Trainable Evaluation System for Multi-Sensor Data Generalization
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Solution Overview
Problem
Existing trainable evaluation systems for vehicles struggle with obsolescence when sensor technologies change, as they require retraining with new sensor data, which is costly and difficult due to the limited availability of labeled training data and rapid sensor innovation cycles.
Innovation Solution
A trainable evaluation system with multiple independent input stages and a processing stage that learns to generalize across different sensor modalities, allowing for the reuse of training data across various sensors and enabling efficient adaptation to new camera models by concentrating content evaluation in the processing stage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the evaluation system uses a single integrated input stage for all sensors, then the system structure is simpler, but the system cannot effectively generalize across different sensor modalities and requires retraining when sensors change
Solution Approach 1:
The patent divides the evaluation system into multiple independent input stages, each dedicated to processing data from a specific sensor modality (e.g., camera, radar, LIDAR). This segmentation allows each input stage to learn modality-specific features while the shared processing stage learns generalizable patterns, resolving the contradiction between structural simplicity and adaptability across different sensors.
Solution Approach 2:
The patent creates a shared processing stage that receives inputs from multiple specialized input stages and processes them uniformly. This universal processing stage learns generalizable representations that work across different sensor modalities, enabling the system to adapt to new sensors without complete retraining while maintaining a relatively simple overall structure.
2Measurement precision
If the evaluation system is trained with data from a specific sensor model, then the training achieves high precision for that sensor, but the training becomes obsolete when the sensor is upgraded to a successor model
Solution Approach 1:
By separating sensor-specific processing (in individual input stages) from general processing (in the shared processing stage), the patent enables the general processing stage to learn robust, sensor-agnostic patterns. This allows high precision for specific sensor models while maintaining adaptability to successor models, as the general patterns learned transfer across sensor generations.
Solution Approach 2:
The patent allows the system to adapt to new sensor models by changing only the parameters in the new input stage corresponding to the successor sensor, while retaining the shared processing stage parameters learned from previous sensors. This selective parameter adaptation preserves measurement precision for the new sensor while avoiding complete retraining.
3Reliability
If labeled training data is collected extensively for retraining, then the evaluation system can be updated for new sensors, but the cost and time required increase significantly
Solution Approach 1:
The segmented architecture allows retraining to be performed independently for each new sensor modality in its dedicated input stage, rather than requiring complete system retraining. This significantly reduces the time and labeled data needed for updates, as only the relevant input stage parameters need adjustment while the shared processing stage parameters remain valid.
Solution Approach 2:
The shared processing stage serves multiple sensor modalities simultaneously, meaning that training data from one sensor type contributes to the generalizable knowledge that benefits all sensor types. This universal learning approach reduces the total amount of labeled training data needed across all sensor generations compared to training separate systems for each sensor.
Data Source
AI summary
An evaluation system for processing measured data which include physical measured data detected with the aid of one or multiple sensors, and/or realistic synthetic measured data of the sensor(s), into one or multiple evaluation results. The system includes at least two input stages independent from each other, which are designed to receive measured data and process these measured data into precursors. At least one processing stage, receives the precursors from all input stages as inputs and is designed to process one or multiple input precursor(s) into a shared intermediate product. At least one output stage, which is designed to process the intermediate product into one or multiple evaluation result(s) of the evaluation system. A method for training the evaluation system. A method for operating the evaluation system is also provided.