AV Software Ecosystem Clustering for Environment Interaction Detection
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Solution Overview
Problem
Current systems fail to effectively aggregate and analyze data from autonomous vehicles to identify interactions between software ecosystems and environmental conditions, leading to unpredictable adverse performance outcomes.
Innovation Solution
An interaction detection and analysis (IDA) computing device that receives, stores, and analyzes software ecosystem data, environmental conditions data, and performance data from multiple autonomous vehicles, using clustering and machine learning algorithms to detect interactions and correlations that may result in adverse outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple autonomous vehicles is aggregated and analyzed using clustering and machine learning algorithms, then the ability to detect software-environment interactions and predict adverse outcomes is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system segments the fleet data into clusters based on software ecosystem similarities using clustering algorithms. This segmentation allows the system to analyze interactions within homogeneous groups, improving detection accuracy while managing complexity through modular processing of segmented data sets.
Solution Approach 2:
The patent introduces an intermediary processing layer that aggregates raw vehicle data, applies clustering algorithms to group similar software ecosystems, and then applies machine learning algorithms to detect interactions. This intermediary structure mediates between raw data and final interaction detection, managing system complexity through structured data transformation.
2Measurement precision
If comprehensive software ecosystem data, environmental conditions data, and performance data are collected and stored for each vehicle, then the precision of interaction detection is improved, but the data storage requirements and processing load increase
Solution Approach 1:
The system extracts and stores only the essential data elements needed for interaction detection: software ecosystem data, environmental conditions data, and performance data. By taking out only the relevant features and storing them in structured data records, the system achieves high measurement precision while avoiding the burden of storing complete raw data sets.
Solution Approach 2:
The patent applies local quality by storing data in vehicle-specific data records that contain only the relevant attributes for that vehicle's software ecosystem and operating conditions. Each data record is tailored to the specific vehicle's characteristics, providing precise local information without duplicating unnecessary data across all vehicles.
3Productivity
If clustering algorithms are applied to identify subsets of vehicles with similar software ecosystems, then the efficiency of interaction detection is improved, but the computational processing time increases
Solution Approach 1:
The system performs preliminary clustering of vehicle data into groups with similar software ecosystems before applying machine learning algorithms to detect interactions. This preliminary action organizes the data in advance, so that subsequent interaction detection can be performed more efficiently on pre-grouped data sets rather than on the entire fleet data.
Solution Approach 2:
By segmenting the fleet into clusters of vehicles with similar software ecosystems, the system divides the large-scale interaction detection problem into smaller, more manageable sub-problems. Each cluster can be analyzed independently, reducing the computational complexity and processing time compared to analyzing all vehicles together.
Data Source
AI summary
An interaction detection and analysis (“IDA”) computing device for aggregating and analyzing operations data from a plurality of autonomous vehicles (“AVs”) may be provided. The IDA computing device may include at least one processor programmed to (i) receive software ecosystem data, environmental conditions data, and performance data for a plurality of AVs, (ii) store the received data in a plurality of data records, (iii) apply at least one clustering algorithm to the plurality of data records to identify a subset of data records for AVs that have similar software ecosystems, and (iv) apply at least one machine learning algorithm to the identified subset of data records to detect i) an interaction between at least one software application and at least one environmental condition that may result in a particular outcome and ii) a correlation between the detected interaction and the particular outcome.