Impact Analysis of Inference Time Attack of Perception Sensors on Autonomous Vehicles
This addresses cybersecurity and safety issues for autonomous vehicles, focusing on a less-explored attack surface, though it appears incremental as it builds on existing work on perception security.
The paper tackles the problem of inference time attacks on perception sensors in autonomous vehicles, demonstrating through simulation that these attacks can threaten the safety of the ego vehicle and other traffic participants.
As a safety-critical cyber-physical system, cybersecurity and related safety issues for Autonomous Vehicles (AVs) have been important research topics for a while. Among all the modules on AVs, perception is one of the most accessible attack surfaces, as drivers and AVs have no control over the outside environment. Most current work targeting perception security for AVs focuses on perception correctness. In this work, we propose an impact analysis based on inference time attacks for autonomous vehicles. We demonstrate in a simulation system that such inference time attacks can also threaten the safety of both the ego vehicle and other traffic participants.