Multi-Sensor FOV Verification Using Projected Known Data
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Solution Overview
Problem
Sensors in vehicles are prone to errors and malicious attacks due to environmental factors and hacking, leading to inaccurate data and safety hazards in automated driving systems.
Innovation Solution
A verification system projects known data into the overlapping field-of-view of multiple sensors and detects the known data to verify sensor operation, using a projector to emit encoded images like QR codes that can be decoded by trusted sensors, thereby identifying malfunctioning or compromised sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are used to acquire data from the surrounding environment, then the system can perceive obstacles and objects, but the sensors are vulnerable to errors from external factors and malicious attacks
Solution Approach 1:
The system performs preliminary verification by projecting known test patterns (QR codes, encoded images) into the sensors' field of view before actual operation. This advance testing establishes a baseline for sensor accuracy and detects potential vulnerabilities to spoofing attacks or environmental interference, allowing the system to preemptively identify and compensate for sensor errors.
Solution Approach 2:
The system implements a feedback mechanism where sensors capture images of projected test patterns, and the captured data is compared against the original known patterns. This closed-loop verification process provides continuous feedback on sensor performance, enabling real-time detection of drift, calibration errors, or malicious manipulation, and allowing dynamic adjustment of trust levels for each sensor.
2Adaptability or versatility
If multiple sensors operate at different frequencies to sense the same field-of-view, then the system can gather diverse data, but the sensors can be vulnerable to spoofing attacks involving fake scenery
Solution Approach 1:
The system introduces an intermediary verification layer using projected test patterns that serve as a common reference for multiple sensors operating at different frequencies. These patterns act as mediators that translate and align data from disparate sensor types (e.g., visible light cameras, infrared sensors, radar), enabling cross-frequency verification to detect spoofing attacks that would otherwise exploit the heterogeneity of sensor modalities.
Solution Approach 2:
The system employs test patterns with distinct visual characteristics (QR codes, encoded images with specific color schemes and patterns) that can be detected and verified across different sensor frequencies. The use of visually distinguishable patterns with known properties allows the system to verify sensor responses and detect anomalies caused by spoofing, as genuine patterns will match expected characteristics while fake scenery will not.
3Productivity
If the system trusts sensor data for automated driving decisions, then the system can execute control tasks, but false readings from malfunctioning sensors can cause safety hazards
Solution Approach 1:
The system performs preliminary verification by projecting known test patterns (QR codes, encoded images) into the sensors' field of view before actual operation. This advance testing establishes a baseline for sensor accuracy and detects potential vulnerabilities to spoofing attacks or environmental interference, allowing the system to preemptively identify and compensate for sensor errors.
Solution Approach 2:
The system implements a feedback mechanism where sensors capture images of projected test patterns, and the captured data is compared against the original known patterns. This closed-loop verification process provides continuous feedback on sensor performance, enabling real-time detection of drift, calibration errors, or malicious manipulation, and allowing dynamic adjustment of trust levels for each sensor.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to projecting known data into an overlapping field-of-view (FOV) between multiple sensors and detecting the known data for verifying sensor operation and information from the multiple sensors. In one embodiment, a method includes projecting known data using a projector within a FOV of multiple sensors. The method also includes acquiring information within an overlapping FOV that includes the known data. The method also includes indicating a verification for one of the multiple sensors and communicating the information for executing a downstream task upon detecting the known data.