AV Software Interaction Detection Under Changing Environmental Conditions
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Solution Overview
Problem
Current systems fail to effectively evaluate risk based on interactions between autonomous vehicle (AV) software ecosystems and environmental conditions, leading to potential adverse vehicle performance issues.
Innovation Solution
A centralized system, the Interaction Detection and Analysis (IDA) computing device, aggregates and analyzes data from multiple AVs using clustering algorithms to identify similar software ecosystems and machine learning algorithms to detect interactions between software applications and environmental conditions that may result in specific outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from a single autonomous vehicle is analyzed, then the analysis system remains simple and manageable, but the ability to detect software interaction issues under specific environmental conditions is insufficient
Solution Approach 1:
The patent combines data from multiple autonomous vehicles into a centralized database, merging individual vehicle datasets to enable comprehensive analysis of software-environment interactions across diverse conditions and vehicle configurations
Solution Approach 2:
The patent segments the large volume of aggregated data into meaningful clusters using clustering algorithms, grouping similar software-environment scenarios together to facilitate targeted analysis and reduce complexity of the overall dataset
2Reliability
If comprehensive data from multiple vehicles is aggregated, then the detection capability improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary data processing and clustering before detailed interaction analysis, pre-organizing the aggregated data into structured groups that simplify subsequent machine learning analysis and reduce computational complexity
Solution Approach 2:
The patent introduces clustering algorithms as an intermediary step between raw data aggregation and final interaction detection, creating structured intermediate representations that bridge the gap between voluminous raw data and meaningful safety assessments
3Measurement precision
If traditional data aggregation methods are used, then implementation is straightforward, but the ability to detect correlations between software applications and environmental conditions is limited
Solution Approach 1:
The patent replaces traditional mechanical data aggregation methods with machine learning algorithms, using automated computational models to detect complex correlations between software applications and environmental conditions that would be impossible to identify through conventional analysis
Data Source
AI summary
An interaction detection and analysis (“IDA”) computing device that includes at least one processor in communication with at least one memory device is provided. The at least one processor being configured to store software ecosystem data, environmental condition data, and performance data in a plurality of data records in a database, wherein each data record i) is associated with one autonomous vehicle (AV) of a plurality of AVs and ii) includes the software ecosystem data, the environmental condition data, and the performance data of the corresponding AV; apply at least one machine learning algorithm to a set of the plurality of data records to identify an interaction between at least one software application and at least one environmental condition resulting in a particular outcome; and transmit, to at least one AV associated with the set of data records, at least one alert message advising of the particular outcome.