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序列保存双FoV防御用于自动驾驶汽车中的交通标志和光识别
Abhishek Joshi1, Janhavi Krishna Koda2, Abhishek Phadke3
1Department of Computer Science, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究引入了自动驾驶汽车 (AV) 的新型防御框架,以改善交通信号灯和标志识别. 该系统通过减少真实世界和数字威胁造成的错误分类来提高安全性.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 自动驾驶汽车 (AV) 的安全性取决于对交通信号和标志的准确感知.
- 现实世界条件 (光,雨,泥) 和对抗性攻击会降低感知,导致危险的错误分类.
- 现有的研究缺乏时间连续性,多视场 (FoV) 传感,以及对各种降解类型的综合防御.
研究的目的:
- 为AV感知系统开发和评估一个强大的防御框架.
- 解决时间连续性的局限性,多FoV传感,以及对自然和数字退化的综合防御.
- 引入一个全面的双FoV基准数据集来评估AV感知强度.
主要方法:
- 一个三层防御框架,结合了特征挤压,推断时间温度缩放和基于的异常检测与时间投票.
- 开发一个双FoV基准数据集,包含500个序列和广泛的扰动.
- 在各种操作设计领域和注释类型 (3D和2D) 中评估防御堆的性能.
主要成果:
- 统一的防御堆在一个具有挑战性的测试集中实现了79.8%的mAP.
- 攻击成功率降低了51% (从37.4%降至18.2%),高风险错误分类降低了32%.
- 交叉FoV验证和时间投票改善了在照明变化 (+3.5% mAP) 和遮蔽 (+2.7% mAP) 下的稳定性.
结论:
- 拟议的防御框架显著提高了对各种威胁的AV感知系统的稳定性.
- 双FoV基准和防御堆为推进AV安全研究提供了宝贵的资源.
- 未来的工作需要对合成物理对抗性强度进行更大的验证.
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