Experimental Evaluation of Security Attacks on Self-Driving Car Platforms

Deep learning-based perception pipelines in autonomous ground vehicles are vulnerable to both adversarial manipulation and network-layer disruption. We present a systematic, on-hardware experimental evaluation of five attack classes: FGSM, PGD, man-in-the-middle MitM, denial-of-service DoS, and...

Attacking Driverless Cars with Projected Images

Interesting research -- "Phantom Attacks Against Advanced Driving Assistance Systems": Abstract: The absence of deployed vehicular communication systems, which prevents the advanced driving assistance systems ADASs and autopilots of semi/fully autonomous cars to validate their virtual perception...