Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack

Understand

Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical.

• In this work, we are the first to systematically study the security of state-of-the-art deep learning based ALC systems in their designed operational domains under physical-world adversarial attacks.
• We formulate the problem with a safety-critical attack goal, and a novel and domain-specific attack vector: dirty road patches.
• To systematically generate the attack, we adopt an optimization-based approach and overcome domain-specific design challenges such as camera frame inter-dependencies due to attack-influenced vehicle control, and the lack of objective function design for lane detection models.