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Related Experiment Video

Updated: Jul 1, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving.

K T Yasas Mahima¹, Asanka Perera², Sreenatha Anavatti¹

¹School of Engineering and Technology, University of New South Wales, Canberra, ACT 2612, Australia.

Sensors (Basel, Switzerland)

|December 9, 2023

Summary

This summary is machine-generated.

This study explores adversarial attacks on 3D LiDAR semantic segmentation for autonomous vehicles. Findings show ground-level points are vulnerable, impacting perception system robustness.

Keywords:

LiDAR adversarial attacks autonomous vehicles semantic segmentation

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Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Deep learning excels in 2D/3D vision but is vulnerable to adversarial attacks.
Adversarial attacks on autonomous vehicle perception are increasingly studied, yet 3D LiDAR semantic segmentation remains underexplored.

Purpose of the Study:

To investigate the adversarial robustness of 3D LiDAR semantic segmentation in autonomous vehicles.
To develop and analyze LiDAR point-based adversarial attack methods.

Main Methods:

Developed and analyzed three LiDAR point-based adversarial attack methods.
Evaluated attacks on various networks using the SemanticKITTI dataset.
Investigated class-wise point distribution's influence on adversarial robustness.

Main Results:

The Cylinder3D network exhibited the highest susceptibility to the analyzed adversarial attacks.
Ground-level points were found to be particularly vulnerable to point perturbation attacks.
Networks utilizing point data representations showed notable resistance to attack transferability.

Conclusions:

Adversarial attacks pose a significant threat to 3D LiDAR semantic segmentation in autonomous vehicles.
Understanding class-wise vulnerability, especially for ground-level points, is crucial for developing robust systems.
Findings provide a foundation for creating advanced adversarial attacks and effective countermeasures for LiDAR-based perception.