Probabilistic model for high-level intention estimation and trajectory prediction in urban environments

IF 0.8 Q4 ROBOTICS
Yunsoo Bok, Naoki Suganuma, Keisuke Yoneda
{"title":"Probabilistic model for high-level intention estimation and trajectory prediction in urban environments","authors":"Yunsoo Bok,&nbsp;Naoki Suganuma,&nbsp;Keisuke Yoneda","doi":"10.1007/s10015-024-00973-4","DOIUrl":null,"url":null,"abstract":"<div><p>To enable successful automated driving, precise behavior prediction of surrounding vehicles is indispensable in urban traffic scenarios. Furthermore, given that a vehicle’s behavior is influenced by the movements of other road users, it becomes crucial to estimate their intentions to anticipate precise future motion. However, the elevated complexity resulting from interdependencies among traffic participants and the uncertainty arising from the object recognition errors present additional challenges. Despite extensive research on inferring intentions, many studies have concentrated on estimating intentions from interactions, resulting in a lack of practicality in urban traffic environments due to low computational efficiency and low robustness against recognition failure of strongly interacting road users. In this paper, we introduce a practical stochastic model for intention estimation and trajectory prediction of surrounding vehicles in automated driving under urban traffic environments. The trajectory is forecasted based on hierarchically computed and probabilistically estimated intentions, which represent an interpretation of vehicle behavior, utilizing only the kinematic state of the focal vehicle and HD maps to ensure real-time performance and enhance robustness. The evaluated results demonstrate that the proposed model surpasses straightforward methods in terms of accuracy while maintaining computational efficiency and exhibits robustness against the recognition failure of traffic participants which strongly influence the focal vehicle.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"29 4","pages":"557 - 566"},"PeriodicalIF":0.8000,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-024-00973-4.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Life and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s10015-024-00973-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0

Abstract

To enable successful automated driving, precise behavior prediction of surrounding vehicles is indispensable in urban traffic scenarios. Furthermore, given that a vehicle’s behavior is influenced by the movements of other road users, it becomes crucial to estimate their intentions to anticipate precise future motion. However, the elevated complexity resulting from interdependencies among traffic participants and the uncertainty arising from the object recognition errors present additional challenges. Despite extensive research on inferring intentions, many studies have concentrated on estimating intentions from interactions, resulting in a lack of practicality in urban traffic environments due to low computational efficiency and low robustness against recognition failure of strongly interacting road users. In this paper, we introduce a practical stochastic model for intention estimation and trajectory prediction of surrounding vehicles in automated driving under urban traffic environments. The trajectory is forecasted based on hierarchically computed and probabilistically estimated intentions, which represent an interpretation of vehicle behavior, utilizing only the kinematic state of the focal vehicle and HD maps to ensure real-time performance and enhance robustness. The evaluated results demonstrate that the proposed model surpasses straightforward methods in terms of accuracy while maintaining computational efficiency and exhibits robustness against the recognition failure of traffic participants which strongly influence the focal vehicle.

城市环境中高层次意图估计和轨迹预测的概率模型
要实现成功的自动驾驶,在城市交通场景中对周围车辆的精确行为预测是必不可少的。此外,由于车辆的行为会受到其他道路使用者动作的影响,因此估算他们的意图以预测未来的精确动作变得至关重要。然而,交通参与者之间的相互依存关系导致的复杂性增加,以及物体识别误差带来的不确定性带来了额外的挑战。尽管对推断意图进行了广泛的研究,但许多研究都集中在从交互中估计意图上,结果由于计算效率低和对强烈交互的道路使用者识别失败的鲁棒性低,在城市交通环境中缺乏实用性。在本文中,我们介绍了一种实用的随机模型,用于城市交通环境下自动驾驶的意图估计和周围车辆的轨迹预测。轨迹预测基于分层计算和概率估计的意图,它代表了对车辆行为的解释,仅利用焦点车辆的运动状态和高清地图来确保实时性并增强鲁棒性。评估结果表明,所提出的模型在保持计算效率的同时,在准确性方面超越了直接方法,并在交通参与者识别失败时表现出鲁棒性,因为交通参与者对焦点车辆有很大影响。
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
2.00
自引率
22.20%
发文量
101
期刊介绍: Artificial Life and Robotics is an international journal publishing original technical papers and authoritative state-of-the-art reviews on the development of new technologies concerning artificial life and robotics, especially computer-based simulation and hardware for the twenty-first century. This journal covers a broad multidisciplinary field, including areas such as artificial brain research, artificial intelligence, artificial life, artificial living, artificial mind research, brain science, chaos, cognitive science, complexity, computer graphics, evolutionary computations, fuzzy control, genetic algorithms, innovative computations, intelligent control and modelling, micromachines, micro-robot world cup soccer tournament, mobile vehicles, neural networks, neurocomputers, neurocomputing technologies and applications, robotics, robus virtual engineering, and virtual reality. Hardware-oriented submissions are particularly welcome. Publishing body: International Symposium on Artificial Life and RoboticsEditor-in-Chiei: Hiroshi Tanaka Hatanaka R Apartment 101, Hatanaka 8-7A, Ooaza-Hatanaka, Oita city, Oita, Japan 870-0856 ©International Symposium on Artificial Life and Robotics
×
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