Vehicle Anomaly Detection via Multi-Source Object Data Comparison
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Solution Overview
Problem
Existing anomaly detection techniques, such as those described in PTL 1, are inadequate in detecting cyberattacks that cause false object detection results in vehicles, leading to potential collisions or unsafe travel conditions.
Innovation Solution
The proposed anomaly detection device compares object detection results from two vehicles to determine if either is being attacked, by obtaining and analyzing first and second object detection results from respective object detection devices, and outputting a determination of attack status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object detection results from a single vehicle are used for anomaly detection, then the detection system is simple, but the reliability of detecting cyberattacks is insufficient
Solution Approach 1:
The patent combines object detection results from multiple vehicles (first apparatus and second apparatus) into a unified detection system. By merging detection data from different sources, the system achieves higher reliability in detecting cyberattacks while maintaining manageable complexity through centralized processing.
Solution Approach 2:
The patent introduces an intermediary processing system that receives object detection results from multiple vehicles, compares them against each other and against learned patterns, and determines anomalies. This intermediary layer enables reliable attack detection without requiring direct complex interactions between all vehicles.
2Measurement precision
If object detection results are compared between multiple vehicles to detect attacks, then the detection accuracy improves, but the processing complexity increases
Solution Approach 1:
The patent segments the comparison process into manageable components: obtaining detection results from multiple vehicles, comparing results between vehicles, comparing against learned patterns, and determining anomalies. This segmentation reduces processing complexity while maintaining high accuracy through systematic multi-step analysis.
Data Source
AI summary
An anomaly detection device includes a processor and a non-transitory memory that stores a program. The processor executes the program to operate an anomaly detection device as an obtainer that obtains a first object detection result generated by a first object detection device that is included in a first apparatus which is a vehicle and detects an object in the vicinity of the first apparatus and a second object detection result generated by a second object detection device that is included in a second apparatus in the vicinity of the first apparatus and detects an object in the vicinity of the second apparatus; a determiner that determines whether at least one of the first apparatus or the second apparatus is being attacked, by comparing the first object detection result and the second object detection result; and an outputter that outputs a result of determination by the determiner.