Autonomous Vehicle Event Labeling via Machine Learning
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Solution Overview
Problem
The generation of labelled datasets for machine learning models used in autonomous driving vehicles is time-intensive, expensive, and often results in lower quality training datasets, leading to suboptimal software models that can have undesirable consequences for autonomous vehicle operations.
Innovation Solution
A method for automatically labelling data processing events in autonomous vehicles using a processor that identifies events within a time window, extracts attributes, and updates software models by generating labels for previously unidentified or misidentified events, allowing for improved detection of future events without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labelling is used to create training datasets, then quality control is possible, but time consumption and cost increase significantly
Solution Approach 1:
The system enables self-service by having the autonomous vehicle itself label its own sensor data. The processor automatically identifies events, extracts attributes, generates labels, and updates the software model without requiring external human annotators. This transforms the manual labelling process into an autonomous one, where the vehicle uses its own sensor data and processing capabilities to create training datasets for improving its navigation system.
Solution Approach 2:
The system implements feedback by continuously using the generated labels to update the software model, which then improves event detection accuracy. The labels created from sensor data are fed back into the training process, allowing the model to learn from real-world events and progressively improve its performance. This closed-loop feedback mechanism enables continuous improvement of the navigation system using actual operational data.
2Measurement precision
If manual labelling is used to create training datasets, then accuracy can be controlled, but cost increases significantly
Solution Approach 1:
The system eliminates the need for expensive human annotators by enabling the autonomous vehicle to label its own data. The processor automatically identifies events, extracts attributes, generates labels, and updates the software model without requiring external human resources. This transforms the manual labelling process into an autonomous one, significantly reducing operational costs while maintaining labeling quality through systematic automated processing.
Solution Approach 2:
The system replaces the mechanical process of manual human labelling with an automated computational system. Instead of human annotators using cognitive processing to label data, the system uses algorithmic event detection, attribute extraction, and label generation mechanisms. This substitution of mechanical human labor with automated computational processes dramatically reduces costs while maintaining consistency and accuracy through programmable logic.
3Productivity
If existing software models are used for event detection, then immediate operation is possible, but detection accuracy is insufficient
Solution Approach 1:
The system performs preliminary action by continuously generating labels and updating the software model in advance of critical navigation decisions. The processor identifies events, extracts attributes, generates labels, and updates the model continuously during operation, so that when navigation decisions need to be made, the model is already optimized with the latest labeled data. This enables the system to improve detection accuracy without delaying immediate operational responses.
Solution Approach 2:
The system implements feedback by continuously using the generated labels to update the software model, which then improves event detection accuracy. The labels created from sensor data are fed back into the training process, allowing the model to learn from real-world events and progressively improve its performance. This closed-loop feedback mechanism enables continuous improvement of the navigation system using actual operational data.
Data Source
AI summary
In some embodiments, a method comprises receiving, at a processor of an autonomous vehicle and from at least one sensor, sensor data distributed within a time window. A first event being a first event type occurring at a first time in the time window is identified by the processor using a software model based on the sensor data. At least one first attribute associated with the first event is extracted by the processor. A second event being the first event type occurring at a second time in the time window is identified by the processor based on the at least one first attribute. In response to determining that the second event is not yet recognized as being the first event type, a first label for the second event is generated by the processor.