Dynamic Vehicle Data Collection Scheduling System
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Solution Overview
Problem
Current data collection, processing, and machine learning analysis systems in vehicles operate in a fixed manner, lacking the ability to selectively collect and process data based on specific time, geographical location, and other triggers, limiting their flexibility and effectiveness.
Innovation Solution
A dynamic data collection system that allows for scheduling data collection jobs on multiple vehicles, processing, conditioning, and applying machine learning models, using cloud-based servers to distribute data processing applications and machine learning models to vehicles, enabling flexible data selection and analysis based on time, location, and other triggers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data collection systems operate in a fixed manner, then system simplicity is maintained, but flexibility and adaptability to different conditions deteriorate
Solution Approach 1:
The system transitions from fixed data collection to dynamic scheduling where collection parameters (time, location, triggers) can be adjusted in real-time based on conditions. The scheduling system allows operators to define when and where data should be collected from multiple vehicles, making the system adaptable to different scenarios without permanent reconfiguration.
Solution Approach 2:
The patent implements dynamic changes in data collection parameters including time of day (TOD), geographical location, and trigger conditions. These parameters can be modified through the scheduling system to adapt to different data collection campaigns without changing the underlying system architecture.
2Measurement precision
If data is collected from multiple vehicles with selective criteria, then data relevance and accuracy improve, but system complexity and coordination requirements increase
Solution Approach 1:
The scheduling system serves multiple functions: it coordinates data collection across multiple vehicles, manages timing and location criteria, handles trigger conditions, and distributes collection parameters. This multi-functional approach consolidates coordination complexity into a single system rather than requiring separate mechanisms for each function.
Solution Approach 2:
The patent introduces a scheduling system as an intermediary layer between the data collection requirements and the actual vehicle sensors. This mediator translates high-level collection criteria into specific vehicle commands, simplifying the coordination complexity by providing a unified interface for managing multiple vehicles.
3Loss of information
If dynamic scheduling and selective data collection are implemented, then data relevance improves, but processing and management complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-defining scheduling parameters, time windows, location criteria, and trigger conditions before data collection begins. This advance preparation reduces processing complexity during actual data collection, as the system only needs to evaluate pre-established criteria rather than making complex decisions in real-time.
Data Source
AI summary
A method for collecting data of a surrounding of a plurality of motor vehicles by a data collection system involves generating a collection job having a first set of information describing which data are to be collected by the plurality of motor vehicles, and a second set of information describing when the data are to be collected, for the plurality of motor vehicles. The collection job is transmitted to the plurality of motor vehicles by a communication device of the data collection system. Preprocessed data is received from each motor vehicle. The surroundings of each motor vehicle were captured by a capturing device of each motor vehicle depending on the transmitted collection job to generate collected data and the collected data were preprocessed by an electronic computing device of each motor vehicle to generate preprocessed data and further processing of the transmitted and preprocessed data by the backend server.