Using 3D Shadows to Detect Object Hiding Attacks on Autonomous Vehicle Perception

TL;DR

This paper introduces a novel method leveraging 3D shadows in LiDAR point clouds to detect objects hidden from autonomous vehicle perception systems, enhancing security against spoofing attacks.

Contribution

The work presents a new approach using 3D shadow analysis to locate hidden obstacles, improving detection accuracy and obstacle distance prediction in autonomous vehicle perception.

Findings

High accuracy in matching shadows to hidden objects

Effective detection of occluded obstacles

Precise estimation of obstacle distance from ego-vehicle

Abstract

Autonomous Vehicles (AVs) are mostly reliant on LiDAR sensors which enable spatial perception of their surroundings and help make driving decisions. Recent works demonstrated attacks that aim to hide objects from AV perception, which can result in severe consequences. 3D shadows, are regions void of measurements in 3D point clouds which arise from occlusions of objects in a scene. 3D shadows were proposed as a physical invariant valuable for detecting spoofed or fake objects. In this work, we leverage 3D shadows to locate obstacles that are hidden from object detectors. We achieve this by searching for void regions and locating the obstacles that cause these shadows. Our proposed methodology can be used to detect an object that has been hidden by an adversary as these objects, while hidden from 3D object detectors, still induce shadow artifacts in 3D point clouds, which we use for obstacle detection. We show that using 3D shadows for obstacle detection can achieve high accuracy in matching shadows to their object and provide precise prediction of an obstacle's distance from the ego-vehicle.