ML Vehicle Cycle Tracking in Repair Bays
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Solution Overview
Problem
Challenges in efficiently and reliably tracking the time vehicles spend in repair bays due to limited hardware infrastructure and obstacles blocking camera views in manufacturing facilities.
Innovation Solution
A machine learning-based system that uses cameras and multi-modal models to detect vehicle types and durations in repair bays, incorporating buffer thresholds to account for obstacles and provide real-time alerts when cycle thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera-based tracking is used in repair bays, then hardware infrastructure is simple, but measurement precision deteriorates due to obstacles blocking camera views
Solution Approach 1:
The patent introduces machine learning models as an intermediary layer between the camera and the tracking system. The ML model processes camera images to detect vehicles, classify them by type, and determine their presence even when partially obscured by obstacles. This intermediary intelligence layer compensates for the limited view capabilities of simple camera hardware, resolving the contradiction between measurement precision and device complexity.
2Productivity
If continuous monitoring is implemented, then productivity is improved, but loss of time increases due to obstacles blocking views
Solution Approach 1:
The system continuously monitors repair bays using cameras and machine learning models, providing real-time feedback on vehicle presence, type, and duration. The system tracks cycle times and compares them against thresholds, alerting operators when vehicles exceed expected processing times. This continuous feedback mechanism enables proactive management of productivity while accounting for time losses due to obstacles or other factors.
3Measurement precision
If simple timer-based tracking is used, then device complexity is low, but measurement precision deteriorates when vehicles are hidden by obstacles
Solution Approach 1:
The patent replaces simple mechanical timer-based tracking with an intelligent system using machine learning models. The ML models analyze camera images to detect vehicles, determine their types (e.g., sedan, truck, motorcycle), and track their presence duration. This substitution of mechanical timing with computational vision enables accurate measurement even when vehicles are partially hidden by obstacles, significantly improving measurement precision while managing device complexity through software intelligence.
Data Source
AI summary
Systems and methods for machine-learning based cycle time tracking and reporting for vehicles are provided. A system includes a processor coupled with memory. The system identifies one or more models trained with machine learning relating to physical characteristics of vehicles and location designations associated with vehicle areas. The system receives, from one or more cameras, a video stream that captures a vehicle disposed in a vehicle area comprising a location designation. The system determines, based on an analysis of a plurality of frames of the video stream and via the one or more models, a type of the vehicle disposed in the vehicle area and a duration the vehicle is disposed in the vehicle area. The system performs, based on the type of the vehicle and a comparison of the duration of the vehicle with a threshold, an action to cause delivery of the vehicle from the vehicle area.