Autonomous Vehicle Fleet Diagnostics for Automated Maintenance Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles require efficient and cost-effective methods to diagnose maintenance needs without manual inspections, which are costly and time-consuming, and to normalize sensor measurements across varying road conditions and vehicle ages.
Innovation Solution
An autonomous vehicle is instructed to drive a specific course where it collects diagnostic data, which is analyzed by a remote computing system using machine learning algorithms to identify maintenance needs based on data from a fleet of similar vehicles, allowing for precise and timely repair scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspections are used to diagnose maintenance needs, then skilled human drivers can identify damage and maintenance requirements, but the process becomes costly and time-consuming
Solution Approach 1:
The patent replaces manual mechanical inspection with automated sensor systems and machine learning algorithms. Sensors mounted on the autonomous vehicle collect diagnostic data during normal operation, and machine learning models analyze this data to identify maintenance needs, eliminating the need for human inspectors and significantly reducing inspection time while maintaining or improving diagnostic accuracy
Solution Approach 2:
The autonomous vehicle performs self-diagnosis by collecting and analyzing its own operational data through onboard sensors and processing systems. The vehicle can autonomously identify maintenance requirements without external inspection, enabling continuous monitoring and immediate detection of issues during normal operation
2Reliability
If manual inspections are conducted to determine maintenance needs, then damage and wear can be identified, but the inspections are costly
Solution Approach 1:
The patent replaces expensive manual inspection processes with automated sensor systems and machine learning analysis. The sensor suite and processing algorithms provide continuous monitoring of vehicle condition at a fraction of the cost of human inspections, while improving detection capability through multi-sensor data fusion
Solution Approach 2:
The system enables continuous monitoring of vehicle condition during normal operation rather than periodic manual inspections. Sensors continuously collect data on vehicle parameters, allowing real-time detection of wear and damage trends, which prevents costly reactive repairs by enabling proactive maintenance scheduling
3Quantity of substance
If sensor measurements are collected under varying road conditions and vehicle ages, then comprehensive diagnostic data is obtained, but the data becomes difficult to normalize and analyze
Solution Approach 1:
The patent transforms raw sensor measurements into normalized diagnostic parameters by applying machine learning models that account for varying road conditions and vehicle ages. The system learns to adjust for environmental factors and vehicle degradation patterns, converting diverse sensor inputs into standardized maintenance indicators that are comparable across different operating conditions
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary layer between raw sensor data and maintenance decisions. These algorithms process and normalize the complex multi-sensor data, filtering out noise from varying conditions and extracting meaningful diagnostic information that directly indicates maintenance needs
Data Source
AI summary
The present technology is effective to cause at least one processor to instruct an autonomous vehicle to navigate a specific course and to record diagnostic measurements while navigating the specific course, receive the diagnostic measurements from the autonomous vehicle, and analyze the diagnostic measurements from the autonomous vehicle in a context provided by a collection of diagnostic measurement data collected from a fleet of similar autonomous vehicles navigating the specific course.