Autonomous vehicles (AVs) rely on accurate and robust sensor observations for safety-critical decision making in a variety of conditions. The fundamental building blocks of such systems are sensors and classifiers that process ultrasound, radar, GPS, lidar, and camera signals [1]. It is of primary importance that the resulting decisions are robust to perturbations, which can take the form of different types of nuisances and data transformations and can even be adversarial perturbations (APs).

中文翻译：

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实现自动驾驶汽车的鲁棒感测：对抗性视角

自动驾驶汽车 (AV) 依靠准确而强大的传感器观察来在各种条件下做出安全关键的决策。此类系统的基本构建块是处理超声波、雷达、GPS、激光雷达和摄像头信号的传感器和分类器 [1]。最重要的是，由此产生的决策对扰动具有鲁棒性，扰动可以采取不同类型的干扰和数据转换的形式，甚至可以是对抗性扰动 (AP)。