DTTCNet: Time-to-Collision Estimation With Autonomous Emergency Braking Using Multi-Scale Transformer Network

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiaoqiang Teng;Shibiao Xu;Deke Guo;Yulan Guo;Weiliang Meng;Xiaopeng Zhang
{"title":"DTTCNet: Time-to-Collision Estimation With Autonomous Emergency Braking Using Multi-Scale Transformer Network","authors":"Xiaoqiang Teng;Shibiao Xu;Deke Guo;Yulan Guo;Weiliang Meng;Xiaopeng Zhang","doi":"10.1109/TMC.2024.3454122","DOIUrl":null,"url":null,"abstract":"The rapid advancement of autonomous driving technologies has brought the significance of Autonomous Emergency Braking (AEB) systems, which are paramount in mitigating collision risk and elevating road safety by preemptively applying brakes when a potential collision is detected. Within the core mechanisms of AEB systems, the Time-to-Collision (TTC) estimation plays a pivotal role, in quantitatively determining the criticality and timing for initiating braking interventions. However, existing TTC estimation approaches exhibit sensitivity to diverse driving scenarios, compromising the performance of AEB systems, especially in instantaneous situations. To address these issues, this paper presents DTTCNet, a novel supervised deep learning model for TTC estimation that leverages multi-scale transformer architectures and multi-task losses, thereby enhancing precision and boosting system performance. The DTTCNet first extracts spatiotemporal features from raw sensor data and utilizes a supervised training strategy. The multi-scale transformer architecture effectively captures variations across different scales, while the multi-task loss function optimizes the network training performance. Our experimental results on a challenging dataset demonstrate that DTTCNet achieves approximately 20% performance improvements over existing methods in terms of accuracy. This signifies a promising approach to augmenting the safety of autonomous driving systems with the integration of aftermarket mobile devices (e.g., Mobileye and Bosch products).","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14903-14917"},"PeriodicalIF":7.7000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10663858/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

The rapid advancement of autonomous driving technologies has brought the significance of Autonomous Emergency Braking (AEB) systems, which are paramount in mitigating collision risk and elevating road safety by preemptively applying brakes when a potential collision is detected. Within the core mechanisms of AEB systems, the Time-to-Collision (TTC) estimation plays a pivotal role, in quantitatively determining the criticality and timing for initiating braking interventions. However, existing TTC estimation approaches exhibit sensitivity to diverse driving scenarios, compromising the performance of AEB systems, especially in instantaneous situations. To address these issues, this paper presents DTTCNet, a novel supervised deep learning model for TTC estimation that leverages multi-scale transformer architectures and multi-task losses, thereby enhancing precision and boosting system performance. The DTTCNet first extracts spatiotemporal features from raw sensor data and utilizes a supervised training strategy. The multi-scale transformer architecture effectively captures variations across different scales, while the multi-task loss function optimizes the network training performance. Our experimental results on a challenging dataset demonstrate that DTTCNet achieves approximately 20% performance improvements over existing methods in terms of accuracy. This signifies a promising approach to augmenting the safety of autonomous driving systems with the integration of aftermarket mobile devices (e.g., Mobileye and Bosch products).
DTTCNet:利用多尺度变压器网络估算自主紧急制动的碰撞时间
自动驾驶技术的飞速发展带来了自主紧急制动(AEB)系统的重要性,该系统在检测到潜在碰撞时,会预先采取制动措施,从而降低碰撞风险,提高道路安全性。在自动紧急制动(AEB)系统的核心机制中,碰撞时间(TTC)估算在定量确定启动制动干预的临界点和时机方面发挥着关键作用。然而,现有的 TTC 估算方法对不同的驾驶场景非常敏感,影响了 AEB 系统的性能,尤其是在瞬时情况下。为了解决这些问题,本文提出了一种用于 TTC 估计的新型监督深度学习模型 DTTCNet,该模型利用多尺度变压器架构和多任务损失,从而提高了精度并提升了系统性能。DTTCNet 首先从原始传感器数据中提取时空特征,并采用监督训练策略。多尺度变换器架构能有效捕捉不同尺度的变化,而多任务损失函数则能优化网络训练性能。我们在一个具有挑战性的数据集上的实验结果表明,DTTCNet 在准确性方面比现有方法提高了约 20% 的性能。这标志着通过集成售后市场移动设备(如 Mobileye 和博世产品)来增强自动驾驶系统安全性的方法大有可为。
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
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
×
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