Driving Control Algorithm Parameter Tuning for Safety-Critical Conditions
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Solution Overview
Problem
Existing autonomous driving systems face challenges in optimizing algorithm performance and reliability under varying driving conditions, leading to inefficiencies and potential safety issues.
Innovation Solution
An algorithm operation management apparatus and method that adjusts operating parameters of vehicle algorithms based on learning results for different driving conditions, using a determination part to optimize performance and reliability by adjusting processor occupation time, resource usage, processing resolution, complexity level, and operation priority.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operating parameters for driving control algorithms are dynamically adjusted based on learning results from multiple driving conditions, then the reliability and performance of autonomous driving algorithms are improved, but the device complexity and computational overhead increase
Solution Approach 1:
The system performs preliminary learning during normal driving conditions to build a knowledge base of operating parameters for various driving scenarios. When an accident or safety-critical situation occurs, the system can immediately apply pre-learned parameter adjustments without requiring complex real-time computation, thus improving reliability while managing system complexity
Solution Approach 2:
The system dynamically changes operating parameters (such as sensor sampling rates, processing resolution, algorithm priority levels) based on learned driving conditions. This allows the system to optimize algorithm performance for different scenarios without requiring complete redesign of the control architecture, balancing reliability improvement with acceptable complexity
2Reliability
If operating parameters are adjusted to prioritize safety-critical algorithms during accident situations, then the reliability is improved, but the processing time and resource allocation for non-critical algorithms are reduced
Solution Approach 1:
The system segments algorithms into priority levels (safety-critical, important, optional) and applies different operating parameters to each segment based on the current driving condition. This allows safety-critical algorithms to receive enhanced resources while non-critical algorithms operate with reduced parameters, optimizing the trade-off between reliability and processing time
Solution Approach 2:
The system dynamically adjusts the priority levels and resource allocation of different algorithms based on the current driving situation. During normal conditions, all algorithms operate at standard priorities. During safety-critical situations, the system dynamically reconfigures priorities to ensure safety-critical algorithms receive necessary computational resources while minimizing overall processing time
Data Source
AI summary
Disclosed herein is an algorithm operation management apparatus and method, in which the algorithm operation management apparatus includes a learning part configured to learn by classifying an accident situation and a safety situation for each driving condition; a determination part configured to adjust an operating parameter for at least one algorithm related to driving control of a vehicle on the basis of a determination result of the learning part; and an operation part configured to perform the driving control of the vehicle by applying the adjusted operating parameter on the basis of the at least one algorithm.