Autonomous Vehicle Work Ticket Triage Using Dynamic Priority
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Solution Overview
Problem
The challenge in maintaining autonomous vehicle fleets is the inefficient categorization and prioritization of failure events, which hinders strategic and expeditious repairs.
Innovation Solution
A dynamic priority number (DPN) calculation system that assesses severity, occurrence likelihood, and confidence scores to prioritize work orders based on quantified metrics, enabling automated triage and scheduling of repairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual categorization and prioritization of failure events is used, then human judgment and flexibility are maintained, but efficiency and speed of repair scheduling deteriorate
Solution Approach 1:
The system enables automated self-service triage where the work order management system automatically categorizes and prioritizes failure events using machine-reported telemetry data, eliminating the need for manual human intervention in the initial assessment phase while maintaining systematic decision-making capabilities
Solution Approach 2:
The patent replaces manual mechanical processes of human categorization and prioritization with an automated computational system that uses machine-reported telemetry data, algorithms, and predefined criteria to systematically assess and rank work orders, thereby increasing efficiency and reducing time loss
2Productivity
If automated triage systems are implemented, then repair scheduling efficiency is improved, but system complexity increases
Solution Approach 1:
The system integrates multiple functions into a single automated triage platform that can process various types of machine-reported telemetry data, handle different failure event categories, and generate prioritized work order schedules, thereby managing complexity through functional consolidation rather than proliferation
Solution Approach 2:
The patent manages system complexity by dynamically adjusting prioritization parameters and thresholds based on machine-reported data characteristics, allowing the system to adapt to different failure scenarios and data types without requiring fundamentally different processing architectures
3Measurement precision
If comprehensive machine-reported telemetry data is analyzed, then accuracy of failure event prioritization is improved, but data processing requirements and computational load increase
Solution Approach 1:
The system extracts and processes only the most relevant telemetry data parameters from the comprehensive machine-reported data stream, filtering out redundant information and focusing computational resources on critical failure indicators, thereby maintaining prioritization accuracy while reducing overall computational load
Solution Approach 2:
The patent applies partial processing by analyzing only the essential subset of telemetry data needed for effective prioritization decisions, rather than exhaustively processing all available machine-reported data, thus achieving sufficient accuracy with reduced computational energy consumption
Data Source
AI summary
The disclosed technology provides solutions for improving work ticketing triage and in particular, for improving the triage of work orders for detected failure events (or predicted failure events) for a fleet of vehicles, such as a fleet of autonomous vehicles (AVs). In some aspects, a process of the disclosed technology can include steps for receiving field data, processing the field data to identify two or more potential failure events associated with at least one AV from among one or more AVs, and automatically generating a work order for each of the two or more potential failure events. In some aspects, the process can further include steps for calculating a dynamic priority number for each of the two or more potential failure events and sorting the work orders based on the dynamic priority. Systems and machine-readable media are also provided.