Abstract

Monocular Depth Estimation (MDE) plays a crucial role in vision-based Autonomous Driving (AD) systems. It utilizes a singlecamera image to determine the depth of objects, facilitating driving decisions such as braking a few meters in front of a detected obstacle or changing lanes to avoid collision. In this paper, we investigate the security risks associated with monocular vision-based depth estimation algorithms utilized by AD systems. By exploiting the vulnerabilities of MDE and the principles of optical lenses, we introduce πΏπ‘’π‘›π‘ π΄π‘‘π‘‘π‘Žπ‘π‘˜, a physical attack that involves strategically placing optical lenses on the camera of an autonomous vehicle to manipulate the perceived object depths. πΏπ‘’π‘›π‘ π΄π‘‘π‘‘π‘Žπ‘π‘˜ encompasses two attack formats: concave lens attack and convex lens attack, each utilizing different optical lenses to induce false depth perception. We begin by constructing a mathematical model of our attack, incorporating various attack parameters. Subsequently, we simulate the attack and evaluate its real-world performance in driving scenarios to demonstrate its effect on state-of-the-art MDE models. The results highlight the significant impact of πΏπ‘’π‘›π‘ π΄π‘‘π‘‘π‘Žπ‘π‘˜ on the accuracy of depth estimation in AD systems.

Department(s)

Computer Science

Keywords and Phrases

Autonomous Driving Β· Camera Β· Monocular Depth Estimation Β· Autonomous Vehicle Β· Optical Lens.

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Publication Date

25 September, 2024

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