Lune

USENIX Security2022Top-tier venue

Rolling Colors: Adversarial Laser Exploits against Traffic Light Recognition

Chen Yan, Zhijian Xu, Zhanyuan Yin, Xiaoyu Ji, Wenyuan Xu

2022Year
21Top-tier citations

Abstract

Traffic light recognition is essential for fully autonomous driving in urban areas. In this paper, we investigate the feasibility of fooling traffic light recognition mechanisms by shedding laser interference on the camera. By exploiting the rolling shutter of CMOS sensors, we manage to inject a color stripe overlapped on the traffic light in the image, which can cause a red light to be recognized as a green light or vice versa. To increase the success rate, we design an optimization method to search for effective laser parameters based on empirical models of laser interference. Our evaluation in emulated and real-world setups on 2 state-of-the-art recognition systems and 5 cameras reports a maximum success rate of 30% and 86.25% for Red-to-Green and Green-to-Red attacks. We observe that the attack is effective in continuous frames from more than 40 meters away against a moving vehicle, which may cause end-to-end impacts on self-driving such as running a red light or emergency stop. To mitigate the threat, we propose redesigning the rolling shutter mechanism.

Ask about this paper

Your agent reads all of it.

Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.

Questions to start from

Your agent calls

Luneget_paper_fulltext

Ask in Lune

Free to start. No credit card required.

lune papers fulltext ce8aa658-583f-49e6-b285-23c4d8f5ebff

Cited by top-tier papers21

Ask how each one uses it

Builds on9

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines