AI Vehicle Monitoring for Real-Time Degradation Mitigation
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Solution Overview
Problem
Existing vehicles lack effective systems to monitor and mitigate degradation attributes in real-time, leading to potential safety and performance issues.
Innovation Solution
Integration of an AI-based system that receives sensor data to identify degrading attributes, determines actions to reduce degradation, and provides notifications through a display device, utilizing a zonal architecture of ECUs for efficient power management and integrating AI models for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and AI-based analysis systems are integrated into vehicles, then vehicle safety and reliability are improved, but device complexity and power consumption increase
Solution Approach 1:
The vehicle system is divided into multiple ECUs organized in zones, with dedicated functions for sensor data acquisition, AI model execution, and user interface control. This segmentation allows complex monitoring functions to be distributed across specialized modules rather than concentrated in a single complex system.
Solution Approach 2:
An ECU serves as an intermediary between sensors and the AI model, pre-processing sensor data and managing communication between different system components. This intermediary layer simplifies the overall system architecture by centralizing data management functions.
2Measurement precision
If continuous sensor data collection and AI model execution are performed, then degradation detection precision is improved, but energy consumption increases
Solution Approach 1:
The system executes AI models periodically based on degradation thresholds and operational conditions rather than continuously. The ECU monitors sensor data and triggers AI analysis only when degradation indicators suggest potential issues, reducing computational load while maintaining detection precision.
Solution Approach 2:
The system dynamically adjusts monitoring intensity and AI model execution frequency based on vehicle operating conditions, degradation rates, and power availability. This allows high-precision monitoring when needed while conserving energy during normal operation.
3Duration of action of stationary object
If proactive maintenance recommendations are provided, then vehicle longevity is improved, but loss of time for implementation occurs
Solution Approach 1:
The system performs preliminary degradation analysis and provides maintenance recommendations before actual vehicle failures occur. By continuously monitoring sensor data and executing AI models, the system identifies potential issues early and alerts users in advance, allowing proactive maintenance scheduling.
Solution Approach 2:
The system implements feedback loops where AI model results inform maintenance recommendations, which are then tracked through the user interface. This feedback mechanism ensures that maintenance actions are timely and effective, reducing overall vehicle downtime while extending longevity.
Data Source
AI summary
An example operation includes one or more of receiving sensor data of a vehicle as the vehicle travels along a route, identifying an attribute of the vehicle that is degrading while the vehicle is travelling along the route based on the sensor data, determining an action to take to reduce degradation of the attribute of the vehicle when travelling along the route based on execution of an artificial intelligence (AI) model on the sensor data, and displaying a notification with an instruction to perform the action to take to reduce the degradation of the attribute of the vehicle via a display device of the vehicle.