Autonomous Vehicle Diagnostic Testing for Fleet-Based Maintenance Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in efficiently identifying maintenance and repair needs due to the absence of human assessment, with regular inspections being costly and time-consuming, and sensor measurements being difficult to normalize for road conditions and vehicle age.
Innovation Solution
An autonomous vehicle is instructed to drive a specific course to collect diagnostic measurements, which are analyzed against a dataset from similar vehicles to determine if repairs are needed, using a combination of sensor data and machine learning algorithms to classify maintenance requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspections are conducted to identify damage and maintenance needs, then detection accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated system using sensors (cameras, lidars, radars) and machine learning algorithms to detect damage and maintenance needs. The autonomous vehicle captures images and sensor data, which are then analyzed by trained machine learning models to identify issues without human intervention, thereby reducing time consumption while maintaining detection accuracy.
Solution Approach 2:
The autonomous vehicle performs self-diagnosis by collecting and analyzing its own sensor data to identify maintenance needs and damage. The vehicle autonomously captures images of its components, processes them through machine learning algorithms, and determines its own maintenance requirements without external inspection, enabling continuous operation and reducing downtime.
2Reliability
If regular manual inspections are performed, then maintenance needs are identified, but cost and time consumption increase
Solution Approach 1:
The patent replaces costly manual inspection systems with an automated sensor-based detection system. Multiple sensors (cameras, lidars, radars) continuously monitor vehicle components, and machine learning algorithms analyze the data to identify maintenance needs, eliminating the need for scheduled manual inspections and reducing vehicle downtime.
Solution Approach 2:
The autonomous inspection system operates continuously as the vehicle moves, capturing sensor data at all times rather than requiring periodic stops for manual inspection. This continuous monitoring enables real-time detection of maintenance issues, improving vehicle availability while maintaining reliable maintenance identification.
3Measurement precision
If sensor measurements are collected for diagnostic purposes, then maintenance accuracy is improved, but data normalization difficulty increases
Solution Approach 1:
The patent introduces machine learning algorithms as intermediaries between raw sensor measurements and maintenance decisions. These algorithms automatically process, normalize, and interpret multi-source sensor data (images, lidar points, radar signals), handling the complexity of data integration and normalization while providing accurate maintenance predictions.
Solution Approach 2:
The system transforms raw sensor measurements into standardized features and parameters through machine learning processing. The algorithms convert diverse sensor data into normalized representations that can be directly compared and analyzed, automatically adjusting for variations in sensor conditions, vehicle age, and environmental factors.
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.