Vehicle Log Event Timelines for Faster Root Cause Triage
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Solution Overview
Problem
Troubleshooting autonomous vehicles is challenging due to their complexity, requiring significant time and resources to trace and resolve issues, as engineers must manually analyze large amounts of log data to identify the root cause of errors.
Innovation Solution
The techniques involve generating an event timeline from log data to identify and triage issues, correlating occurrences of issues across multiple timelines, and determining the cause of problems, thereby allowing for quick and efficient resolution of issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of log data is performed to identify root cause of errors, then measurement precision of issue cause is improved, but loss of time increases significantly
Solution Approach 1:
The patent introduces an intermediary system comprising a database storing historical log data and issue information, and a processing system that automatically correlates current log data with historical data to identify potential causes. This intermediary automated system mediates between the raw log data and the engineer, eliminating the need for manual analysis while maintaining high accuracy in root cause identification.
Solution Approach 2:
The patent creates copies of historical log data and issue information in a structured database format, allowing the system to compare and correlate current issues with historical patterns. By working with copied and structured data rather than raw logs, the system rapidly identifies root causes without manual intervention, resolving the time-precision contradiction.
2Productivity
If complex systems are used in autonomous vehicles to improve performance, then productivity is improved, but device complexity increases making troubleshooting difficult
Solution Approach 1:
The patent segments the complex autonomous vehicle system into multiple operational components (sensors, processors, actuators, software modules) and tracks log data for each segment separately. The database stores segmented log data and issue information, allowing the system to isolate and analyze specific components without being overwhelmed by overall system complexity.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors log data, compares it with historical data, and provides feedback about potential issues and their causes. This automated feedback loop enables the complex system to self-diagnose problems, maintaining high productivity while reducing the effective complexity for troubleshooting purposes.
3Measurement precision
If extensive log data is collected during vehicle operation to improve measurement capability, then measurement precision is improved, but loss of time increases for analyzing the data
Solution Approach 1:
The patent performs preliminary action by pre-processing and structuring log data during vehicle operation, organizing it into categories and storing it in a ready-to-analyze format in the database. Historical log data is pre-correlated with known issues, so when an error occurs, the system only needs to query pre-processed data rather than analyzing raw logs from scratch, maintaining high measurement precision while minimizing analysis time.
Data Source
AI summary
Techniques for identifying and triaging issues associated with a vehicle in order to resolve the issues are described. The techniques may include receiving log data associated with a vehicle and generating an event timeline based at least in part on the log data. The event timeline may comprise a time-ordered record of events associated with operations of the vehicle. The techniques may also include identifying, based at least in part on an event included in the event timeline, a first occurrence of an issue associated with the vehicle. In some instances, a correlation may be determined between the first occurrence of the issue and a second occurrence of the issue, and a cause of the issue may also be determined based at least in part on the correlation. In some examples, the second occurrence of the issue may appear in the event timeline or another event timeline.