Autonomous Driving Pattern Evaluation for Continuous Learning Safety
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Solution Overview
Problem
Autonomous driving systems require continuous evaluation to minimize accidents due to flaws and inefficiencies, especially as they scale and deploy more vehicles on the road, but existing technologies lack effective methods for continuous assessment and adaptation to new situations.
Innovation Solution
An autonomous vehicle evaluation system that uses machine learning to cluster driving patterns based on contextual and action factors, evaluates pattern degradation, and adapts by updating the autonomous driving control system to maintain safety and performance thresholds, with the ability to self-modify and share learning across the ecosystem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous driving systems continuously learn and adapt through self-modification, then performance and efficiency improve, but system reliability and safety may deteriorate due to uncontrolled changes
Solution Approach 1:
The patent implements a feedback mechanism where the autonomous driving system continuously monitors its own driving patterns, compares them against established safety thresholds and performance metrics, and receives feedback signals that trigger selective updates. This closed-loop feedback ensures that learning and adaptation occur only when beneficial and safe, preventing uncontrolled modifications that could compromise reliability.
Solution Approach 2:
The system dynamically adjusts its learning parameters and adaptation rates based on operational context, safety conditions, and performance needs. Rather than continuous uncontrolled learning, the system modulates its learning intensity and selectivity, enabling high productivity when safe and maintaining reliability when conditions require stability.
2Adaptability or versatility
If the autonomous driving system is continuously evaluated and modified, then adaptability to new situations improves, but system complexity increases
Solution Approach 1:
The evaluation system is segmented into distinct functional modules: data collection module, pattern recognition module, evaluation module, and update module. Each module handles specific tasks independently, managing complexity through functional decomposition while maintaining overall adaptability through coordinated operation of these specialized components.
Solution Approach 2:
The system performs preliminary evaluation and validation of potential modifications before implementing them. By pre-assessing the impact of proposed changes against safety criteria and performance goals, the system prepares adaptation strategies in advance, reducing the complexity of real-time decision-making while maintaining high adaptability.
3Reliability
If driving patterns are clustered and evaluated for degradation, then safety and performance are maintained, but computational resources and time are consumed
Solution Approach 1:
The system implements periodic evaluation of driving patterns at strategically determined intervals rather than continuous monitoring. By evaluating patterns periodically based on accumulated data thresholds or time intervals, the system maintains safety and performance through regular assessments while minimizing unnecessary computational overhead and time loss between evaluations.
Solution Approach 2:
The evaluation process dynamically adjusts parameters such as evaluation frequency, data sampling rates, and clustering granularity based on operational conditions, risk levels, and computational resource availability. This adaptive parameter adjustment optimizes the balance between thorough safety evaluation and efficient resource utilization, reducing time loss when safety risks are low while intensifying evaluation when risks increase.
Data Source
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.