CPU Usage Monitoring for Safe Autonomous System Operation
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Solution Overview
Problem
In complex systems like autonomous vehicles, identifying and troubleshooting sources of increased latency and CPU usage is challenging due to interconnectedness, which can lead to unsafe operations.
Innovation Solution
The implementation of techniques that tag data with unique identifiers and timestamps to determine system latency and CPU usage, allowing for comparison to operational ranges and triggering safe state actions when anomalies are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If systems are updated and altered to improve functionality, then system capabilities are enhanced, but identifying and troubleshooting sources of increased latency and CPU usage becomes more difficult
Solution Approach 1:
The system establishes baseline performance metrics (CPU usage, latency) before system updates or alterations occur. By pre-defining what normal operation looks like across interconnected systems, the patent enables easier detection of anomalies after updates, reversing the typical troubleshooting difficulty caused by system complexity and interconnectivity.
2Reliability
If monitoring and troubleshooting capabilities are enhanced to identify latency and CPU usage sources, then system safety is improved, but system complexity increases
Solution Approach 1:
The patent implements a unified monitoring framework that simultaneously tracks multiple metrics (CPU usage, latency, error rates) across diverse interconnected systems using a single standardized approach. This universal monitoring system improves reliability by comprehensively observing system health while avoiding the complexity of implementing separate specialized monitoring tools for each metric or system type.
3Reliability
If detailed tracking of latency and CPU usage is implemented to ensure safe operation, then operational safety is improved, but computational overhead increases
Solution Approach 1:
The monitoring system applies different levels of observation detail to different systems and metrics based on their criticality and resource consumption patterns. High-criticality systems receive more detailed monitoring while less critical systems use lighter-weight tracking, ensuring operational safety for essential functions while minimizing overall computational overhead and energy consumption across the entire interconnected system.
Data Source
AI summary
Performance anomalies in complex systems can be difficult to identify and diagnose. In an example, CPU-usage associated with one or more of the systems can be determined. An anomalous event can be determined based on the determined CPU-usage. In some examples, based at least in part on determining the event, the system may be controlled in a safe state and/or reconfigured to obviate the anomalous event.