Autonomous Vehicle Software Interaction Detection
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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, utilizing an interaction detection and analysis (IDA) computing device, aggregates and analyzes data from multiple AVs to identify performance outcomes. This involves receiving software ecosystem data, environmental conditions data, and performance data, applying clustering algorithms to identify similar software ecosystems, and using machine learning algorithms to detect interactions and correlations between software applications and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from a single autonomous vehicle is analyzed, then the analysis is simple and quick, but the ability to detect software interaction issues under specific environmental conditions is insufficient
Solution Approach 1:
The patent segments the large volume of aggregated data from multiple autonomous vehicles into individual vehicle data records, each containing software ecosystem data, environmental conditions data, and performance data. This segmentation allows the system to process and analyze data from many vehicles while maintaining the ability to detect specific software-environment interactions by comparing patterns across segmented records.
Solution Approach 2:
The patent merges data from multiple autonomous vehicles into a centralized system, combining software ecosystem data, environmental conditions data, and performance data from many vehicles. This merging enables the detection of software interaction issues that would be invisible when analyzing single-vehicle data, as patterns emerge from the aggregated dataset.
2Reliability
If software updates are performed frequently to fix issues, then vehicle performance improves, but the complexity of software ecosystem management increases
Solution Approach 1:
The patent performs preliminary analysis of software ecosystem data, environmental conditions data, and performance data to identify potential interaction issues before they manifest as adverse vehicle performance. By detecting software-environment interactions in advance through data aggregation and analysis, the system enables proactive software updates that prevent performance degradation rather than reacting to failures.
Solution Approach 2:
The patent implements a feedback mechanism where performance data from autonomous vehicles is continuously aggregated and analyzed to detect software interaction patterns. This feedback loop informs subsequent software updates, allowing the system to refine software ecosystems based on real-world performance data and identified interaction issues, thereby improving reliability while managing complexity through data-driven iterations.
3Measurement precision
If comprehensive data aggregation from multiple sources is performed, then detection capability improves, but data processing time and computational resources increase
Solution Approach 1:
The patent segments comprehensive data from multiple sources into structured data records for individual vehicles, each containing software ecosystem data, environmental conditions data, and performance data. This segmentation enables efficient processing by organizing data into manageable units that can be analyzed systematically, reducing the time required to process comprehensive datasets while maintaining detection capability.
Solution Approach 2:
The patent introduces an intermediary processing layer that aggregates and standardizes data from multiple autonomous vehicles before analysis. This intermediary system organizes raw data into structured formats, pre-processes information, and prepares datasets for interaction detection, thereby reducing the computational burden and processing time required for comprehensive data 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 A Vs 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.