Autonomous Driving Evaluation Through Continuous Pattern Degradation Tracking
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Solution Overview
Problem
Existing autonomous vehicle evaluation systems lack the capability for continuous, holistic evaluation of autonomous driving control systems, which is essential for ensuring safety and performance as the systems adapt and evolve over time.
Innovation Solution
A computing platform that receives sensor data from autonomous vehicle sensors, determines driving patterns, clusters them based on primary and secondary contextual and action factors, and evaluates their degradation over time, enabling continuous learning and self-modification of the autonomous driving system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous driving control systems continuously adapt and evolve through learning, then system performance and efficiency improve, but safety and reliability deteriorate due to unpredictable behavior changes
Solution Approach 1:
The patent implements continuous evaluation of autonomous driving systems by collecting real-world driving data, analyzing system responses through machine learning models, and providing feedback loops that assess both performance improvements and safety metrics. This feedback mechanism enables the system to learn from operations while maintaining safety through continuous monitoring and validation.
Solution Approach 2:
The system performs preliminary evaluation and validation of autonomous driving behaviors before full deployment. By pre-assessing system responses in simulated environments and conducting gradual real-world testing with continuous monitoring, the system prepares for safe adaptation while maintaining reliability during the learning process.
2Reliability
If comprehensive evaluation of autonomous driving systems is implemented, then safety and performance improve, but system complexity increases
Solution Approach 1:
The evaluation system is segmented into multiple independent components: data collection modules that gather driving information, machine learning models that analyze specific aspects of system behavior, and separate evaluation frameworks that assess different safety and performance metrics. This modular architecture reduces overall system complexity while enabling comprehensive evaluation.
Solution Approach 2:
The patent creates a universal evaluation platform that can assess multiple autonomous driving systems using the same framework and methodologies. This multi-functional system handles diverse data types, evaluates various driving scenarios, and applies consistent safety criteria across different vehicle platforms, reducing the need for separate evaluation systems.
3Measurement precision
If continuous monitoring and evaluation of autonomous driving systems is performed, then safety issues are detected earlier, but data processing requirements and computational resources increase
Solution Approach 1:
The system applies partial monitoring strategies where not all driving scenarios are evaluated with the same level of computational intensity. Routine, low-risk driving behaviors receive minimal evaluation, while unusual or high-risk scenarios trigger more intensive analysis. This selective approach maintains high safety assessment accuracy while reducing overall computational resource consumption.
Data Source
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AI summary
Aspects of the disclosure relate to an autonomous vehicle evaluation system that performs continuous evaluation of the actions, strategies, preferences, margins, and responses of an autonomous driving control system. A computing platform may receive sensor data from one or more autonomous vehicle sensors, manufacturer computing platform, or V2X computing platform. Based on this sensor data, the computing platform may determine one or more driving patterns. Based on a primary context corresponding to the one or more driving patterns, the computing platform may group the one or more driving patterns. The computing platform may determine a driving pattern degradation output indicating degradation corresponding to the one or more grouped driving patterns, and the computing platform may send the driving pattern degradation output to an autonomous driving system, which may cause the autonomous driving system to take corrective action accordingly.