Pub Date : 2024-01-02DOI: 10.1080/15472450.2022.2095206
Wenyan Hu , Stephan Winter , Kourosh Khoshelham
Ephemeral incidents, or events in traffic or on the roadside that have only local and short-term impact on road safety and road capacity, are noteworthy for vehicles nearby—especially those approaching and planning to pass by. We study ways to communicate detected ephemeral incidents between connected vehicles, comparing various decentralized (vehicle-to-vehicle) communication strategies and weighing with established centralized mechanisms with regard to efficiency and broadcasting redundancy. The strategies are implemented in a simulation using realistic road networks, travel routes and traffic. We identify the strategy that achieves up to 100% success rate in transmitting incident messages to the affected vehicles under each scenario, while minimizing broadcast redundancy. In general, decentralized vehicle-to-vehicle communication strategies show strong potential to transmit incident messages efficiently and effectively.
{"title":"Decentralized spreading of ephemeral road incident information between vehicles","authors":"Wenyan Hu , Stephan Winter , Kourosh Khoshelham","doi":"10.1080/15472450.2022.2095206","DOIUrl":"10.1080/15472450.2022.2095206","url":null,"abstract":"<div><p>Ephemeral incidents, or events in traffic or on the roadside that have only local and short-term impact on road safety and road capacity, are noteworthy for vehicles nearby—especially those approaching and planning to pass by. We study ways to communicate detected ephemeral incidents between connected vehicles, comparing various decentralized (vehicle-to-vehicle) communication strategies and weighing with established centralized mechanisms with regard to efficiency and broadcasting redundancy. The strategies are implemented in a simulation using realistic road networks, travel routes and traffic. We identify the strategy that achieves up to 100% <em>success rate</em> in transmitting incident messages to the affected vehicles under each scenario, while minimizing broadcast redundancy. In general, decentralized vehicle-to-vehicle communication strategies show strong potential to transmit incident messages efficiently and effectively.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 1","pages":"Pages 16-30"},"PeriodicalIF":3.6,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75209160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-02DOI: 10.1080/15472450.2022.2109417
Rafael Peralta , Israel Becerra , Ubaldo Ruiz , Rafael Murrieta-Cid
This work is about the generation of driving styles for autonomous cars. Here, we propose a definition of driving style based on the partition of controller parameters for self-driving vehicles. The main contributions of this work are the following. 1) A methodology based on the controllers’ parameters for creating comfortable driving styles that can be used as autonomous cars’ operation modes. 2) A proposal to use virtual reality as a testbed for the evaluation of driving styles by users. 3) As an illustration of our methodology, we determine and evaluate distinguishable driving styles by partitioning the time-to-collision parameter of the Intelligent Driver Model (IDM) controller using the Just Noticeable Difference (JND). 4) A proposal of four driving styles that are equally preferable among passengers.
{"title":"A methodology for generating driving styles for autonomous cars","authors":"Rafael Peralta , Israel Becerra , Ubaldo Ruiz , Rafael Murrieta-Cid","doi":"10.1080/15472450.2022.2109417","DOIUrl":"10.1080/15472450.2022.2109417","url":null,"abstract":"<div><p>This work is about the generation of driving styles for autonomous cars. Here, we propose a definition of driving style based on the partition of controller parameters for self-driving vehicles. The main contributions of this work are the following. 1) A methodology based on the controllers’ parameters for creating comfortable driving styles that can be used as autonomous cars’ operation modes. 2) A proposal to use virtual reality as a testbed for the evaluation of driving styles by users. 3) As an illustration of our methodology, we determine and evaluate distinguishable driving styles by partitioning the time-to-collision parameter of the Intelligent Driver Model (IDM) controller using the Just Noticeable Difference (JND). 4) A proposal of four driving styles that are equally preferable among passengers.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 1","pages":"Pages 120-140"},"PeriodicalIF":3.6,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72485415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-02DOI: 10.1080/15472450.2022.2106564
Zihe Zhang , Qifan Nie , Jun Liu , Alex Hainen , Naima Islam , Chenxuan Yang
Real-time prediction of crash risk can support traffic incident management by generating critical information for practitioners to allocate resources for responding to anticipated traffic crashes proactively. Unlike previous studies using archived traffic data covering a limited highway environment such as a segment or corridor, this study uses a statewide live traffic database from HERE to develop real-time traffic crash prediction models. This database provides crowdsourced probe vehicle data that are high-resolution real-time traffic speed for the entire freeway network (nearly 2,000 miles) in Alabama. This study aims to use machine learning models to predict crash risk on freeways according to pre-crash traffic dynamics (e.g., mean speed, speed reduction) along with static freeway attributes. Traffic speed characteristics were extracted from the HERE database for both pre-crash and crash-free traffic conditions. Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were developed and compared. Separate models were estimated for three major crash types: single-vehicle, rear-end, and sideswipe crashes. The model prediction accuracy indicated that the RF models outperform other models. Models for rear-end crashes are found to have greater accuracy than other models, which implies that rear-end crashes have a significant relationship with pre-crash traffic dynamics and are more predictable. The traffic speed factors that are ranked high in terms of feature importance are the speed variance and speed reduction prior to crashes. According to partial dependence plots, the rear-end crash risk is positively related to the speed variance and speed reductions. More results are discussed in the paper.
碰撞风险的实时预测可以为交通事故管理提供支持,为从业人员分配资源以积极应对预期的交通事故提供重要信息。与以往使用覆盖有限高速公路环境(如路段或走廊)的存档交通数据的研究不同,本研究使用 HERE 的全州实时交通数据库来开发实时交通事故预测模型。该数据库提供的众包探测车辆数据是阿拉巴马州整个高速公路网络(近 2000 英里)的高分辨率实时交通速度。本研究旨在使用机器学习模型,根据碰撞前的交通动态(如平均车速、车速降低)以及高速公路的静态属性来预测高速公路上的碰撞风险。从 HERE 数据库中提取了碰撞前和无碰撞交通状况下的车速特征。开发并比较了随机森林 (RF)、支持向量机 (SVM) 和极端梯度提升 (XGBoost)。针对三种主要碰撞类型(单车碰撞、追尾碰撞和侧擦碰撞)分别估算了模型。模型预测准确性表明,RF 模型优于其他模型。追尾碰撞事故模型的准确性高于其他模型,这意味着追尾碰撞事故与碰撞前的交通动态有重要关系,并且更容易预测。就特征重要性而言,排名靠前的交通速度因素是速度方差和碰撞前速度降低。根据偏倚图,追尾碰撞风险与速度方差和速度降低呈正相关。本文讨论了更多结果。
{"title":"Machine learning based real-time prediction of freeway crash risk using crowdsourced probe vehicle data","authors":"Zihe Zhang , Qifan Nie , Jun Liu , Alex Hainen , Naima Islam , Chenxuan Yang","doi":"10.1080/15472450.2022.2106564","DOIUrl":"10.1080/15472450.2022.2106564","url":null,"abstract":"<div><p>Real-time prediction of crash risk can support traffic incident management by generating critical information for practitioners to allocate resources for responding to anticipated traffic crashes proactively. Unlike previous studies using archived traffic data covering a limited highway environment such as a segment or corridor, this study uses a statewide live traffic database from HERE to develop real-time traffic crash prediction models. This database provides crowdsourced probe vehicle data that are high-resolution real-time traffic speed for the entire freeway network (nearly 2,000 miles) in Alabama. This study aims to use machine learning models to predict crash risk on freeways according to pre-crash traffic dynamics (e.g., mean speed, speed reduction) along with static freeway attributes. Traffic speed characteristics were extracted from the HERE database for both pre-crash and crash-free traffic conditions. Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were developed and compared. Separate models were estimated for three major crash types: single-vehicle, rear-end, and sideswipe crashes. The model prediction accuracy indicated that the RF models outperform other models. Models for rear-end crashes are found to have greater accuracy than other models, which implies that rear-end crashes have a significant relationship with pre-crash traffic dynamics and are more predictable. The traffic speed factors that are ranked high in terms of feature importance are the speed variance and speed reduction prior to crashes. According to partial dependence plots, the rear-end crash risk is positively related to the speed variance and speed reductions. More results are discussed in the paper.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 1","pages":"Pages 84-102"},"PeriodicalIF":3.6,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89311787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-14DOI: 10.1080/15472450.2023.2291680
Yuening Hu, Dan Zhao, Ying Wang, Guangming Zhao
Autonomous vehicles (AVs) hold great potential to improve traffic safety. However, urban streets present a dynamic environment where unforeseen and complex scenarios can arise. The establishment of...
{"title":"DAnoScenE: a driving anomaly scenario extraction framework for autonomous vehicles in urban streets","authors":"Yuening Hu, Dan Zhao, Ying Wang, Guangming Zhao","doi":"10.1080/15472450.2023.2291680","DOIUrl":"https://doi.org/10.1080/15472450.2023.2291680","url":null,"abstract":"Autonomous vehicles (AVs) hold great potential to improve traffic safety. However, urban streets present a dynamic environment where unforeseen and complex scenarios can arise. The establishment of...","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"15 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138689862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.1080/15472450.2023.2289149
Dhwani Shah, Chris Lee, Yong Hoon Kim
Car following (CF) models are used in microscopic traffic simulation tools to help assess the effects of a new road design or to assess the effect of change in traffic flow. In 1981, Gipps develope...
{"title":"Modified Gipps model: a collision-free car following model","authors":"Dhwani Shah, Chris Lee, Yong Hoon Kim","doi":"10.1080/15472450.2023.2289149","DOIUrl":"https://doi.org/10.1080/15472450.2023.2289149","url":null,"abstract":"Car following (CF) models are used in microscopic traffic simulation tools to help assess the effects of a new road design or to assess the effect of change in traffic flow. In 1981, Gipps develope...","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"1 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138689918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.1080/15472450.2023.2289123
Minh Hieu Nguyen, Soohyun Kim, Sung Bum Yun, Sangyoon Park, Joon Heo
Service area analysis is crucial for determining the accessibility of public facilities in smart cities. However, the acquisition of service areas using conventional approaches has been limited. Fi...
{"title":"An efficient data-driven method to construct dynamic service areas from large-scale taxi location data","authors":"Minh Hieu Nguyen, Soohyun Kim, Sung Bum Yun, Sangyoon Park, Joon Heo","doi":"10.1080/15472450.2023.2289123","DOIUrl":"https://doi.org/10.1080/15472450.2023.2289123","url":null,"abstract":"Service area analysis is crucial for determining the accessibility of public facilities in smart cities. However, the acquisition of service areas using conventional approaches has been limited. Fi...","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"194 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138548496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.1080/15472450.2023.2289118
Evangelos Mintsis, Eleni I. Vlahogianni, Evangelos Mitsakis, Georgia Aifadopoulou
Research in the field of dynamic eco-driving has been primarily coupled with connected and automated vehicles which are equipped with automation functions that can accurately execute energy-efficie...
动态生态驾驶领域的研究主要与联网和自动化车辆相结合,这些车辆配备了自动化功能,可以准确地执行节能…
{"title":"Advisory versus automated dynamic eco-driving at signalized intersections: lessons learnt from empirical evidence and simulation experiments","authors":"Evangelos Mintsis, Eleni I. Vlahogianni, Evangelos Mitsakis, Georgia Aifadopoulou","doi":"10.1080/15472450.2023.2289118","DOIUrl":"https://doi.org/10.1080/15472450.2023.2289118","url":null,"abstract":"Research in the field of dynamic eco-driving has been primarily coupled with connected and automated vehicles which are equipped with automation functions that can accurately execute energy-efficie...","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"314 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AbstractUrban rail transit is an integral part of public transit, and has been extensive built in China. Previous studies have proved that the spatial features are closely related to rail transit ridership, considering a fundamental role of short-term passenger flow forecast in the urban rail operation, it is meaningful to explore how these factors affect the prediction accuracy. This study aims to find a way to improve prediction accuracy by considering spatial features of stations based on deep learning. Therefore, a CNN-LSTM model capturing the spatial and temporal features was applied and Suzhou (China) was choosing as a case study to explore the influence of three spatial features, namely relative position, location, and land use, on the prediction accuracy. The predict model used can extract spatiotemporal features and accurately predict the citywide stations, and the results show that, for the relative position, the inbound and outbound flow prediction errors of transfer stations and middle stations are the lowest, respectively. As for locational features, the more distant the station is from the city center, the more accurate the results are. For stations where land use is dominated by work and living services, the predictions are more accurate. The error rate is higher for stations whose services are mainly tourism, transportation, and leisure services. This study’s results can help operators predict the short-term passenger flow of target stations based on different demands and optimize their services on this basis.Keywords: CNN-LSTMpassenger flowprediction accuracyspatiotemporal featuresurban rail transit AcknowledgementsThe authors are grateful for the dataset from Suzhou rail transit Group Co., Ltd, and we are grateful for the advice of Prof. Ziyuan Pu.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research is supported by Postgraduate Research & Practice Innovation Program of Jiangsu Province (project number: KYCX22_0271).
{"title":"How spatial features affect urban rail transit prediction accuracy: a deep learning based passenger flow prediction method","authors":"Shuang Li, Xiaoxi Liang, Meina Zheng, Junlan Chen, Ting Chen, Xiucheng Guo","doi":"10.1080/15472450.2023.2279633","DOIUrl":"https://doi.org/10.1080/15472450.2023.2279633","url":null,"abstract":"AbstractUrban rail transit is an integral part of public transit, and has been extensive built in China. Previous studies have proved that the spatial features are closely related to rail transit ridership, considering a fundamental role of short-term passenger flow forecast in the urban rail operation, it is meaningful to explore how these factors affect the prediction accuracy. This study aims to find a way to improve prediction accuracy by considering spatial features of stations based on deep learning. Therefore, a CNN-LSTM model capturing the spatial and temporal features was applied and Suzhou (China) was choosing as a case study to explore the influence of three spatial features, namely relative position, location, and land use, on the prediction accuracy. The predict model used can extract spatiotemporal features and accurately predict the citywide stations, and the results show that, for the relative position, the inbound and outbound flow prediction errors of transfer stations and middle stations are the lowest, respectively. As for locational features, the more distant the station is from the city center, the more accurate the results are. For stations where land use is dominated by work and living services, the predictions are more accurate. The error rate is higher for stations whose services are mainly tourism, transportation, and leisure services. This study’s results can help operators predict the short-term passenger flow of target stations based on different demands and optimize their services on this basis.Keywords: CNN-LSTMpassenger flowprediction accuracyspatiotemporal featuresurban rail transit AcknowledgementsThe authors are grateful for the dataset from Suzhou rail transit Group Co., Ltd, and we are grateful for the advice of Prof. Ziyuan Pu.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research is supported by Postgraduate Research & Practice Innovation Program of Jiangsu Province (project number: KYCX22_0271).","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"37 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135036902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1080/15472450.2023.2277713
Michelangelo-Santo Gulino, Krzysztof Damaziak, Anita Fiorentino, Dario Vangi
AbstractThe downward trend in the number of fatalities and serious injuries related to road accidents depends on the implementation of increasingly performing Advanced Driver Assistance Systems (ADAS) in the circulating fleet. The greatest benefit of the adoption of ADASs like Autonomous Emergency Braking (AEB) consists in limiting the frequency of impacts. However, in Inevitable Collision States (ICSs), the decrease in impact closing speed guaranteed by the AEB may not reduce the Injury Risk (IR) for the occupants: IR is a function of the vehicle’s velocity change in the collision (ΔV) – a combination of impact closing speed and impact eccentricity. The work virtually analyses, in lane departure ICS scenarios, the performance of an adaptive steering and braking intervention logic based on instantaneous IR minimization. The adaptive logic reduces IR compared to the absence of intervention (down to 80 times lower) and to the AEB (down to 40 times lower) by leading the ego vehicle toward eccentric impact configurations. It is highlighted that full activation of the steer-by-wire system in 0.3 s allows the adaptive logic to also reduce the frequency of impacts; it is further evidenced that employing a function capable of modulating the braking level to minimize IR entails disadvantages from the IR perspective compared to the AEB: efficient intervention strategies on the steering are the only alternative for increasing the safety provided by high-performance ADASs. Finally, compared to previous literature, the study highlights high efficiencies of the adaptive logic in a wide range of ICS scenarios.Keywords: ΔVactuation timeautonomous emergency braking AEBimpact closing speedscan timevelocity change Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://transport.ec.europa.eu/news/road-safety-eu-fatalities-below-pre-pandemic-levels-progress-remains-too-slow-2023-02-21_en2 https://www.acea.auto/figure/average-age-of-eu-vehicle-fleet-by-country/3 EC Regulation n ° 661/20094 https://www.nissan-global.com/EN/INNOVATION/TECHNOLOGY/ARCHIVE/AUTONOMOUS_EMERGENCY_STEERING_SYSTEM/5 http://iglad.net/
{"title":"Handling inevitable collision states by Advanced Driver Assistance Systems functions: software-in-the-loop performance assessment of an injury risk-based logic in a “lane departure” scenario","authors":"Michelangelo-Santo Gulino, Krzysztof Damaziak, Anita Fiorentino, Dario Vangi","doi":"10.1080/15472450.2023.2277713","DOIUrl":"https://doi.org/10.1080/15472450.2023.2277713","url":null,"abstract":"AbstractThe downward trend in the number of fatalities and serious injuries related to road accidents depends on the implementation of increasingly performing Advanced Driver Assistance Systems (ADAS) in the circulating fleet. The greatest benefit of the adoption of ADASs like Autonomous Emergency Braking (AEB) consists in limiting the frequency of impacts. However, in Inevitable Collision States (ICSs), the decrease in impact closing speed guaranteed by the AEB may not reduce the Injury Risk (IR) for the occupants: IR is a function of the vehicle’s velocity change in the collision (ΔV) – a combination of impact closing speed and impact eccentricity. The work virtually analyses, in lane departure ICS scenarios, the performance of an adaptive steering and braking intervention logic based on instantaneous IR minimization. The adaptive logic reduces IR compared to the absence of intervention (down to 80 times lower) and to the AEB (down to 40 times lower) by leading the ego vehicle toward eccentric impact configurations. It is highlighted that full activation of the steer-by-wire system in 0.3 s allows the adaptive logic to also reduce the frequency of impacts; it is further evidenced that employing a function capable of modulating the braking level to minimize IR entails disadvantages from the IR perspective compared to the AEB: efficient intervention strategies on the steering are the only alternative for increasing the safety provided by high-performance ADASs. Finally, compared to previous literature, the study highlights high efficiencies of the adaptive logic in a wide range of ICS scenarios.Keywords: ΔVactuation timeautonomous emergency braking AEBimpact closing speedscan timevelocity change Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://transport.ec.europa.eu/news/road-safety-eu-fatalities-below-pre-pandemic-levels-progress-remains-too-slow-2023-02-21_en2 https://www.acea.auto/figure/average-age-of-eu-vehicle-fleet-by-country/3 EC Regulation n ° 661/20094 https://www.nissan-global.com/EN/INNOVATION/TECHNOLOGY/ARCHIVE/AUTONOMOUS_EMERGENCY_STEERING_SYSTEM/5 http://iglad.net/","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"3 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135391417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}