AV Software Interaction Screening for Trip-Specific Risk Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to effectively evaluate and mitigate potential adverse performance outcomes in autonomous vehicles due to interactions between software ecosystems and environmental conditions, which can lead to unforeseen issues like collisions, especially under specific conditions.
Innovation Solution
A system and method utilizing an AV computing device to aggregate and analyze data from multiple vehicles, identifying correlations between software applications and environmental conditions to predict and prevent adverse outcomes by executing remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from a large number of AVs is aggregated for enhanced analysis, then measurement precision and reliability of performance outcome evaluation improve, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the evaluation process into distinct modules: data collection from multiple AVs, software ecosystem characterization, environmental condition monitoring, correlation analysis, and risk scoring. Each module handles specific data types and processing tasks independently, managing complexity while enabling comprehensive multi-vehicle analysis for precise adverse outcome prediction
Solution Approach 2:
The system introduces an intermediary computing device that acts as a mediator between individual AVs and the central evaluation system. This intermediary aggregates software ecosystem data, environmental conditions, and performance outcomes from multiple vehicles, performing preliminary processing and correlation analysis before submitting results to the central system, thereby distributing computational complexity
2Adaptability or versatility
If software applications are updated frequently to improve performance, then adaptability and functionality improve, but reliability deteriorates due to interaction issues and unintended adverse outcomes
Solution Approach 1:
The system performs preliminary evaluation of software updates before deployment by analyzing correlation data from multiple AVs to predict potential adverse outcomes. The computing device assesses proposed software changes against historical interaction patterns and environmental conditions, identifying risks before the update reaches the fleet, thus maintaining reliability while enabling adaptability
Solution Approach 2:
The system implements continuous feedback loops where performance data from AVs running updated software is collected and analyzed to detect unintended interactions. When adverse outcomes are identified, the system generates feedback signals to trigger software recalls or patches, creating a self-correcting mechanism that maintains reliability alongside frequent updates
3Reliability
If comprehensive data collection from multiple AVs is implemented to evaluate software interactions, then reliability and measurement precision improve, but loss of time and processing overhead increase
Solution Approach 1:
The system implements partial data collection by focusing on specific software ecosystem parameters and environmental conditions most correlated with adverse outcomes. Rather than collecting all possible data from every AV, the system identifies and monitors only the critical subsets of data that provide sufficient reliability for prediction, reducing processing time while maintaining accuracy
Solution Approach 2:
The system performs preliminary filtering and preprocessing of data at the source AVs and intermediary devices before central aggregation. Data is screened, validated, and pre-processed locally to eliminate redundant information and ensure quality, reducing the burden on central processing systems and minimizing overall data collection time while maintaining reliable analysis
Data Source
AI summary
An autonomous vehicle (AV) computing device including at least one processor may be provided. The at least processor may be programmed to (i) receive a proposed trip including a destination location and a departure time, (ii) determine environmental conditions data based on the destination location and the departure time, (iii) retrieve current software ecosystem data for the AV, (iv) retrieve aggregated data for a plurality of AVs, the aggregated data including a plurality of correlations, each correlation including a) an interaction between at least one software application and at least one environmental condition and b) an adverse performance outcome associated with the interaction, (v) compare the environmental conditions data for the proposed trip and the current software ecosystem data for the AV to the plurality of correlations to identify an adverse performance outcome, and (vi) execute a remedial action to avoid the adverse performance outcome.