Autonomous Vehicle Software Interaction Detection
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Solution Overview
Problem
Current systems lack the capability to effectively analyze and manage software interactions within autonomous vehicles (AVs) to predict and mitigate adverse performance outcomes, as they cannot aggregate and analyze data from multiple AVs to identify risks specific to individual vehicles.
Innovation Solution
A centralized interaction detection and analysis (IDA) computing device that receives and stores data from AVs, compares software ecosystems and hardware with similar AVs, identifies potential performance outcomes and risks, and alerts users, using clustering and machine learning algorithms to detect correlations between software and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple AVs is aggregated for analysis, then the ability to identify software interaction risks is improved, but the system complexity increases
Solution Approach 1:
The system segments the fleet-wide software ecosystem data into individual AV profiles, each containing software versions, hardware configurations, and performance metrics. This segmentation allows the centralized system to manage complexity by processing data in manageable, vehicle-specific units rather than as a monolithic dataset.
Solution Approach 2:
The centralized analysis system acts as an intermediary between individual AVs and the risk identification process. It aggregates data from multiple AVs, performs interaction analysis, and returns risk assessments to individual vehicles, thereby enabling fleet-wide learning without requiring complex peer-to-peer communication infrastructure.
2Reliability
If software ecosystems are updated regularly, then adverse outcomes are reduced, but the detection and measurement of interaction issues becomes more difficult
Solution Approach 1:
The system performs preliminary analysis of software interactions by comparing an AV's software ecosystem against known problematic combinations identified from fleet data. This preliminary risk assessment occurs before adverse outcomes manifest, allowing proactive updates or mitigations to be applied.
Solution Approach 2:
The system establishes a feedback loop where performance data from individual AVs is continuously collected, analyzed against fleet-wide patterns, and used to generate risk assessments that trigger software updates. This feedback mechanism makes interaction issues detectable even in frequently updated ecosystems by leveraging collective fleet learning.
3Reliability
If individual AV risk assessment is implemented, then safety is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary aggregation and preprocessing of software ecosystem data at the centralized level while AVs are operating normally. This preliminary work prepares risk profiles in advance, so that when individual AV assessment is needed, the processing time is minimized by leveraging pre-computed fleet-wide patterns.
Solution Approach 2:
The system merges individual AV data with fleet-wide aggregation results to perform risk assessment. By combining pre-processed fleet patterns with individual vehicle specifics, the system achieves comprehensive individual assessment without requiring complete re-analysis of all fleet data for each vehicle.
Data Source
AI summary
An interaction detection and analysis (“IDA”) computing device for analyzing data from a plurality of autonomous vehicles (“AVs”) relative to an individual AV that identifies potential performance outcomes and risks based on interactions associated with software onboard the AVs may be provided. The IDA computing device may include at least one processor programmed to (i) receive AV data from the individual AV including a software ecosystem and hardware installed on the individual AV, (ii) store the received AV data in a data record in a database, (iii) compare the software ecosystem and the hardware for the individual AV to data associated with AVs having relatively similar software ecosystems and hardware, (iv) identify at least one known performance outcome and risk based on the data from the AVs having relatively similar software ecosystems and hardware, and (v) alert a user of the individual AV of the identified performance outcome and risk.