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Title: PASS: A System-Driven Evaluation Platform for Autonomous Driving Safety and Security
Safety and security play critical roles for the success of Autonomous Driving (AD) systems. Since AD systems heavily rely on AI components, the safety and security research of such components has also received great attention in recent years. While it is widely recognized that AI component-level (mis)behavior does not necessarily lead to AD system-level impacts, most of existing work still only adopts component-level evaluation. To fill such critical scientific methodology-level gap from component-level to real system-level impact, a system-driven evaluation platform jointly constructed by the community could be the solution. In this paper, we present PASS (Platform for Auto-driving Safety and Security), a system-driven evaluation prototype based on simulation. By sharing our platform building concept and preliminary efforts, we hope to call on the community to build a uniform and extensible platform to make AI safety and security work sufficiently meaningful at the system level.
Authors:
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Award ID(s):
1929771 1932464 2145493
Publication Date:
NSF-PAR ID:
10359464
Journal Name:
NDSS Workshop on Automotive and Autonomous Vehicle Security (AutoSec)
Sponsoring Org:
National Science Foundation
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