Machine Learning Training With Reliability-Weighted Sensor Labels
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Solution Overview
Problem
Training artificial neural networks for tasks like classification or object detection requires large sets of manually labeled data, which is costly and time-consuming, especially when using sensors like radar and lidar that have inter-sensor perception discrepancies.
Innovation Solution
A method that uses an optimization criterion based on a mapping and energy map to filter out approximations of lower quality ground truths, incorporating a reliability map to weight the training process, thereby reducing the impact of imperfect sensor data from radar and lidar.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually labeled data is used for training, then training accuracy is improved, but training cost and time consumption increase
Solution Approach 1:
The system performs self-labeling by using the neural network to automatically generate labels from sensor data without human intervention. The network processes sensor inputs and produces corresponding labels autonomously, eliminating the need for manual annotation while maintaining training quality.
Solution Approach 2:
The system creates synthetic labeled data by copying and transforming sensor data through the neural network processing pipeline. Instead of manually creating labels, the system generates them by passing sensor data through the network architecture, effectively copying the labeling process from manual to automated execution.
2Productivity
If automated labeling is used, then training efficiency is improved, but data quality and reliability deteriorate
Solution Approach 1:
The system implements feedback mechanisms where the neural network's predictions are continuously refined based on performance metrics. The network processes sensor data, generates labels, evaluates its own performance, and adjusts its parameters accordingly, creating a closed-loop system that improves data quality over time through self-correction.
Solution Approach 2:
The system dynamically adjusts the training process by modifying network architecture, learning rates, and processing parameters based on real-time performance feedback. The labeling quality is not static but evolves as the network learns and adapts, allowing the system to maintain high productivity while progressively improving data reliability.
3Quantity of substance
If all sensor data is used for training, then data quantity is improved, but negative influence from low quality data increases
Solution Approach 1:
The system applies different quality standards and processing methods to different regions or types of sensor data. Instead of treating all data uniformly, the network identifies and processes high-quality and low-quality data regions differently, applying appropriate weighting or filtering to each local region based on its characteristics and reliability.
Solution Approach 2:
The system changes parameters such as data weighting factors, threshold values, and processing intensity based on the quality assessment of different data regions. By dynamically adjusting these parameters, the system can include more data in training while controlling the negative influence of low-quality portions through parameter-based modulation of their impact.
Data Source
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AI summary
A computer implemented method for training a machine learning method comprises the following steps carried out by computer hardware components: determining measurement data from a first sensor; determining approximations of ground truths based on a second sensor; and training the machine learning method based on the measurement data and the approximations of ground truths; wherein approximations of ground truths of lower approximation quality have a lower effect on the training than approximations of ground truths of higher approximation quality.