End-to-End Target Liveness Detection via mmWave Radar and Vision Fusion for Autonomous Vehicles

IF 3.9 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Sensor Networks Pub Date : 2023-10-18 DOI:10.1145/3628453
Shuai Wang, Luoyu Mei, Zhimeng Yin, Hao Li, Ruofeng Liu, Wenchao Jiang, Chris Xiaoxuan Lu
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Abstract

The successful operation of autonomous vehicles hinges on their ability to accurately identify objects in their vicinity, particularly living targets such as bikers and pedestrians. However, visual interference inherent in real-world environments, such as omnipresent billboards, poses substantial challenges to extant vision-based detection technologies. These visual interference exhibit similar visual attributes to living targets, leading to erroneous identification. We address this problem by harnessing the capabilities of mmWave radar, a vital sensor in autonomous vehicles, in combination with vision technology, thereby contributing a unique solution for liveness target detection. We propose a methodology that extracts features from the mmWave radar signal to achieve end-to-end liveness target detection by integrating the mmWave radar and vision technology. This proposed methodology is implemented and evaluated on the commodity mmWave radar IWR6843ISK-ODS and vision sensor Logitech camera. Our extensive evaluation reveals that the proposed method accomplishes liveness target detection with a mean average precision (mAP) of 98.1%, surpassing the performance of existing studies.
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基于毫米波雷达和视觉融合的自动驾驶车辆端到端目标活动检测
自动驾驶汽车能否成功运行,取决于它们能否准确识别附近的物体,尤其是骑自行车的人和行人等活生生的目标。然而,现实环境中固有的视觉干扰,如无所不在的广告牌,对现有的基于视觉的检测技术提出了重大挑战。这些视觉干扰表现出与活体目标相似的视觉属性,导致错误识别。我们通过利用毫米波雷达(自动驾驶汽车中的重要传感器)与视觉技术相结合的能力来解决这一问题,从而为活体目标检测提供了独特的解决方案。我们提出了一种从毫米波雷达信号中提取特征的方法,通过集成毫米波雷达和视觉技术来实现端到端的活体目标检测。该方法在商用毫米波雷达IWR6843ISK-ODS和罗技视觉传感器相机上实现和评估。我们的广泛评估表明,所提出的方法以98.1%的平均精度(mAP)完成了活体目标检测,超过了现有研究的性能。
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来源期刊
ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks 工程技术-电信学
CiteScore
5.90
自引率
7.30%
发文量
131
审稿时长
6 months
期刊介绍: ACM Transactions on Sensor Networks (TOSN) is a central publication by the ACM in the interdisciplinary area of sensor networks spanning a broad discipline from signal processing, networking and protocols, embedded systems, information management, to distributed algorithms. It covers research contributions that introduce new concepts, techniques, analyses, or architectures, as well as applied contributions that report on development of new tools and systems or experiences and experiments with high-impact, innovative applications. The Transactions places special attention on contributions to systemic approaches to sensor networks as well as fundamental contributions.
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