the vehicle motion parameters during the cut-in process. Second, by utilizing Fourier analysis, a spectral analysis model was built to quantify and analyze the vehicle motion characteristics, providing insights into scenario safety. Finally, we created approximate analytical equations for the normalized disturbance frequencies in the nonlinear response scenarios of autonomous driving systems by combining the SCP with a frequency spectrum analysis model. The results showed that the normalized disturbance frequency in the cut-in scenario was approximately 0.2. When the relative longitudinal distance and speed of the vehicle are the same, if the cut-in speed of the cut-in vehicle is larger, the normalized disturbance frequency is higher, indicating that the cut-in process of the autonomous vehicle is more dangerous and may trigger a collision.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"205-218"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323054","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

CPS Architecture Design for Urban Roadway Intersections Based on MBSE 基于 MBSE 的城市道路交叉口 CPS 架构设计

Journal of Intelligent and Connected Vehicles Pub Date : 2024-09-26 DOI: 10.26599/JICV.2023.9210030

Chen Wang;Xiaoping Ma;Limin Jia;Zheng Lai;Zhexuan Yang;Han Yan;Jing Zhao

{"title":"CPS Architecture Design for Urban Roadway Intersections Based on MBSE","authors":"Chen Wang;Xiaoping Ma;Limin Jia;Zheng Lai;Zhexuan Yang;Han Yan;Jing Zhao","doi":"10.26599/JICV.2023.9210030","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210030","url":null,"abstract":"With the rapid growth of urbanization and the increasing demand for transportation, urban traffic congestion has become a hindrance to individuals' travel experience. Urban intersections are one of the primary sources of traffic congestion, and these bottlenecks have a negative impact not only on traffic efficacy but also on the surrounding road traffic in the region. To alleviate urban traffic congestion, cyber-physical systems have been widely implemented in the transportation industry, allowing for the perception, analysis, calculation, and dispatching of urban traffic flow, as well as making urban transportation safe, efficient, and quick. As the system scale and functions increase, system design has become increasingly complex, necessitating a deeper comprehension of the system's structure and interaction relationships to construct a stable and reliable system. Therefore, this study proposes a method for designing cyber-physical systems for urban traffic intersections based on Model-Based Systems Engineering (MBSE). This method models and analyses exhaustively the system's requirements, functions, and logical architecture using System Modeling Language (SysML). After the architecture design has been completed, an architecture verification and optimization method based on Failure Mode and Effect Analysis (FMEA) for urban road intersection cyber-physical systems is utilized to analyze the architecture's reliability by analyzing the failure modes of activities and to optimize the system architecture to improve the design's efficiency and reliability.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"190-204"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323055","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

Multisensor Information Fusion: Future of Environmental Perception in Intelligent Vehicles 多传感器信息融合：智能汽车环境感知的未来

Journal of Intelligent and Connected Vehicles Pub Date : 2024-07-16 DOI: 10.26599/JICV.2023.9210049

Yongsheng Zhang;Chen Tu;Kun Gao;Liang Wang

{"title":"Multisensor Information Fusion: Future of Environmental Perception in Intelligent Vehicles","authors":"Yongsheng Zhang;Chen Tu;Kun Gao;Liang Wang","doi":"10.26599/JICV.2023.9210049","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210049","url":null,"abstract":"As urban transportation increasingly impacts daily life, efficiently utilizing traffic resources and developing public transportation have become crucial for addressing issues such as congestion, frequent accidents, and noise pollution. The rapid advancement of intelligent autonomous driving technologies, particularly environmental perception technologies, offers new directions for solving these problems. This review discusses the application of multisensor information fusion technology in environmental perception for intelligent vehicles, analyzing the components and performance of various sensors and their specific applications in autonomous driving. Through multisensor information fusion, the accuracy of environmental perception is enhanced, optimizing decision support for autonomous driving systems and thereby improving vehicle safety and driving efficiency. This study also discusses the challenges faced by information fusion technology and future development trends, providing references for further research and application in intelligent transportation systems.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"163-176"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10600093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324356","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

Localization and Mapping Algorithm Based on Lidar-IMU-Camera Fusion 基于激光雷达-IMU-摄像头融合的定位和绘图算法

Journal of Intelligent and Connected Vehicles Pub Date : 2024-06-01 DOI: 10.26599/JICV.2023.9210027

Yibing Zhao;Yuhe Liang;Zhenqiang Ma;Lie Guo;Hexin Zhang

{"title":"Localization and Mapping Algorithm Based on Lidar-IMU-Camera Fusion","authors":"Yibing Zhao;Yuhe Liang;Zhenqiang Ma;Lie Guo;Hexin Zhang","doi":"10.26599/JICV.2023.9210027","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210027","url":null,"abstract":"Positioning and mapping technology is a difficult and hot topic in autonomous driving environment sensing systems. In a complex traffic environment, the signal of the Global Navigation Satellite System (GNSS) will be blocked, leading to inaccurate vehicle positioning. To ensure the security of automatic electric campus vehicles, this study is based on the Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain (LEGO-LOAM) algorithm with a monocular vision system added. An algorithm framework based on Lidar-IMU-Camera (Lidar means light detection and ranging) fusion was proposed. A lightweight monocular vision odometer model was used, and the LEGO-LOAM system was employed to initialize monocular vision. The visual odometer information was taken as the initial value of the laser odometer. At the back-end opti9mization phase error state, the Kalman filtering fusion algorithm was employed to fuse the visual odometer and LEGO-LOAM system for positioning. The visual word bag model was applied to perform loopback detection. Taking the test results into account, the laser radar loopback detection was further optimized, reducing the accumulated positioning error. The real car experiment results showed that our algorithm could improve the mapping quality and positioning accuracy in the campus environment. The Lidar-IMU-Camera algorithm framework was verified on the Hong Kong city dataset UrbanNav. Compared with the LEGO-LOAM algorithm, the results show that the proposed algorithm can effectively reduce map drift, improve map resolution, and output more accurate driving trajectory information.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"97-107"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543845","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

Segmented Trust Assessment in Autonomous Vehicles via Eye-Tracking 通过眼球跟踪对自动驾驶汽车进行分段信任评估

Journal of Intelligent and Connected Vehicles Pub Date : 2024-06-01 DOI: 10.26599/JICV.2023.9210037

Miklós Lukovics;Szabolcs Prónay;Barbara Nagy

{"title":"Segmented Trust Assessment in Autonomous Vehicles via Eye-Tracking","authors":"Miklós Lukovics;Szabolcs Prónay;Barbara Nagy","doi":"10.26599/JICV.2023.9210037","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210037","url":null,"abstract":"Previous studies have identified trust as one of the key factors in the technology acceptance of autonomous vehicles. As these studies mostly investigated the population in general, little is known about segment-specific differences. Furthermore, the widely used survey methods are less able to capture the deeper forms of trust—which neuroscientific methods are much better suited to capture. The main objective of our research is to study trust as one of the key factors of technology acceptance related to autonomous vehicles by using neuroscientific methods for specific consumer segments. Real-time eye-tracking tests were applied to a sample of 113 participants, combined with a posttest self-report. The tests were carried out under laboratory conditions during which our subjects watched videos recorded with the internal cameras of autonomous vehicles. Based on the fixation count, total fixation duration, and pupil dilation, we empirically verified that the trust level of all five identified segments is relatively low, while the trust level of the “traditional rejecting” segment is the lowest. An increase in trust level can be shown if the subjects receive extra information about the journey. Another important finding is that the self-reported trust level is not always congruent with the eye-tracking analysis results; therefore, combined approaches can lead to greater measurement validity.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"151-161"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543846","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

Kinematics-Aware Multigraph Attention Network with Residual Learning for Heterogeneous Trajectory Prediction 具有残差学习功能的运动学感知多图注意力网络用于异构轨迹预测

Journal of Intelligent and Connected Vehicles Pub Date : 2024-06-01 DOI: 10.26599/JICV.2023.9210036

Zihao Sheng;Zilin Huang;Sikai Chen

{"title":"Kinematics-Aware Multigraph Attention Network with Residual Learning for Heterogeneous Trajectory Prediction","authors":"Zihao Sheng;Zilin Huang;Sikai Chen","doi":"10.26599/JICV.2023.9210036","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210036","url":null,"abstract":"Trajectory prediction for heterogeneous traffic agents plays a crucial role in ensuring the safety and efficiency of automated driving in highly interactive traffic environments. Numerous studies in this area have focused on physics-based approaches because they can clearly interpret the dynamic evolution of trajectories. However, physics-based methods often suffer from limited accuracy. Recent learning-based methods have demonstrated better performance, but they cannot be fully trusted due to the insufficient incorporation of physical constraints. To mitigate the limitations of purely physics-based and learning-based approaches, this study proposes a kinematics-aware multigraph attention network (KA-MGAT) that incorporates physics models into a deep learning framework to improve the learning process of neural networks. Besides, we propose a residual prediction module to further refine the trajectory predictions and address the limitations arising from simplified assumptions in kinematic models. We evaluate our proposed model through experiments on two challenging trajectory datasets, namely, ApolloScape and NGSIM. Our findings from the experiments demonstrate that our model outperforms various kinematics-agnostic models with respect to prediction accuracy and learning efficiency.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"138-150"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586904","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543876","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