{"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).
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.