Edge Analytics for Vehicle Test Data Processing
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Solution Overview
Problem
The evaluation of vehicle test data generated during development phases is time-consuming and computation-intensive due to the fragmented nature of the data and the limited processing capabilities of vehicle control devices, which are not designed for complex computing operations.
Innovation Solution
A cloud-based storage and computing system generates edge analytics algorithms based on vehicle test data and transmits them to an edge analytics computing device inside the vehicle, allowing for decentralized evaluation of the data without the need for continuous data transmission to the cloud, using encrypted connections and AI methods like neural networks for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If vehicle test data is evaluated using cloud-based storage and computing devices, then processing power and data analysis capability are improved, but network resources and data transmission requirements increase
Solution Approach 1:
The system divides the evaluation process into two segments: cloud-based algorithm generation and edge-based data execution. The cloud device generates and transmits algorithms, while the edge analytics computing device executes them locally during test drives, eliminating the need to continuously transmit raw data to the cloud for processing.
Solution Approach 2:
The cloud-based storage and computing device performs preliminary action by generating and preparing edge analytics algorithms in advance based on modelling vehicle test data. These pre-generated algorithms are then transmitted to the edge analytics computing device, which executes them during actual test drives without requiring real-time cloud processing.
2Productivity
If control devices perform complex computing operations for data evaluation, then data processing capability is improved, but device complexity and cost increase
Solution Approach 1:
The system extracts the complex computing operations from the control devices and relocates them to a dedicated edge analytics computing device. The control devices only perform simple data collection and transmission functions, while the edge analytics computing device handles the complex algorithm execution, maintaining simplicity in control devices while enabling advanced processing.
Solution Approach 2:
The edge analytics computing device acts as an intermediary between the simple control devices and the complex cloud-based algorithms. It receives algorithms from the cloud, executes them locally using its own processing power, and returns results, thereby enabling complex processing without increasing control device complexity.
3Loss of information
If all vehicle test data is transmitted to cloud for evaluation, then comprehensive data analysis is achieved, but data transmission time and network load increase
Solution Approach 1:
The system extracts only the essential information (evaluation results and insights) from the raw vehicle test data and transmits these condensed results to the cloud for storage and further analysis. The bulk of the raw data remains processed locally at the edge device, dramatically reducing transmission volume and time while preserving analysis completeness.
Data Source
AI summary
A method for processing vehicle test data of a vehicle, in which a cloud-based storage and computing device that is communicatively connected to the vehicle via a data network is provided with a multiplicity of modelling vehicle test data that are provided by a plurality of control devices and/or sensor devices of the vehicle. The cloud-based storage and computing device analyzes the received modelling vehicle test data and takes these modelling vehicle test data as a basis for generating test-case-specific edge analytics algorithms in an automated manner and transmits the algorithms to an edge analytics computing device inside the vehicle via the data network. The edge analytics computing device receives a multiplicity of vehicle test data from the control devices and/or the sensor devices during the performance of test or trial drives by the vehicle and evaluates the data by way of the edge analytics algorithms.