{"title":"基于高速公路匝道影响区车辆移动不确定性的改进型驾驶安全场模型","authors":"Yueru Xu, Wei Ye, Yalin Luan, Bingbo Cui","doi":"10.3390/systems12090370","DOIUrl":null,"url":null,"abstract":"Road traffic accidents result in numerous fatalities and injuries annually. Advanced driving assistance systems (ADASs) have garnered significant attention to mitigate these harms. An accurate safety assessment can significantly improve the effectiveness and credibility of ADASs. However, a real-time safety assessment remains a key challenge due to the complex interactions among humans, vehicles, and the road environment. Traditional safety assessment methods, relying on crash data and surrogate safety measures (SSMs), face limitations in real-time applicability and scenario coverage, especially in freeway ramp areas with frequent merging and lane changing. To address these gaps, this paper develops a driving safety field based on the uncertainty of vehicle movements, which integrates the characteristics of driving behaviors, vehicles, and the road environment. The proposed method is validated with a simulation of driving scenarios and ROC curves obtained from the NGSIM dataset. The results demonstrate that our proposed driving safety field effectively quantifies the real-time risk in ramp influence areas and outperforms Time to Collision (TTC), making it suitable for integration into collision warning systems of ADASs.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"16 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Improved Driving Safety Field Model Based on Vehicle Movement Uncertainty for Highway Ramp Influence Areas\",\"authors\":\"Yueru Xu, Wei Ye, Yalin Luan, Bingbo Cui\",\"doi\":\"10.3390/systems12090370\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Road traffic accidents result in numerous fatalities and injuries annually. Advanced driving assistance systems (ADASs) have garnered significant attention to mitigate these harms. An accurate safety assessment can significantly improve the effectiveness and credibility of ADASs. However, a real-time safety assessment remains a key challenge due to the complex interactions among humans, vehicles, and the road environment. Traditional safety assessment methods, relying on crash data and surrogate safety measures (SSMs), face limitations in real-time applicability and scenario coverage, especially in freeway ramp areas with frequent merging and lane changing. To address these gaps, this paper develops a driving safety field based on the uncertainty of vehicle movements, which integrates the characteristics of driving behaviors, vehicles, and the road environment. The proposed method is validated with a simulation of driving scenarios and ROC curves obtained from the NGSIM dataset. The results demonstrate that our proposed driving safety field effectively quantifies the real-time risk in ramp influence areas and outperforms Time to Collision (TTC), making it suitable for integration into collision warning systems of ADASs.\",\"PeriodicalId\":36394,\"journal\":{\"name\":\"Systems\",\"volume\":\"16 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://doi.org/10.3390/systems12090370\",\"RegionNum\":4,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SOCIAL SCIENCES, INTERDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.3390/systems12090370","RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOCIAL SCIENCES, INTERDISCIPLINARY","Score":null,"Total":0}
An Improved Driving Safety Field Model Based on Vehicle Movement Uncertainty for Highway Ramp Influence Areas
Road traffic accidents result in numerous fatalities and injuries annually. Advanced driving assistance systems (ADASs) have garnered significant attention to mitigate these harms. An accurate safety assessment can significantly improve the effectiveness and credibility of ADASs. However, a real-time safety assessment remains a key challenge due to the complex interactions among humans, vehicles, and the road environment. Traditional safety assessment methods, relying on crash data and surrogate safety measures (SSMs), face limitations in real-time applicability and scenario coverage, especially in freeway ramp areas with frequent merging and lane changing. To address these gaps, this paper develops a driving safety field based on the uncertainty of vehicle movements, which integrates the characteristics of driving behaviors, vehicles, and the road environment. The proposed method is validated with a simulation of driving scenarios and ROC curves obtained from the NGSIM dataset. The results demonstrate that our proposed driving safety field effectively quantifies the real-time risk in ramp influence areas and outperforms Time to Collision (TTC), making it suitable for integration into collision warning systems of ADASs.