Vehicle Data Processing Method for Cost Optimization
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Solution Overview
Problem
The high costs associated with data storage and transfer in automated driving systems, particularly in semiautomated and highly automated vehicles, are exacerbated by the generation of large data volumes and the inefficiency of recording non-relevant data, leading to increased memory usage and prolonged recording campaigns, as well as limited data transfer in operational fleets due to cost constraints.
Innovation Solution
A method that assesses the value of data in real-time within the vehicle, using a cost function to determine whether to store or transfer data externally, prioritizing data based on technical usability and timeliness, allowing for efficient decision-making on storage and transfer, thereby reducing costs and optimizing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all generated data are stored in the vehicle and transferred later, then complete data sets are available for development, but storage costs and transfer costs increase significantly
Solution Approach 1:
The system performs preliminary assessment of data value immediately upon generation in the vehicle, determining which data are worth storing and transferring before actual storage or transfer occurs. This preliminary classification avoids storing low-value data and ensures high-value data are prioritized for transfer.
Solution Approach 2:
Different data receive different treatment based on their assessed value. High-value data are transferred externally, medium-value data are stored locally with selective transfer, and low-value data are discarded. This differentiated approach optimizes both data completeness and cost efficiency.
2Quantity of substance
If memory capacity is increased to store more data, then data availability improves, but vehicle costs and storage efficiency deteriorate
Solution Approach 1:
The system dynamically adjusts data retention policies based on real-time assessment of data value and current storage capacity. As storage fills up, the system prioritizes keeping high-value data and discarding low-value data, rather than using static storage allocation.
Solution Approach 2:
The system changes the parameter of data value assessment continuously, evaluating data based on timeliness, novelty, and relevance to development goals. This allows the same storage capacity to effectively hold more valuable data over time as data age and lose value.
3Loss of energy
If data transfer volume is limited to reduce costs, then transfer costs decrease, but data value for development is lost
Solution Approach 1:
The system performs preliminary assessment of data value immediately upon generation in the vehicle, determining which data are worth storing and transferring before actual storage or transfer occurs. This preliminary classification avoids storing low-value data and ensures high-value data are prioritized for transfer.
Solution Approach 2:
The system uses feedback from development results to continuously improve data value assessment. As development goals become clearer and data patterns are understood better, the system refines its ability to identify which data are most valuable, optimizing transfer decisions over time.
4Loss of information
If recording campaigns are extended to capture more situations of interest, then data quality improves, but recording duration and costs increase
Solution Approach 1:
The system performs preliminary assessment of data value immediately upon generation in the vehicle, determining which data are worth storing and transferring before actual storage or transfer occurs. This preliminary classification avoids storing low-value data and ensures high-value data are prioritized for transfer.
Solution Approach 2:
The system replaces the mechanical approach of extending recording duration with an intelligent filtering system that identifies and captures valuable data situations regardless of campaign length. This substitution of intelligent detection for extended time-based recording reduces campaign duration while maintaining data quality.
Data Source
AI summary
A method for processing data in a vehicle. The method includes: a) receiving data of components of the vehicle, b) checking the received data with respect their value for the development, further development, and/or the serial operation of vehicles and/or components thereof, c) deciding in consideration of step b) whether the data are to be stored in the vehicle (1) or sent to a vehicle-external location.