Autonomous Driving Failure Detection With Learned Sound Criteria
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Solution Overview
Problem
Current technologies lack an accurate method for determining vehicle failure in autonomous driving systems, which is crucial for determining responsibility in case of accidents and ensuring safety, as existing methods are not reliable enough to differentiate between driver and vehicle control.
Innovation Solution
A vehicle failure determining apparatus and method that uses sensors to collect autonomous driving information, compares it with a determination criterion, and updates this criterion through learning processes, incorporating precision diagnosis sections based on road and traffic information to generate reliable failure determination results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous driving information is collected and compared with determination criterion to determine vehicle failure, then vehicle failure determination capability is improved, but measurement precision and reliability are insufficient without continuous learning updates
Solution Approach 1:
The system performs preliminary learning to generate failure sound information before actual failure determination. The controller collects learning autonomous driving information, processes it through learning algorithms, and generates determination criteria in advance. This preliminary preparation ensures that when actual failure determination is needed, reliable and precise criteria are already available for comparison.
Solution Approach 2:
The system implements continuous feedback through the learning process. The controller collects autonomous driving information, compares it with determination criteria, and when new failure patterns are detected or after accumulating sufficient data, updates the determination criteria through renewed learning. This feedback loop continuously improves measurement precision and reliability.
2Measurement precision
If learning processes are used to generate and update failure sound information, then determination accuracy is improved, but device complexity and processing time increase
Solution Approach 1:
The learning system operates autonomously without requiring external intervention. The controller automatically collects autonomous driving information, performs learning processing, generates failure sound information, and updates determination criteria all through self-service mechanisms. This reduces the need for complex external training infrastructure while maintaining high determination accuracy.
Solution Approach 2:
The system performs learning and generates determination criteria in advance before actual failure determination is needed. By preparing the learning models and criteria beforehand, the system reduces real-time processing complexity while maintaining high accuracy during actual failure detection operations.
3Reliability
If precision diagnosis sections are set based on road and traffic information, then determination reliability in critical sections is improved, but device complexity increases
Solution Approach 1:
The system applies different determination strategies to different road sections. Precision diagnosis sections identified based on road and traffic information receive enhanced determination reliability through prioritized comparison with failure sound information. This local quality approach focuses computational resources on critical sections where failure determination is most important, rather than uniformly processing all sections.
Solution Approach 2:
The system preliminarily identifies and classifies precision diagnosis sections based on road and traffic information before performing failure determination. This preliminary classification allows the system to prepare appropriate determination criteria and allocate processing resources in advance, improving reliability in critical sections without adding excessive real-time complexity.
Data Source
AI summary
An apparatus for determining failure of a vehicle is provided. The apparatus includes a sensor configured to obtain autonomous driving information, and a controller configured to compare the autonomous driving information obtained during autonomous driving with a determination criterion set as failure sound information and to determine whether the failure occurs, based on the comparison result.