{"title":"商用自适应巡航控制系统在各种驾驶情况下的动态特性:响应时间、字符串稳定性和非对称行为","authors":"Hwapyeong Yu, Hwasoo Yeo","doi":"10.1016/j.trc.2024.104931","DOIUrl":null,"url":null,"abstract":"<div><div>Adaptive Cruise Control (ACC) is a popular feature for long-distance highway driving due to its convenience. Research has been conducted on the driving characteristics of commercial ACC vehicles that could impact road capacity and congestion. While response time and string stability are major characteristics, previous studies have often overlooked their variations across driving situations. This study analyzes the dynamic characteristics of commercial ACC, including response time, string stability, and asymmetric behavior across different driving situations. A method is proposed to extract the response time of commercial ACC vehicles during cruising, decelerating, and accelerating situations, using cross-correlation and acceleration threshold methods. Phenomena that influence string stability are categorized based on driving situations focusing on their origin and features. This study identifies patterns in asymmetric behavior and presents a car-following model calibration process that incorporates observed features using the OpenACC dataset. The findings reveal distinct variations in response time across different driving situations, escalating in the sequence of deceleration, cruising, and acceleration. String instability during deceleration is influenced by the vehicle’s response time, while during acceleration, it stems from an expanded gap reduction process. ACC vehicles exhibit asymmetric behavior, with a reduced tolerance for gap changes. The Helly model, which integrates response times, asymmetric behavior, and maximum acceleration, accurately simulates vehicle movement and string instability. The observed variations in response time and asymmetric behavior across driving situations provide an understanding of the traffic hysteresis of commercial ACC vehicles. Furthermore, our analysis suggests that achieving string stability requires diverse approaches for each driving situation.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104931"},"PeriodicalIF":7.6000,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic characteristics of commercial Adaptive Cruise Control across driving situations: Response time, string stability, and asymmetric behavior\",\"authors\":\"Hwapyeong Yu, Hwasoo Yeo\",\"doi\":\"10.1016/j.trc.2024.104931\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Adaptive Cruise Control (ACC) is a popular feature for long-distance highway driving due to its convenience. Research has been conducted on the driving characteristics of commercial ACC vehicles that could impact road capacity and congestion. While response time and string stability are major characteristics, previous studies have often overlooked their variations across driving situations. This study analyzes the dynamic characteristics of commercial ACC, including response time, string stability, and asymmetric behavior across different driving situations. A method is proposed to extract the response time of commercial ACC vehicles during cruising, decelerating, and accelerating situations, using cross-correlation and acceleration threshold methods. Phenomena that influence string stability are categorized based on driving situations focusing on their origin and features. This study identifies patterns in asymmetric behavior and presents a car-following model calibration process that incorporates observed features using the OpenACC dataset. The findings reveal distinct variations in response time across different driving situations, escalating in the sequence of deceleration, cruising, and acceleration. String instability during deceleration is influenced by the vehicle’s response time, while during acceleration, it stems from an expanded gap reduction process. ACC vehicles exhibit asymmetric behavior, with a reduced tolerance for gap changes. The Helly model, which integrates response times, asymmetric behavior, and maximum acceleration, accurately simulates vehicle movement and string instability. The observed variations in response time and asymmetric behavior across driving situations provide an understanding of the traffic hysteresis of commercial ACC vehicles. Furthermore, our analysis suggests that achieving string stability requires diverse approaches for each driving situation.</div></div>\",\"PeriodicalId\":54417,\"journal\":{\"name\":\"Transportation Research Part C-Emerging Technologies\",\"volume\":\"170 \",\"pages\":\"Article 104931\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2024-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Research Part C-Emerging Technologies\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0968090X24004522\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"TRANSPORTATION SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part C-Emerging Technologies","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968090X24004522","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Dynamic characteristics of commercial Adaptive Cruise Control across driving situations: Response time, string stability, and asymmetric behavior
Adaptive Cruise Control (ACC) is a popular feature for long-distance highway driving due to its convenience. Research has been conducted on the driving characteristics of commercial ACC vehicles that could impact road capacity and congestion. While response time and string stability are major characteristics, previous studies have often overlooked their variations across driving situations. This study analyzes the dynamic characteristics of commercial ACC, including response time, string stability, and asymmetric behavior across different driving situations. A method is proposed to extract the response time of commercial ACC vehicles during cruising, decelerating, and accelerating situations, using cross-correlation and acceleration threshold methods. Phenomena that influence string stability are categorized based on driving situations focusing on their origin and features. This study identifies patterns in asymmetric behavior and presents a car-following model calibration process that incorporates observed features using the OpenACC dataset. The findings reveal distinct variations in response time across different driving situations, escalating in the sequence of deceleration, cruising, and acceleration. String instability during deceleration is influenced by the vehicle’s response time, while during acceleration, it stems from an expanded gap reduction process. ACC vehicles exhibit asymmetric behavior, with a reduced tolerance for gap changes. The Helly model, which integrates response times, asymmetric behavior, and maximum acceleration, accurately simulates vehicle movement and string instability. The observed variations in response time and asymmetric behavior across driving situations provide an understanding of the traffic hysteresis of commercial ACC vehicles. Furthermore, our analysis suggests that achieving string stability requires diverse approaches for each driving situation.
期刊介绍:
Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.