Automotive Data Ingestion Framework for Platform Adaptability
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Solution Overview
Problem
The automotive industry faces complexity in obtaining data objects due to varied hardware and software platforms, leading to fragmented sensor readings and impediments in fostering an application ecosystem that utilizes vehicle data effectively.
Innovation Solution
A data ingestion framework is provided, utilizing a Hardware Abstraction Layer (HAL) and HAL Service Ingestor (HSI) framework to normalize data from different bus protocols, operating systems, and hardware platforms, allowing for the creation of Synthetic Sensors that derive intelligent insights from vehicle data without bespoke customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If bespoke customization is used to obtain data objects from varied hardware and software platforms, then data access can be achieved for each specific platform, but system complexity and development time increase significantly
Solution Approach 1:
The patent implements a universal data ingestion framework that can access data from multiple hardware and software platforms through a standardized interface. The framework uses platform-agnostic abstractions and a common data bus protocol to retrieve data objects from diverse sources without requiring bespoke customization for each platform, thereby reducing system complexity while maintaining broad adaptability.
Solution Approach 2:
The patent introduces an intermediary data bus layer that mediates between diverse hardware/software platforms and the data consumption layer. This intermediary layer handles platform-specific details internally while providing a consistent, simplified interface to consumers, effectively isolating complexity and enabling universal access across different platforms without requiring consumers to understand platform-specific nuances.
2Reliability
If platform-specific data access methods are used, then data can be obtained from each platform, but time to develop and deploy applications increases
Solution Approach 1:
The patent segments the data access functionality into separate, modular components: platform-specific data collectors that handle raw data retrieval and a universal data bus layer that standardizes the data format. This segmentation allows reliable platform-specific data access while isolating complexity in reusable modules, reducing development time for new applications as they can simply subscribe to standardized data objects without re implementing platform-specific access logic.
Solution Approach 2:
The framework performs preliminary action by pre-implementing and pre-testing platform-specific data collection mechanisms during framework development. The universal data bus and data objects are pre-configured with standardized interfaces and protocols, so that when applications are deployed, they can immediately access data without time-consuming setup or customization, thereby reducing overall development time while maintaining reliable data access.
3Quantity of substance
If fragmented sensor readings from different platforms are collected, then comprehensive data can be gathered, but data consistency and quality deteriorate
Solution Approach 1:
The patent applies parameter changes by transforming data from various platforms into a standardized format with consistent parameters and units. The universal data bus layer normalizes data types, value ranges, and communication protocols, converting fragmented sensor readings into homogeneous data objects that maintain consistency in quality and structure, thereby enabling reliable aggregation and analysis of comprehensive data from multiple sources.
Data Source
AI summary
A method at a computing device for providing vehicle data to a client, the method including receiving a data object at an ingestor block at the computing device, the ingestor block comprising a plurality of ingestor instances capable of interacting with different entities; converting the data object to a frame; providing the frame to a translation stack at the computing device to convert the frame into a normalized message; and providing the normalized message to the client.