Autonomous Vehicle Failure Mode Management via Remote Server Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in managing failure modes due to the substantial computing power and data required to analyze risk levels and identify alternatives, especially with continuously changing data such as weather and traffic conditions, making it impractical to provide timely information to each vehicle.
Innovation Solution
A system that utilizes a remote server to collect and analyze data from autonomous vehicles, including failure mode diagnostics and environmental conditions, to determine risk levels and instruct vehicles on safe routes or remedial actions, leveraging a network for communication between vehicles, servers, and data sources like weather and traffic services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If each autonomous vehicle performs comprehensive risk analysis and failure mode evaluation locally, then the vehicle can make autonomous safety decisions, but the computing power and data processing requirements become prohibitively high for individual vehicles
Solution Approach 1:
The patent introduces a remote server as an intermediary between autonomous vehicles and data sources. The server receives failure mode data from vehicles, performs comprehensive risk analysis by integrating multiple data sources (weather, traffic, resource availability), and returns risk levels and alternative actions. This mediator approach allows complex analysis to be performed centrally rather than requiring each vehicle to have equivalent computing capabilities.
Solution Approach 2:
The patent shifts the problem from a single-vehicle dimension to a multi-vehicle network dimension. By collecting data from multiple vehicles and multiple external data sources, the system performs risk analysis in a higher-dimensional space that incorporates fleet-wide patterns and environmental context, enabling more accurate risk assessment than any single vehicle could achieve alone.
2Measurement precision
If the vehicle collects and processes extensive real-time data from multiple sources to evaluate failure mode risk, then the risk assessment accuracy improves, but the time required to provide timely instructions deteriorates
Solution Approach 1:
The system performs preliminary risk assessments by continuously monitoring failure modes and maintaining updated risk models based on historical data and current conditions. When a failure mode occurs, the pre-established risk models and alternative action plans enable rapid response without requiring time-consuming real-time analysis of all possible scenarios.
Solution Approach 2:
The system implements continuous feedback loops where risk assessments are updated based on incoming data from vehicles and external sources. This allows the system to adapt to changing conditions dynamically, improving accuracy over time while maintaining responsive performance through iterative refinement rather than exhaustive analysis.
Data Source
AI summary
A first computer including a processor is programmed to receive an indication of a failure mode in a vehicle and wirelessly transmit the indication of the failure mode to a remote server. The computer is further programmed to receive a revised route to a destination based at least in part on the failure mode and operate the vehicle along the revised route.