Autonomous Driving Confidence Level Prediction
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Solution Overview
Problem
Current autonomous driving systems cannot predict or alert drivers of potential future failures, leading to unexpected system failures during operation, which can compromise safety and the driving experience.
Innovation Solution
A method and system that determines the confidence level of an autonomous driving system by assessing the impact of environmental factors on hardware capabilities, using real-time data processing and indexing technologies to calculate the potential effect on system functions and alert the driver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined threshold comparison method is used to detect system failures, then system reliability is improved by ensuring safe operation through functional safety design, but the system cannot predict future failures or inform drivers in advance, leading to sudden functional failures during driving
Solution Approach 1:
The system performs preliminary evaluation of hardware capability under future environmental conditions before actual failure occurs. By pre-assessing how environmental factors will affect hardware performance on upcoming road sections, the system can predict potential failures and inform drivers in advance, transforming reactive failure detection into proactive failure prediction.
Solution Approach 2:
The patent introduces an intermediary evaluation mechanism that bridges the gap between current system status and future failure risk. The hardware capability evaluation under environmental factors acts as an intermediary indicator, translating complex environmental impacts into a comprehensible confidence level that informs drivers about future system reliability without requiring direct detection of actual failures.
2Device complexity
If the system only responds to failures after they occur through predefined functional safety measures, then system complexity is kept manageable through standardized response protocols, but driver experience deteriorates due to panic and unexpected failures
Solution Approach 1:
The system implements a feedback mechanism that provides drivers with confidence level information about future system performance. By continuously evaluating hardware capability under predicted environmental conditions and communicating this information to drivers, the system creates a closed-loop feedback system that enhances driver confidence and reduces panic without requiring complex real-time control adjustments.
3Loss of information
If real-time environmental factor monitoring and hardware capability evaluation is implemented, then the system can predict future failures and improve driver awareness, but computational complexity increases due to continuous data processing and analysis
Solution Approach 1:
The system extracts only the essential environmental factors that have significant impact on hardware capability from the complete set of environmental parameters. By identifying and focusing on key factors rather than processing all environmental data, the system reduces computational complexity while maintaining effective failure prediction capability.
Data Source
AI summary
The present disclosure provides a method for identifying a confidence level of an autonomous driving system in an autonomous driving vehicle. The method includes determining hardware currently used by the autonomous driving system to realize required functions and environmental factors which affect the hardware capability of the hardware, and establishing a relationship between the hardware capability and the environmental factors; based on environmental factors on a future specific road section acquired in real time, calculating a degree to which the hardware capability will be affected on the specific road section; in consideration of acquired the environmental factors, judging an influence of the affected hardware capability on the realization of required functions of the autonomous driving system based on the acquired the environmental factors; and reflecting the influence on the realization of required functions of the autonomous driving system as a confidence level, and prompting the confidence level to a driver.