Validation Processor for Autonomous Driving Road Condition Estimation
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Solution Overview
Problem
Machine learning-based estimates of environmental conditions in autonomous driving are not deterministic, leading to a need for extensive verification tests to ensure accuracy, which is impractical due to the large amount of data required, especially in safety-sensitive applications like autonomous driving.
Innovation Solution
An apparatus and method that utilize a validation processor to confirm or invalidate machine learning-based environmental condition estimates using ground truth measurements from higher-confidence sensors, such as ABS and TCS sensors, and communicate validation information between vehicles and a cloud server for fleet-wide validation and update.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning networks are used for environmental condition estimation in autonomous driving, then the system can process sensor data and provide environmental perception, but the estimates are not deterministically accurate and require extensive verification tests
Solution Approach 1:
The patent introduces a validation processor as an intermediary component that sits between the machine learning processor and the control system. This validation processor receives both the ML estimates and ground truth measurements from sensors, compares them, and validates the ML estimates before they are used for control decisions. This intermediary layer resolves the contradiction by maintaining automation while adding a deterministic validation mechanism.
Solution Approach 2:
The system implements a feedback loop where ground truth measurements from sensors are continuously compared against machine learning estimates. The validation processor uses this feedback to confirm or invalidate ML estimates, creating a closed-loop system that ensures reliability while maintaining automated operation. The feedback mechanism allows the system to learn and improve over time while maintaining deterministic accuracy.
2Reliability
If extensive verification tests are performed to validate machine learning estimates, then reliability improves, but the amount of data and testing required becomes impractical
Solution Approach 1:
The validation system uses the vehicle's own operational data and sensors to validate machine learning estimates in real-time during normal operation. Instead of requiring external extensive testing, the system performs self-validation using ground truth measurements from onboard sensors during actual driving conditions. This eliminates the need for large-scale separate testing while maintaining high reliability.
Solution Approach 2:
The system performs validation checks continuously during normal operation rather than requiring extensive pre-deployment testing. By validating ML estimates in advance during each driving episode using real-time sensor data, the system ensures reliability without needing to accumulate large datasets from separate test fleets before deployment.
3Measurement precision
If ground truth measurements are used to validate machine learning estimates, then accuracy is improved, but additional sensors and measurement systems are required
Solution Approach 1:
The validation processor is designed to work with multiple different sensor types and measurement systems, making it a universal validation layer that can accommodate various ground truth measurement approaches. Rather than requiring specific dedicated sensors, the system can utilize data from existing vehicle sensors (wheel speed sensors, accelerometer, GPS) for validation, reducing additional hardware complexity while maintaining measurement precision.
4Reliability
If machine learning networks with deep test depth and learning density are used, then the risk of inaccurate statements decreases, but the complexity and resource requirements increase
Solution Approach 1:
The validation processor serves as an intermediary that simplifies the reliability assurance process. Instead of requiring the machine learning network itself to achieve extremely high test depth and learning density, the validation layer provides an additional deterministic check that compensates for ML limitations. This separates the complexity of achieving high reliability from the ML model design, allowing simpler models to be used while maintaining high accuracy through validation.
Data Source
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AI summary
The present disclosure relates to an apparatus (100) for estimating a road condition. The apparatus (100) comprises an input interface (110) configured to receive input data (112) derived from one or more sensors, wherein each sensor is configured to measure a physical quantity related to a vehicle (200) or its environment, wherein the input data (112) features a current driving status of the vehicle, a machine learning processor (120) configured to map the input data (112) to an estimated road condition (122), and a validation processor (130) configured to validate the estimated road condition (122) based on a measurement (132) of the road condition obtained at the current driving status of the vehicle. Validation information from the validation processor (130) can be transmitted to a cloud server (170) for further processing and producing validation swarm knowledge for a whole vehicle fleet.