Extending the decision-making process during yellow phase from human drivers to autonomous vehicles: A microsimulation study with safety considerations

IF 7.4 2区 工程技术 Q1 ENGINEERING, CIVIL
Efthymis Papadopoulos, Anastasia Nikolaidou, Emmanouil Lilis, Ioannis Politis, Panagiotis Papaioannou
{"title":"Extending the decision-making process during yellow phase from human drivers to autonomous vehicles: A microsimulation study with safety considerations","authors":"Efthymis Papadopoulos,&nbsp;Anastasia Nikolaidou,&nbsp;Emmanouil Lilis,&nbsp;Ioannis Politis,&nbsp;Panagiotis Papaioannou","doi":"10.1016/j.jtte.2023.06.004","DOIUrl":null,"url":null,"abstract":"<div><p>One of the main factors affecting the safety of signalised intersections is the stop/go behaviour during the yellow interval. Although previous research has exhaustively examined drivers' stop/go decision-making, the expected autonomous vehicles' (AVs') stop/go behaviour has not yet been thoroughly investigated. Through a series of simulation experiments developed for conventional and autonomous vehicles using different car-following, lane-changing, lateral placement and stop/go model parameter values, we examine here whether the default VISSIM stop/go parameter values can adequately replicate the observed drivers' behaviour at the considered intersection and assess the suitability of using the currently available options, albeit referring to human drivers, to simulate the expected stop/go behaviour of AVs. We also propose a policy framework for determining the desired behaviour of AVs in yellow interval, which is integrated into an AVs logic and achieved in the last simulation to explore the effect of automation on the stop/go outcome and, hence, on the safety level of signalised intersections. Several data analysis and modeling techniques were used for the formulation of certain scenarios, including binary choice models. The default stop/go parameter values were found unfit to replicate the observed stop/go behaviour and subjected to calibration. Compared to the currently available options, the proposed AVs logic proved to produce the most accurate results, in terms of the stop/go simulation outcome. Regarding the impact of automation on the stop/go outcome, the simulation experiments showed that AVs preferred a more conservative behaviour in favor of road safety, as indicated by the significant reduction (≈15%) in the number of vehicles crossing the stop line during the yellow light and zero instances of red light violation. However, compared to the conservative drivers represented by the default stop/go parameter values, AVs preferred a more rational behaviour in favor of intersection capacity without compromising road safety.</p></div>","PeriodicalId":47239,"journal":{"name":"Journal of Traffic and Transportation Engineering-English Edition","volume":null,"pages":null},"PeriodicalIF":7.4000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095756424000242/pdfft?md5=111a42a9fba1129f123d2511bcfbe389&pid=1-s2.0-S2095756424000242-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Traffic and Transportation Engineering-English Edition","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2095756424000242","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0

Abstract

One of the main factors affecting the safety of signalised intersections is the stop/go behaviour during the yellow interval. Although previous research has exhaustively examined drivers' stop/go decision-making, the expected autonomous vehicles' (AVs') stop/go behaviour has not yet been thoroughly investigated. Through a series of simulation experiments developed for conventional and autonomous vehicles using different car-following, lane-changing, lateral placement and stop/go model parameter values, we examine here whether the default VISSIM stop/go parameter values can adequately replicate the observed drivers' behaviour at the considered intersection and assess the suitability of using the currently available options, albeit referring to human drivers, to simulate the expected stop/go behaviour of AVs. We also propose a policy framework for determining the desired behaviour of AVs in yellow interval, which is integrated into an AVs logic and achieved in the last simulation to explore the effect of automation on the stop/go outcome and, hence, on the safety level of signalised intersections. Several data analysis and modeling techniques were used for the formulation of certain scenarios, including binary choice models. The default stop/go parameter values were found unfit to replicate the observed stop/go behaviour and subjected to calibration. Compared to the currently available options, the proposed AVs logic proved to produce the most accurate results, in terms of the stop/go simulation outcome. Regarding the impact of automation on the stop/go outcome, the simulation experiments showed that AVs preferred a more conservative behaviour in favor of road safety, as indicated by the significant reduction (≈15%) in the number of vehicles crossing the stop line during the yellow light and zero instances of red light violation. However, compared to the conservative drivers represented by the default stop/go parameter values, AVs preferred a more rational behaviour in favor of intersection capacity without compromising road safety.

将黄灯阶段的决策过程从人类驾驶员扩展到自动驾驶车辆:考虑安全因素的微观模拟研究
影响信号交叉口安全的主要因素之一是黄灯间隔期间的停/走行为。虽然以往的研究已经对驾驶员的停车/驶离决策进行了详尽的研究,但对自动驾驶车辆(AV)的预期停车/驶离行为尚未进行深入研究。通过使用不同的跟车、变道、横向位置和停/走模型参数值对传统车辆和自动驾驶车辆进行一系列模拟实验,我们在此考察了 VISSIM 默认的停/走参数值是否能充分复制在所考虑的交叉路口观察到的驾驶员行为,并评估了使用当前可用选项(尽管是参照人类驾驶员)模拟自动驾驶车辆预期停/走行为的适宜性。我们还提出了一个政策框架,用于确定黄色区间内自动驾驶汽车的预期行为,并将其整合到自动驾驶汽车的逻辑中,在最后一次模拟中实现,以探索自动化对停止/通行结果的影响,进而对信号交叉口的安全水平的影响。在制定某些方案时使用了多种数据分析和建模技术,包括二元选择模型。发现默认的停止/通行参数值不适合复制观察到的停止/通行行为,因此进行了校准。事实证明,与目前可用的方案相比,拟议的自动驾驶汽车逻辑在停止/通行模拟结果方面产生了最准确的结果。关于自动化对停/走结果的影响,模拟实验表明,为了道路安全,自动驾驶汽车倾向于采取更保守的行为,黄灯期间越过停止线的车辆数量显著减少(≈15%),闯红灯的情况为零。不过,与默认停车/通行参数值所代表的保守驾驶员相比,自动驾驶汽车倾向于采取更理性的行为,在不影响道路安全的前提下提高交叉路口的通行能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
13.60
自引率
6.30%
发文量
402
审稿时长
15 weeks
期刊介绍: The Journal of Traffic and Transportation Engineering (English Edition) serves as a renowned academic platform facilitating the exchange and exploration of innovative ideas in the realm of transportation. Our journal aims to foster theoretical and experimental research in transportation and welcomes the submission of exceptional peer-reviewed papers on engineering, planning, management, and information technology. We are dedicated to expediting the peer review process and ensuring timely publication of top-notch research in this field.
文献相关原料
公司名称 产品信息 采购帮参考价格
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信