Autonomous Driving (AD) systems rely on AI components to make safety and
correct driving decisions. Unfortunately, today's AI algorithms are known to be
generally vulnerable to adversarial attacks. However, for such AI
component-level vulnerabilities to be semantically impactful at the system
level, it needs to address non-trivial semantic gaps both (1) from the
system-level attack input spaces to those at AI component level, and (2) from
AI component-level attack impacts to those at the system level. In this paper,
we define such research space as semantic AI security as opposed to generic AI
security. Over the past 5 years, increasingly more research works are performed
to tackle such semantic AI security challenges in AD context, which has started
to show an exponential growth trend.
In this paper, we perform the first systematization of knowledge of such
growing semantic AD AI security research space. In total, we collect and
analyze 53 such papers, and systematically taxonomize them based on research
aspects critical for the security field. We summarize 6 most substantial
scientific gaps observed based on quantitative comparisons both vertically
among existing AD AI security works and horizontally with security works from
closely-related domains. With these, we are able to provide insights and
potential future directions not only at the design level, but also at the
research goal, methodology, and community levels. To address the most critical
scientific methodology-level gap, we take the initiative to develop an
open-source, uniform, and extensible system-driven evaluation platform, named
PASS, for the semantic AD AI security research community. We also use our
implemented platform prototype to showcase the capabilities and benefits of
such a platform using representative semantic AD AI attacks.

对自动驾驶（AD）系统中语义 AI 安全的研究领域进行了系统化概括，总共收集和分析了 53 篇论文，概括出 6 个最实质性的科学空白。为了解决最关键的科学方法层面的空缺，我们发起了开源、统一、可扩展的系统驱动评估平台 PASS 的开发，并使用我们实现的平台原型展示了这种平台的能力和优势。

自动驾驶中基于语义 AI 的安全性综述

SoK: On the Semantic AI Security in Autonomous Driving

This paper offers a comprehensive survey of recent developments in 3D object
detection covering the full pipeline from input data, over data representation
and feature extraction to the actual detection modules. We include basic
concepts, focus our survey on a broad spectrum of different approaches arising
in the last ten years and propose a systematization which offers a practical
framework to compare those approaches on the methods level.

本文对近十年来 3D 物体检测领域涉及的输入数据、数据表示、特征提取和实际检测模块等全流程进行了全面的调研，系统化分类并给出相应的实用框架。