Vehicle Object Classification Using Uncertainty-Based Active Learning
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Solution Overview
Problem
The high cost and inefficiency of manually labeling large datasets for training machine learning algorithms in vehicles for partial driving automation, and the need for accurate object classification in automotive perception tasks.
Innovation Solution
An active learning method using uncertainty-based criteria to select patches of automotive sensor data for annotation by an oracle, considering both uncertainty thresholds and patch distance measures to improve object classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large datasets are manually labeled to train machine learning algorithms, then object classification accuracy is improved, but cost and time consumption increase significantly
Solution Approach 1:
The system performs self-labeling by using the machine learning algorithm to predict object classes for unlabeled data points, then using uncertainty-based active learning to selectively query an oracle only for ambiguous cases. This self-service approach automates the majority of labeling while minimizing manual intervention.
Solution Approach 2:
Instead of fully manually labeling all data points, the system applies partial manual labeling only to the most uncertain cases identified by the active learning criteria. The majority of data points are labeled automatically through the machine learning model's predictions, reducing overall labeling effort while maintaining accuracy.
2Measurement precision
If all uncertain data points are sent for annotation, then classification accuracy improves, but data traffic and processing overhead increase
Solution Approach 1:
The system applies different processing qualities to different data points based on their uncertainty levels. High-uncertainty data points undergo rigorous oracle annotation, while low-uncertainty points are processed automatically. The patch distance measure further refines this by identifying representative patches that capture local variations in the data space.
Solution Approach 2:
The system extracts and isolates only the most critical uncertain data points (patches) for oracle annotation, separating them from the bulk of confidently classified data. This extraction is guided by uncertainty thresholds and patch distance measures, ensuring that only necessary data points consume annotation resources.
3Reliability
If manual labeling is performed to ensure accuracy for driving automation, then safety is improved, but cost increases
Solution Approach 1:
The system implements a feedback loop where the machine learning algorithm continuously improves by learning from oracle annotations of uncertain cases. The uncertainty estimates guide which data points to query, and the annotations feed back into retraining the model, creating a self-improving system that enhances safety over time with minimal annotation cost.
Solution Approach 2:
The system performs preliminary automatic classification for all data points before submitting to oracle. This preliminary action filters out confidently classified samples, ensuring that expensive oracle resources are only used when absolutely necessary for safety-critical decisions, thereby reducing overall cost while maintaining reliability.
Data Source
AI summary
The present disclosure relates to enabling active learning for object classification in visual perception tasks in a vehicle. To this end, an object class out of a plurality of object classes is determined for one or more data points within automotive sensor data. Further, an uncertainty value for each of the one or more data points within the automotive sensor data indicative of an uncertainty of the object class determination and one or more object instances within the automotive sensor data are determined. Then, a patch for each data point having a corresponding uncertainty value exceeding an uncertainty threshold and a patch distance measure for each patch is determined. Each patch having a patch distance measure exceeding a patch distance measure threshold is provided to an oracle. Finally, an object annotation of each patch is received from the oracle.