基于矩阵补全的无人车感知系统的攻击防御技术
基于矩阵补全的无人车感知系统的攻击防御技术
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深圳市无人驾驶感知决策与执行技术工程实验室项目(Y7D004);深圳电动汽车动力平台与安全技术重点实验室项目

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Attack Defense Technology of Unmanned Vehicle Perception System Based on Matrix Completion
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Shenzhen Engineering Laboratory for Autonomous Driving Technology(Y7D004);Shenzhen Key Laboratory of Electric Vehicle Powertrain Platform and Safety Technology

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    摘要:

    环境感知系统是无人驾驶技术中至关重要的一环,是整个无人车安全和稳定的前提。目前无 人驾驶领域内对于环境感知技术的研究主要集中在理想环境下的环境信息获取、语义信息高精度识别 以及多传感器的信息融合等,而未形成系统全面的攻击检测和防御体系。该研究利用感知系统中多传 感器感知信号在时域和空间域上的相关性,建立了多传感器之间的信息交叉数学模型,可有效检测到 被攻击的传感器,并基于矩阵补全方法对失真数据进行恢复。实验结果显示,该方法能够较好地检测 被攻击传感器,并恢复因攻击而缺失的目标信息。

    Abstract:

    Environmental perception system is an important part of unmanned driving technology, which is the premise of the safety and stability of the unmanned vehicle system. At present, the environmental perception technology is mainly focused on the environmental information acquisition under ideal environment, the high-precision recognition of semantic information, and the multi-sensor fusion etc. A comprehensive attack detection and defense system for the unmanned driving systems is still not available. In this paper, the correlation of multi-sensor perception signals in the perception system in both temporal and spatial domains are considered to establish a mathematical model, which is used to connect the information among different sensors, and to detect the attacked sensors. More important, it can be also used to recover the distorted data based on the matrix completion method. The experimental results showed that, the proposed method can detect the attacked sensor effectively and recover the missed information in the attached perception systems.

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引文格式
李慧云,邵翠萍,陈贝章,胡延步,杨赵南.基于矩阵补全的无人车感知系统的攻击防御技术 [J].集成技术,2020,9(5):3-14

Citing format
LI Huiyun, SHAO Cuiping, CHEN Beizhang, HU Yanbu, YANG Zhaonan. Attack Defense Technology of Unmanned Vehicle Perception System Based on Matrix Completion[J]. Journal of Integration Technology,2020,9(5):3-14

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  • 收稿日期:2020-05-09
  • 最后修改日期:2020-07-16
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  • 在线发布日期: 2020-09-23
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