Vehicle Anomaly Detection Using Mobile Device Data and Machine Learning
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Solution Overview
Problem
Vehicle owners face challenges in performing early and convenient diagnostics due to the requirement of expensive and specialized equipment to read diagnostic sensors, leading to delayed repairs and increased costs.
Innovation Solution
A system utilizing mobile devices to collect vehicle data, determine vehicle attributes, and train machine-learning models to detect anomalies, allowing for real-time detection of issues without the need for specialized equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive and specialized equipment is used to read diagnostic sensors, then diagnostic accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the expensive diagnostic equipment by using machine learning models trained on data from professional diagnostic tools. The model replicates the diagnostic capabilities of specialized equipment through software, eliminating the need for physical copies of expensive devices while maintaining diagnostic accuracy.
Solution Approach 2:
The patent replaces the mechanical and electronic diagnostic equipment with an information-processing system. Instead of using physical sensors and specialized reading devices, the system uses mobile device sensors combined with machine learning algorithms to perform diagnostics, substituting mechanical systems with computational ones.
2Reliability
If professional diagnostic equipment is required, then detection reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent enables vehicle owners to perform self-diagnosis using their own mobile devices. The system is designed to be autonomous, automatically collecting data from the vehicle and analyzing it through pre-trained machine learning models without requiring professional intervention or specialized knowledge from the user.
Solution Approach 2:
The patent introduces a mobile device as an intermediary between the vehicle's diagnostic sensors and the analysis system. The mobile device serves as a bridge, collecting data from the vehicle and transmitting it to the machine learning model, making professional-grade diagnostics accessible through a common consumer device.
3Loss of time
If early detection is enabled, then loss of time is reduced, but device complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on extensive datasets before deployment. The models are prepared in advance to recognize patterns and anomalies, enabling immediate detection when deployed in the field without requiring complex real-time processing or additional preparation time.
Solution Approach 2:
The patent uses mobile device sensors that exceed the minimum requirements for diagnostics. By utilizing the capabilities of consumer mobile devices, which have more sensors and processing power than strictly necessary, the system achieves early detection without requiring purpose-built complex diagnostic equipment.
Data Source
AI summary
Various embodiments may be directed to systems, devices, apparatuses to perform techniques such as determining vehicle data for a plurality of vehicles, wherein the vehicle data may be based on detections made by input devices of a plurality of mobile devices. Embodiments may further include determining vehicle attributes for the plurality of vehicles and environmental data. Embodiments also include training a machine-learning model with the vehicle data, the vehicle attributes, and the environmental data, and the machine-learning model may be trained to detect anomalies associated with vehicles having the at least one of the vehicle attributes. Embodiments may also include a trained machine-learning model to data to detect anomalies associated with a vehicle.