Journal of Intelligent and Connected Vehicles最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"Critical Roles of Control Engineering in the Development of Intelligent and Connected Vehicles","authors":"Yang Fei;Peng Shi;Yang Liu;Liang Wang","doi":"10.26599/JICV.2023.9210040","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210040","url":null,"abstract":"In recent years, advancements in onboard computing hardware and wireless communication technology have remarkably stimulated the development of intelligent and connected vehicles (ICVs). Specifically, some researchers have investigated the issue of employing various advanced control techniques to optimize the performance of autonomous vehicles in practice (Sun et al., 2023; Zhang et al., 2023a, 2023b). Therefore, this article aims to discuss why and how control engineering plays an essential role in the development of ICVs.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"79-85"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586906","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543874","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}

{"title":"Online Learning-Based Model Predictive Trajectory Control for Connected and Autonomous Vehicles: Modeling and Physical Tests","authors":"Qianwen Li;Peng Zhang;Handong Yao;Zhiwei Chen;Xiaopeng Li","doi":"10.26599/JICV.2023.9210026","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210026","url":null,"abstract":"Motivated by the promising benefits of connected and autonomous vehicles (CAVs) in improving fuel efficiency, mitigating congestion, and enhancing safety, numerous theoretical models have been proposed to plan CAV multiple-step trajectories (time-specific speed/location trajectories) to accomplish various operations. However, limited efforts have been made to develop proper trajectory control techniques to regulate vehicle movements to follow multiple-step trajectories and test the performance of theoretical trajectory planning models with field experiments. Without an effective control method, the benefits of theoretical models for CAV trajectory planning can be difficult to harvest. This study proposes an online learning-based model predictive vehicle trajectory control structure to follow time-specific speed and location profiles. Unlike single-step controllers that are dominantly used in the literature, a multiple-step model predictive controller is adopted to control the vehicle's longitudinal movements for higher accuracy. The model predictive controller output (speed) cannot be interpreted by vehicles. A reinforcement learning agent is used to convert the speed value to the vehicle's direct control variable (i.e., throttle/brake). The reinforcement learning agent captures real-time changes in the operating environment. This is valuable in saving parameter calibration resources and improving trajectory control accuracy. A line tracking controller keeps vehicles on track. The proposed control structure is tested using reduced-scale robot cars. The adaptivity of the proposed control structure is demonstrated by changing the vehicle load. Then, experiments on two fundamental CAV platoon operations (i.e., platooning and split) show the effectiveness of the proposed trajectory control structure in regulating robot movements to follow time-specific reference trajectories.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"86-96"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586903","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543844","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}

{"title":"Roadside Cross-Camera Vehicle Tracking Combining Visual and Spatial-Temporal Information for a Cloud Control System","authors":"Bolin Gao;Zhuxin Li;Dong Zhang;Yanwei Liu;Jiaxing Chen;Ziyuan Lv","doi":"10.26599/JICV.2023.9210034","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210034","url":null,"abstract":"Roadside cameras play a crucial role in road traffic, serving as an indispensable part of integrated vehicle-road-cloud systems due to their extensive visibility and monitoring capabilities. Nevertheless, these cameras face challenges in continuously tracking targets across perception domains. To address the issue of tracking vehicles across nonoverlapping perception domains between cameras, we propose a cross-camera vehicle tracking method within a Vehicle-Road-Cloud system that integrates visual and spatiotemporal information. A Gaussian model with microlevel traffic features is trained using vehicle information obtained through online tracking. Finally, the association of vehicle targets is achieved through the Gaussian model combining time and visual feature information. The experimental results indicate that the proposed system demonstrates excellent performance.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"129-137"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586907","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141544049","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}

{"title":"Public Perception of Connected and Automated Vehicles: Benefits, Concerns, and Barriers from an Australian Perspective","authors":"Ali Matin;Hussein Dia","doi":"10.26599/JICV.2023.9210028","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210028","url":null,"abstract":"This study investigates the attitudes and concerns of the Australian public toward connected and autonomous vehicles (CAVs), and the factors influencing their willingness to adopt this technology. Through a comprehensive survey, a diverse group of respondents provided valuable insights toward various CAV scenarios such as riding in a vehicle with no driver, self-driving public transport, self-driving taxis, and heavy vehicles without drivers. The results highlight the significant impact of safety concerns about automated vehicles on individuals' attitudes across all scenarios. Higher levels of concern were associated with more negative attitudes, and a strong correlation between concerns and opposition underlines the necessity of addressing these apprehensions to build public trust and promote CAV adoption. Interestingly, nearly 70% of respondents felt uncomfortable driving next to a CAV, but they displayed more confidence in adopting automated public transport in the near future. Additionally, around 40% of participants indicated a strong willingness to purchase a CAV, primarily driven by the desire to reduce their carbon footprint and safety considerations. Notably, respondents with health conditions or disability exhibited heightened interest (almost double those without health conditions) in CAV technology. Gender differences emerged in attitudes and preferences toward CAVs, with women expressing a greater level of concern and perceiving higher barriers to CAV deployment. This emphasizes the importance of employing targeted approaches to address the specific concerns of different demographics. The study also underscores the role of trust in technology as a significant barrier to CAV deployment, ranking high among respondents' concerns. To overcome these challenges and facilitate successful CAV deployment, various strategies are suggested, including live demonstrations, dedicated routes for automated public transport, adoption incentives, and addressing liability concerns. The findings from this study offer valuable insights for government agencies, vehicle manufacturers, and stakeholders in promoting the successful implementation of CAVs. By understanding societal acceptance and addressing concerns, decision-makers can devise effective interventions and policies to ensure the safe and widespread adoption of CAVs in Australia. Moreover, vehicle manufacturers can leverage these results to consider design aspects that align with passenger preferences, thereby facilitating the broader acceptance and adoption of CAVs in the future. Finally, this research provides a significant contribution to the understanding of public perception and acceptance of CAVs in the Australian context. By guiding decision-making and informing strategies, the study lays the foundation for a safer and more effective integration of CAVs into the country's transportation landscape.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"108-128"},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10537112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543908","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}