LiDAR Intrusion Detection Through Point Cloud Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
LiDAR sensors in autonomous systems are vulnerable to adversarial attacks that cause false object recognition due to manipulated IR pulse signals, leading to inaccurate perception and potential safety hazards.
Innovation Solution
An intrusion detection method that compares original and modified point clouds using the same perception process to identify adversarial attacks by analyzing object regions, involving modifications such as adding or removing points, and classifying objects based on threshold comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LiDAR sensor transmits and receives IR pulse signals to recognize surrounding objects, then object detection capability is achieved, but vulnerability to adversarial attacks increases causing false recognition
Solution Approach 1:
The system performs preliminary modification on the original point cloud to generate a modified point cloud before adversarial attacks occur. This modified point cloud is then used for detection alongside the original, allowing the system to identify inconsistencies that indicate adversarial attacks before they can cause false recognition
Solution Approach 2:
A modified point cloud is introduced as an intermediary between the original point cloud and the detection result. This intermediary serves as a reference to compare against the original detection, helping to identify when adversarial attacks have compromised the original point cloud's integrity
2Difficulty of detecting and measuring
If frame-by-frame comparison is used to detect abnormalities, then intrusion detection capability is provided, but detection accuracy deteriorates due to large variability between consecutive frames
Solution Approach 1:
Instead of comparing consecutive frames which have large variability, the system creates a modified copy of the original point cloud that preserves the same spatial distribution characteristics. This copy is then compared against the original detection results, providing a more reliable baseline for identifying adversarial attacks since the modified and original point clouds share similar statistical properties
3Reliability
If brute-force method is used to compare recognized object shape with all possible ground truth shapes, then comprehensive detection is achieved, but computational efficiency deteriorates
Solution Approach 1:
The system extracts only the essential geometric features from the modified point cloud (such as point cloud density distribution and spatial characteristics) rather than comparing against all possible ground truth shapes. This extraction of key features enables efficient comparison that maintains detection comprehensiveness while dramatically improving computational efficiency
Data Source
AI summary
An intrusion detection method includes obtaining a first point cloud from a light detection and ranging (LiDAR) sensor, obtaining a first object information by applying a first perception process to the first point cloud, modifying the first point cloud to obtain a second point cloud, obtaining a second object information by applying a second perception process to the second point cloud, and providing an intrusion result based on a comparison between the first object information and the second object information.