Cloud-Based Vehicle Diagnostics with Crowd-Sourced Alert Confidence
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Solution Overview
Problem
Current systems lack an efficient method for managing vehicle data and providing real-time diagnostics, recommendations, and service solutions to vehicle owners, particularly in integrating vehicle systems with cloud-based services for seamless communication and data processing.
Innovation Solution
The system establishes a wireless connection between vehicles and cloud services, allowing for data collection, processing, and notification of alerts, which includes accessing diagnostics and crowd-sourced data to recommend solutions, and enables the transfer of electronic keys for service providers to address vehicle issues, with features like user profile management and customization of vehicle settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle systems are integrated with cloud-based services for real-time data processing and diagnostics, then the quality of service and diagnostic accuracy is improved, but the device complexity and network dependency increase
Solution Approach 1:
The patent introduces a cloud-based processing system as an intermediary between vehicle sensors and diagnostic tools. The vehicle electronics collect raw data locally, then transmit it to cloud services for advanced processing, pattern recognition, and comparison with crowd-sourced data from other vehicles. This intermediary approach enables complex diagnostics without overloading the vehicle's onboard computing resources.
Solution Approach 2:
The diagnostic system is segmented into multiple components: local vehicle electronics for data collection, cloud-based processing for analysis, and user interfaces for presenting results. This segmentation allows each component to specialize in specific tasks, improving overall diagnostic accuracy while distributing system complexity across multiple manageable modules.
2Measurement precision
If crowd-sourced data from other vehicles is integrated into diagnostics, then the confidence level for alert selection is improved, but the quantity of data to process increases
Solution Approach 1:
The cloud-based system performs preliminary filtering, aggregation, and analysis of crowd-sourced data before presenting it to users. Data from multiple vehicles is pre-processed to identify patterns and generate confidence levels for various alerts, so that users receive pre-digested information rather than raw data volumes. This preliminary action reduces the effective data volume users must process while maintaining high confidence levels.
3Ease of operation
If electronic keys are transferable to service providers for vehicle access, then the ease of service operation is improved, but the security risks increase
Solution Approach 1:
The electronic key system implements dynamic access control where service provider credentials are temporarily activated, automatically expire after service completion, and can be remotely revoked. The system adapts key permissions based on the specific service being performed, the service provider's credentials, and real-time monitoring of access events. This dynamic approach enables easy service access while continuously managing security risks through adaptive control mechanisms.
Data Source
AI summary
Methods, systems and computer readable media are provided. One example method includes establishing a connection with a vehicle over a wireless network, and associating the vehicle to a user account of an online service, wherein a vehicle type for the vehicle is identified in the user account. The method further includes receiving vehicle data for vehicle status information. The vehicle status information is for one or more vehicle systems of the vehicle. The method includes accessing one or more databases that include diagnostics data for the vehicle type and crowd sourced data for the vehicle type. The method includes processing the vehicle data that is received against the diagnostics data and the crowed sourced data. The processing is configured to select an alert from among a plurality of possible alerts. The crowd sourced data is configured to influence a confidence level for selecting the alert. The method includes sending a notification of the alert to the user account. The notification includes a recommended solution for handling the alert.