Vehicle Control Unit Validation for Driving Condition Anomalies
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Solution Overview
Problem
Autonomous and semi-autonomous vehicles are vulnerable to slight perturbations in their information inputs, which can cause undesired vehicle behavior due to neural network vulnerabilities, and existing systems lack robust defenses against adversarial attacks and inaccuracies.
Innovation Solution
Implementing methods and computing devices that utilize supplemental information from various data sources, including high-definition maps and second vehicle sensors, to detect and manage driving condition anomalies by comparing and validating sensor data through a driving condition database, leveraging Edge computing for rapid responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If neural networks are used for rapid environmental determination, then decision-making speed is improved, but vulnerability to input perturbations increases
Solution Approach 1:
A validation system acts as an intermediary between the neural network and vehicle control systems. This validation system receives neural network outputs, checks them against expected patterns and multiple sensor inputs, and only passes validated information to control systems. This mediator layer prevents adversarial inputs from directly affecting vehicle behavior while preserving the speed benefits of neural network processing.
Solution Approach 2:
The system implements feedback loops where vehicle sensor data continuously validates and corrects neural network determinations. When real-world sensor inputs contradict neural network outputs, the system adjusts or rejects the neural network determination. This feedback mechanism improves reliability by ensuring neural network outputs align with actual environmental conditions while maintaining rapid response times through efficient validation algorithms.
2Measurement precision
If multiple data sources are integrated for validation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The validation system is segmented into modular components: data collection modules from various sensors, validation rule engines, conflict resolution modules, and output generation modules. Each module handles specific validation tasks independently, making the complex system manageable and maintainable. This segmentation allows precise validation through multiple data sources while organizing complexity into discrete, testable units.
Solution Approach 2:
The validation system uses universal data structures and communication protocols that work across different sensor types and vehicle platforms. A single validation framework handles multiple data sources (cameras, LIDAR, radar, GPS) using common processing logic, reducing overall system complexity despite the diversity of inputs. This multi-functional approach enables precise multi-source validation without proportionally increasing complexity.
Data Source
Figure 1A
Figure 1B~1C
Figure 1D
AI summary
The invention relates to methods performed by a processor of a vehicle control unit for managing a driving condition anomaly. The vehicle may receive a first driving condition based on data from a first vehicle sensor, receive a second driving condition based on data from another data source, determine a driving condition anomaly based on the first driving condition and the second driving condition, send a request for information to a driving condition database remote from the vehicle, receive the requested information from the driving condition database, and resolve the driving condition anomaly based on the requested information from the driving condition database.