{"title":"基于风险场并考虑驾驶员视野特征的多车交叉口驾驶行为模型","authors":"Zhaojie Wang;Guangquan Lu;Haitian Tan","doi":"10.1109/TITS.2024.3465442","DOIUrl":null,"url":null,"abstract":"In most studies on modeling driving behavior at uncontrolled intersections, multi-vehicle interaction scenarios are usually categorized and modeled separately as moving-across behavior and merging behavior. However, it is inappropriate to use a single-behavior model to accurately represent general driving behavior in uncontrolled intersections. In this case, we constructed a general driving behavior model for multi-vehicle interaction at uncontrolled intersections. Initially, the IMM model is employed to anticipate the movement of the vehicle within the driver’s visual field. The risk field theory is applied to assess potential hazards that the vehicle might confront, drawing from the risk homeostasis theory and preview-follower theory, which aids in determining a trajectory that aligns with the drivers’ real-life actions while also meeting the risk constraints. Drivers’ heterogeneity is reflected by risk threshold. This model can simulate driver behavior in traffic congestion at uncontrolled intersections by adjusting risk thresholds when the vehicles are caught in a deadlock situation. Results show that our model can accurately reproduce the priority and trajectory of vehicles crossing the intersection and resolve multi-vehicle conflicts within a reasonable time. This model can be used for traffic simulation at uncontrolled intersections and to provide test validation for automated driving systems.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15532-15546"},"PeriodicalIF":7.9000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Driving Behavior Model for Multi-Vehicle Interaction at Uncontrolled Intersections Based on Risk Field Considering Drivers’ Visual Field Characteristics\",\"authors\":\"Zhaojie Wang;Guangquan Lu;Haitian Tan\",\"doi\":\"10.1109/TITS.2024.3465442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In most studies on modeling driving behavior at uncontrolled intersections, multi-vehicle interaction scenarios are usually categorized and modeled separately as moving-across behavior and merging behavior. However, it is inappropriate to use a single-behavior model to accurately represent general driving behavior in uncontrolled intersections. In this case, we constructed a general driving behavior model for multi-vehicle interaction at uncontrolled intersections. Initially, the IMM model is employed to anticipate the movement of the vehicle within the driver’s visual field. The risk field theory is applied to assess potential hazards that the vehicle might confront, drawing from the risk homeostasis theory and preview-follower theory, which aids in determining a trajectory that aligns with the drivers’ real-life actions while also meeting the risk constraints. Drivers’ heterogeneity is reflected by risk threshold. This model can simulate driver behavior in traffic congestion at uncontrolled intersections by adjusting risk thresholds when the vehicles are caught in a deadlock situation. Results show that our model can accurately reproduce the priority and trajectory of vehicles crossing the intersection and resolve multi-vehicle conflicts within a reasonable time. This model can be used for traffic simulation at uncontrolled intersections and to provide test validation for automated driving systems.\",\"PeriodicalId\":13416,\"journal\":{\"name\":\"IEEE Transactions on Intelligent Transportation Systems\",\"volume\":\"25 11\",\"pages\":\"15532-15546\"},\"PeriodicalIF\":7.9000,\"publicationDate\":\"2024-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Intelligent Transportation Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10704974/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Intelligent Transportation Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10704974/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Driving Behavior Model for Multi-Vehicle Interaction at Uncontrolled Intersections Based on Risk Field Considering Drivers’ Visual Field Characteristics
In most studies on modeling driving behavior at uncontrolled intersections, multi-vehicle interaction scenarios are usually categorized and modeled separately as moving-across behavior and merging behavior. However, it is inappropriate to use a single-behavior model to accurately represent general driving behavior in uncontrolled intersections. In this case, we constructed a general driving behavior model for multi-vehicle interaction at uncontrolled intersections. Initially, the IMM model is employed to anticipate the movement of the vehicle within the driver’s visual field. The risk field theory is applied to assess potential hazards that the vehicle might confront, drawing from the risk homeostasis theory and preview-follower theory, which aids in determining a trajectory that aligns with the drivers’ real-life actions while also meeting the risk constraints. Drivers’ heterogeneity is reflected by risk threshold. This model can simulate driver behavior in traffic congestion at uncontrolled intersections by adjusting risk thresholds when the vehicles are caught in a deadlock situation. Results show that our model can accurately reproduce the priority and trajectory of vehicles crossing the intersection and resolve multi-vehicle conflicts within a reasonable time. This model can be used for traffic simulation at uncontrolled intersections and to provide test validation for automated driving systems.
期刊介绍:
The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.