Autonomous Driving Fault-Rate Control for Selective Function Shutdown
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Solution Overview
Problem
Existing autonomous vehicle systems require extensive data processing and learned models to monitor and identify faults and failures, leading to increased costs and inefficiencies, necessitating a more accurate and efficient method for fault and failure identification.
Innovation Solution
An autonomous driving control apparatus that adaptively calculates fault and failure rates using external information from electronic devices, considers part characteristics and driving conditions, and selectively provides driver alerts or stops functions based on calculated rates, reducing the need for extensive data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a previously learned model or extensive data processing is used to improve fault detection accuracy, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent extracts only the essential parameters needed for fault detection (voltage, current, temperature, rotation speed) from the complex data stream, rather than processing all available data. This selective extraction maintains detection accuracy while reducing processing complexity.
Solution Approach 2:
The system uses simple, easily replaceable sensors and processing logic that can be quickly updated or replaced, avoiding investment in complex, expensive learning models. The fault detection rules are simplified to basic threshold comparisons that are computationally inexpensive.
2Measurement precision
If a previously learned model or extensive data processing is used to improve fault detection accuracy, then measurement precision improves, but manufacturing cost increases
Solution Approach 1:
The system employs inexpensive sensors and simple processing logic that can be easily manufactured and replaced, avoiding the need for expensive machine learning models and high-performance computing hardware.
Solution Approach 2:
The system uses readily available vehicle data from existing sensors without requiring external training data or cloud-based processing, making the system self-sufficient and reducing manufacturing costs.
3Reliability
If real-time monitoring of all parts is performed to improve reliability, then reliability improves, but use of energy increases
Solution Approach 1:
The system extracts only critical parameters (voltage, current, temperature, rotation speed) for monitoring, rather than continuously processing all available sensor data. This selective monitoring maintains reliability while reducing energy consumption.
Solution Approach 2:
The system performs monitoring at just the right level of detail needed for reliable fault detection, using simple threshold comparisons rather than exhaustive analysis, thereby minimizing energy usage while maintaining detection capability.
Data Source
AI summary
An autonomous driving control apparatus requests information associated with a plurality of parts of an autonomous vehicle from an external electronic device and calculates a fault rate for each of the plurality of parts using the information associated with the plurality of parts. The apparatus identifies that a first fault rate corresponding to a first part among the calculated fault rates is greater than or equal to a specified first value and calculates a first failure rate at which the first part causes a failure of the autonomous vehicle using the first fault rate. The apparatus stores the first fault rate and information associated with the first part in storage and stops performing a function associated with the first part or stops performing the entire autonomous driving function of the autonomous vehicle after a fault or a failure of the autonomous vehicle occurs.