Accident; analysis and prevention最新文献

{"title":"Dynamic evaluation of emergency lane occupation based on an improved driving risk field model","authors":"Chuzhao Li ,&nbsp;Yangming Zhou ,&nbsp;Zhiyu Tao ,&nbsp;Guofa Li ,&nbsp;Zhou Wu","doi":"10.1016/j.aap.2025.108143","DOIUrl":"10.1016/j.aap.2025.108143","url":null,"abstract":"<div><div>In China, the frequent occupation of emergency lanes on highways significantly affects traffic efficiency and safety. This study collected data from six highway segments and extracted lane-changing (LC) behavior. A logistic regression model was employed to identify key factors influencing emergency lane changes (ELC). An improved driving risk field model was developed to assess risks during the LC process, integrating the heading angle of the obstacle vehicles, speeds of both the main and obstacle vehicles, as well as the filtering effect of lane lines. Compared to three existing risk field models, our model provides a more accurate assessment of collision risks during lane changes. This study revealed that the primary reasons for ELC are driven by the pursuit of speed and driving space, influenced by the speed and relative distance of surrounding vehicles, and the intention to overtake. Moreover, ELC is characterized by reduced lateral speed and acceleration, as well as a lower minimum time-to-collision (TTC), while exhibiting higher acceptable risks. Risk variation trends during lane changes from emergency to normal lanes include conservative, general, and aggressive types, while changes from normal to emergency lanes feature only conservative and general types. Additionally, ELC significantly negatively impacts following vehicles in the target lane after LC, leading to greater lateral displacement, more significant longitudinal deceleration, and heightened risk levels.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108143"},"PeriodicalIF":5.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A secure and privacy-preserving authentication framework for Connected and Autonomous Vehicles based on DRG-PBFT and zero-knowledge proof","authors":"Anjum Mohd Aslam,&nbsp;Aditya Bhardwaj,&nbsp;Rajat Chaudhary","doi":"10.1016/j.aap.2025.108145","DOIUrl":"10.1016/j.aap.2025.108145","url":null,"abstract":"<div><div>Connected and Autonomous Vehicles (CAVs) are pivotal to advancing Intelligent Transportation Systems (ITS) but introduce significant security and privacy challenges, particularly in dynamic environments requiring real-time data exchange. The existing security measures and consensus mechanisms, such as Practical Byzantine Fault Tolerance (PBFT), are susceptible to various attacks, including identity forgery, unauthorized access, and compromised safety testing, and suffer from scalability and latency issues. This study addresses these challenges by proposing a Dynamic Reputation Grouping-based PBFT (DRG-PBFT) approach, integrated with Simulation Extractable Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (SE-ZK-SNARKS). The proposed framework leverages reputation-based dynamic grouping to enhance consensus efficiency and reduce communication overhead. SE-ZK-SNARKS provide anonymity and privacy-preserving identity authentication, enabling CAVs to prove their legitimacy without revealing sensitive information. The proposed framework has been validated through extensive simulations using the NS-3 network simulator integrated with blockchain. The simulation results demonstrate that our proposed approach outperforms existing methods, achieving reduced consensus latency, communication overhead, authentication time and improved throughput. Overall, the findings and methodologies presented in this study address critical challenges in securing CAV communications while maintaining scalability and efficiency and can serve as a valuable reference for researchers and practitioners aiming to improve the safety and reliability of CAVs in real-time environments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108145"},"PeriodicalIF":5.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-Centric explanations for users in automated Vehicles: A systematic review","authors":"Zishuo Zhu ,&nbsp;Xiaomeng Li ,&nbsp;Patricia Delhomme ,&nbsp;Ronald Schroeter ,&nbsp;Sebastien Glaser ,&nbsp;Andry Rakotonirainy","doi":"10.1016/j.aap.2025.108152","DOIUrl":"10.1016/j.aap.2025.108152","url":null,"abstract":"<div><h3>Background</h3><div>The decision-making processes of automated vehicles (AVs) can confuse users and reduce trust, highlighting the need for clear and human-centric explanations. Such explanations can help users understand AV actions, facilitate smooth control transitions and enhance transparency, acceptance, and trust. Critically, such explanations could improve situational awareness and support timely, appropriate human responses, thereby reducing the risk of misuse, unexpected automated decisions, and delayed reactions in safety–critical scenarios. However, current literature offers limited insight into how different types of explanations impact drivers in diverse scenarios and the methods for evaluating their quality. This paper systematically reviews what, when and how to provide human-centric explanations in AV contexts.</div></div><div><h3>Methods</h3><div>The systematic review followed PRISMA guidelines, and covered five databases—Scopus, Web of Science, IEEE Xplore, TRID, and Semantic Scholar—from 2000 to April 2024. Out of 266 identified articles, 59 met the inclusion criteria.</div></div><div><h3>Results</h3><div>Providing a detailed content explanation following AV’s driving actions in real time does not always increase user trust and acceptance. Explanations that clarify the reasoning behind actions are more effective than those merely describing actions. Providing explanations before action is recommended, though the optimal timing remains uncertain. Multimodal explanations (visual and audio) are most effective when each mode conveys unique information; otherwise, visual-only explanations are preferred. The narrative perspective (first-person vs. third-person) also impacts user trust differently across scenarios.</div></div><div><h3>Conclusions</h3><div>The review underscores the importance of tailoring human-centric explanations to specific driving contexts. Future research should address explanation length, timing, and modality coordination and focus on real-world studies to enhance generalisability. These insights are vital for advancing the research of human-centric explanations in AV systems and fostering safer, more trustworthy human-vehicle interactions, ultimately reducing the risk of inappropriate reactions, delayed responses, or user error in traffic settings.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108152"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning salient representation of crashes and near-crashes using supervised contrastive variational autoencoder","authors":"Boyu Jiang ,&nbsp;Feng Guo","doi":"10.1016/j.aap.2025.108148","DOIUrl":"10.1016/j.aap.2025.108148","url":null,"abstract":"<div><div>Models capable of learning representations that are salient in safety–critical events (SCEs; including crashes and near-crashes) are crucial for road safety. This study proposes a novel deep learning model, the supervised contrastive variational autoencoder (scVAE), that incorporates supervised contrastive learning methods into the variational autoencoder (VAE) framework. By leveraging two distinct encoders, the scVAE encourages the salient latent variables to be discriminative, capturing the unique representations of SCEs while being regulated by the response variable to focus on the most relevant representations for accurate clustering. Through application on the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study kinematic datasets, we demonstrated the effectiveness of the scVAE in learning salient representations that enable improved clustering compared to alternative models. Quantitative analysis revealed clear clustering patterns in the learned salient representation space, facilitating downstream tasks such as generating samples, denoising, and prediction. The proposed approach of combining contrastive and supervised learning can be scaled to other model frameworks and data modalities, offering a promising direction for learning-enhanced representations that cater to tasks of interest. The study findings highlight the contributions of scVAE to traffic safety, offering enhanced capabilities for driving scenario generation and SCE detection.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108148"},"PeriodicalIF":5.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety and efficiency-oriented adaptive strategy controls for connected and automated vehicles in unstable communication environment","authors":"Yangzhen Zhao ,&nbsp;Xuedong Hua ,&nbsp;Weijie Yu ,&nbsp;Wenxie Lin ,&nbsp;Wei Wang ,&nbsp;Qihao Zhou","doi":"10.1016/j.aap.2025.108121","DOIUrl":"10.1016/j.aap.2025.108121","url":null,"abstract":"<div><div>Connected and automated vehicle (CAV) has demonstrated significant potential in enhancing traffic safety, efficiency, and environmental sustainability in the premise of high-quality communication environment. However, attributed to unexpected disturbances in real life, vehicular communication often suffers from unstable information transmission that lowers the performance of CAV platooning. Despite this drawback, existing research lacks sufficient attention on CAV platooning in the presence of unstable communication environment (UCE) and rarely addresses the response strategies. To fill these research gaps, this study integrates dynamic communication topologies into multi-class controls of automated and/or connected vehicles in response to various data abnormalities, based on simulation data generated from the modified intelligent driver model (IDM) and the communication disturbance framework designed to emulate UCE. The impact of UCE on CAV platooning performance is evaluated in terms of traffic safety and efficiency. The study introduces five adaptive dynamic strategy controls (ADSCs) and three hybrid switching ADSCs to enhance platoon performance under varying UCE conditions, along with recommendations on applicable ADSCs for maintaining the platoon performance under different UCE scenarios. Also, our sensitivity analysis of attenuation model parameters identifies optimal configurations for the development of ADSCs. Results show that continuous UCE (CUCE) poses greater safety challenges compared to intermittent UCE (IUCE), with combined IUCE being the most detrimental. Scenarios involving aggressive acceleration behavior significantly increase the collision risk and speed oscillation within the platoon. The proposed hybrid switching ADSCs significantly improve CAV platoon performance, offering resilient and adaptive vehicle control strategies to smooth traffic flow under disturbances.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108121"},"PeriodicalIF":5.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Directional vibrotactile takeover requests on a wrist-worn device: effects of age, pattern type, and urgency in automated driving","authors":"Wei-Hsiang Lo,&nbsp;Gaojian Huang","doi":"10.1016/j.aap.2025.108093","DOIUrl":"10.1016/j.aap.2025.108093","url":null,"abstract":"<div><div>Drivers are still required to perform the takeover task in highly automated vehicles. This task, which is cognitively and physically demanding, may present challenges for older adults due to general age-related declines in perception and cognition. Tactile modalities that may not be occupied by many non-driving-related tasks could serve as a potential solution for delivering takeover requests. Among these, directional vibrotactile stimuli presented via a wrist-worn device represent a promising approach. However, the effects of the two common types of directional vibrotactile patterns, dynamic patterns that vibrate sequentially at different locations and static patterns that vibrate at fixed locations, are still unknown. Therefore, this study aimed to investigate the effect of age (younger and older adults), vibrotactile pattern types (Baseline, Full-Dynamic, Semi-Dynamic, and Static), and interpulse interval (shorter (300 ms) and longer (800 ms)) on takeover performance. Forty participants (20 younger and 20 older adults) were engaged in the SAE Level 3 driving simulator study. Overall, Static and Baseline patterns were associated with faster reaction and takeover times and were perceived as more useful and satisfactory compared to the Full-Dynamic and Semi-Dynamic patterns. Shorter interpulse intervals (300 ms) for vibrotactile takeover requests resulted in better takeover performance, as indicated by shorter reaction and takeover times compared to longer interpulse intervals (800 ms). Finally, younger adults reacted faster to vibrotactile takeover requests than older adults did. The findings from the current study may inform the design of human–machine interfaces on wearable devices for next-generation automated vehicles.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108093"},"PeriodicalIF":5.7,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustering of freeway cut-in scenarios for automated vehicle development considering data dimensionality and imbalance","authors":"Penghui Li ,&nbsp;Qianru Dong ,&nbsp;Xiangjun Zhao ,&nbsp;Chao Lu ,&nbsp;Mengxia Hu ,&nbsp;Xuedong Yan ,&nbsp;Chunjiao Dong","doi":"10.1016/j.aap.2025.108151","DOIUrl":"10.1016/j.aap.2025.108151","url":null,"abstract":"<div><div>Representative driving scenarios derived by clustering of naturalistic driving data are guidelines for the function definition and algorithm development of automated vehicle. However, current clustering methods struggle with data dimensionality and imbalance, leading to significant biases. To tackle these issues, this study proposed a novel two-layer self-adaptive multiprototype-based competitive learning algorithm, and implemented it in clustering of freeway cut-in scenarios. Firstly, the extracted cut-in segments from naturalistic driving data included environmental, static, and dynamic vehicle elements, composed of discrete, continuous, and time series variables, posing a challenge in multi-dimensional parameter clustering. To tackle this, we utilized the K-medoids clustering method, based on dynamic time warping distance, to cluster variables such as cut-in vehicle velocity, converting them into discrete variables and applying one-hot encoding for easier clustering distance calculations. Secondly, to address the imbalance issue where minority sample categories were absorbed into majority types in naturalistic driving data clustering, we employed a multi-prototype clustering method in the second layer. Each cluster was represented by one or more sub-clusters to ensure adequate representation of minority clusters. Moreover, the inclusion of adaptive competitive learning allowed the algorithm to autonomously determine the optimal number of clusters, eliminating the need for manual parameter tuning. Consequently, the proposed algorithm produced eleven representative freeway cut-in scenarios from 2415 segments, with a better clustering goodness than the other traditional clustering methods. Moreover, four representative cut-in scenarios were frequently appeared in the dataset and commonly recognized by previous studies, whilst seven were rare in the dataset but common in real-world driving circumstances, such as at night, adverse weather conditions, and commercial vehicle cut-in scenarios. These findings suggest that the proposed clustering method effectively addresses the challenges of dimensionality and imbalance, indicating its potential for wide application in constructing representative scenarios for automated vehicles development.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108151"},"PeriodicalIF":5.7,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable stacking ensemble learning model for visual-manual distraction level classification for in-vehicle interactions","authors":"Yahui Wang ,&nbsp;Zhoushuo Liang ,&nbsp;Pengfei Tian ,&nbsp;Yue He","doi":"10.1016/j.aap.2025.108150","DOIUrl":"10.1016/j.aap.2025.108150","url":null,"abstract":"<div><div>Recognizing the level of driver distraction during the execution of secondary tasks within the intelligent cockpit is crucial for ensuring a seamless interaction between human drivers and intelligent vehicle systems. To address this issue, this paper proposes a framework for recognizing driver distraction levels that integrates clustering, classification, and interpretability. First, Feature Selection with Optimal Graph(SF²SOG) is employed to identify discriminative features from the data facilitating dimensionality reduction. Following this, Agglomerative Clustering are employed to classify the gathered unlabeled data on driving distraction behaviors into three distinct categories. Additionally, a heuristic-based stacking ensemble model is introduced to identify the levels of driver distraction, using the factors that influence these levels as input parameters for classification. To improve the effectiveness of the stacking model, three diverse classifiers adaptive boosting (AdaBoost)、random forest (RF) and extreme gradient boosting (XGBoost) are selected to serve as the base models, while a relatively simple yet accurate model logistic regression (LR) is used as the <em>meta</em>-classifier. Finally, Shapley Additive exPlanations (SHAP) is employed for interpretability analysis. Notably, heuristic-based stacking ensemble model achieved a commendable accuracy rate of 96.25%, highlighting its significant advantage. Further analysis shows that higher maximum pupil diameter (maxPD) and mean pupil diameter (meanPD) indicate increased distraction, while greater glance frequency and lane deviation reflect reduced situational awareness and control. These findings are crucial for reducing accidents and enhancing driving safety in the era of intelligent vehicles.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108150"},"PeriodicalIF":5.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learnable Operational Design Condition monitor for failure prediction in autonomous driving","authors":"Haowei Xu ,&nbsp;Yougang Bian ,&nbsp;Yang Li ,&nbsp;Hongmao Qin ,&nbsp;Hanchu Zhou ,&nbsp;Fangrong Chang ,&nbsp;Shaofei Wang ,&nbsp;Qing Ye","doi":"10.1016/j.aap.2025.108139","DOIUrl":"10.1016/j.aap.2025.108139","url":null,"abstract":"<div><div>Autonomous vehicle accidents frequently originate from violations of Operational Design Conditions (ODC)—the predefined operational limits of vehicle states, environmental factors, and driver capabilities. ODC violation indicates a high probability of impending functional failure under current operational scenarios, where failure denotes the system’s inability to achieve designated performance thresholds or maintain safety-critical constraints. Therefore, designing and continuously monitoring the boundary states of ODC during system operation constitutes a critical imperative to prevent system failures, ensure operational safety, and mitigate autonomous vehicle accidents. However, prevailing ODC monitoring methods primarily rely on first-order logic checklists, failing to capture emergent risks from parameter interactions. Therefore, this paper establishes an end-to-end methodological framework for constructing a learnable ODC monitor termed ODCNet to realize failure prediction. The architecture first projects operational states into unified latent representations, then derives probabilistic boundary estimates through neural inference, and finally calibrates residual errors via hybrid Gaussian Process regression. Additionally, an adaptive active learning mechanism continuously refines boundary precision through targeted testing of high-uncertainty scenarios. The validation through intersection, lane-keeping, and vehicle detection case studies demonstrates the failure prediction performance of the ODC monitor that precedes accidents.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108139"},"PeriodicalIF":5.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of sensory distractions on reaction time: a comprehensive modeling approach","authors":"Rajesh Chouhan ,&nbsp;Yogesh Bhagwan Chavan ,&nbsp;Ashish Dhamaniya ,&nbsp;Constantinos Antoniou","doi":"10.1016/j.aap.2025.108154","DOIUrl":"10.1016/j.aap.2025.108154","url":null,"abstract":"<div><div>Auditory distractions can significantly impact driving safety by affecting a driver’s ability to focus, react, and make decisions, and consequently increase Driver’s Reaction Time. The present study aims to examine the impact of such distractions using experimental data employing the Vienna Test System (VTS). Eighty drivers (70% male, 30% female) from mixed traffic environments participated, resulting in 240 data samples across two age groups (young and mature). Reaction time was assessed under three conditions: (i) Normal, (ii) Music, and (iii) Call using the VTS, which includes visual and auditory stimuli requiring physical reactions, thus closely simulating real-life driving scenarios. Results showed that male drivers’ reaction time increased by 4.8% and 20.1% under Music and Call conditions, respectively, while female drivers exhibited increases of 6.1% and 13.5%, respectively. The comparison of reaction time among young and mature groups under each condition indicated that Call distractions led to the highest increase (12.0%) in reaction time, followed by Normal (10.8%) and Music (8.3%). A second-degree polynomial equation was proposed to estimate reaction time from age. Additionally, the Weibull Accelerated Failure Time (AFT) model identified key factors affecting reaction time under normal conditions, resulting in a formulated mathematical expression. A validation experiment using real-world crash and near-crash videos reinforced the findings. The study provides empirical insights for enhancing safety measures and could be leveraged within advanced driver assistance systems (ADAS).</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108154"},"PeriodicalIF":5.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}