Panoptic Segmentation Label Generation for Autonomous Vehicles
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Solution Overview
Problem
Existing techniques for panoptic segmentation labeling in autonomous vehicles face challenges in generating consistent labels across time and multiple camera views, leading to inaccuracies in object tracking and segmentation, due to the lack of efficient and large-scale manually labeled data, especially for video frames captured by multiple sensors.
Innovation Solution
A system that processes sensor data from multiple camera views to generate panoramic views, using 3D bounding box annotations and panoptic segmentation neural networks like Panoptic-DeepLab, to ensure consistent object instance identifiers across time and camera views, thereby improving training efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of sensor data is performed to generate panoptic segmentation labels, then labeling accuracy can be achieved, but the process is extremely time-consuming and expensive
Solution Approach 1:
The patent uses automatically generated segmentation labels from neural networks as copies to replace manual labeling. The system generates initial segmentation labels using trained neural networks, then uses these automated labels as training data to improve the network, eliminating the need for time-consuming manual labeling while maintaining accuracy through iterative refinement
Solution Approach 2:
The system performs self-labeling by using its own neural network predictions to generate training data. The neural network automatically generates segmentation labels from sensor data, and these self-generated labels are used to retrain and improve the network, creating a self-improving system that doesn't rely on external manual labeling
2Adaptability or versatility
If panoptic segmentation labels are generated for video frames from multiple sensors, then comprehensive scene understanding is achieved, but consistency across time and sensors becomes difficult to maintain
Solution Approach 1:
The patent merges segmentation labels from multiple camera views and time points into a unified panoptic segmentation label. The system processes sensor data from multiple cameras, generates segmentation labels for each view, and combines them while ensuring consistency across different sensors and time points through a unified labeling framework
Solution Approach 2:
The system uses feedback loops to ensure label consistency. Generated segmentation labels are used to retrain the neural network, and the improved network generates more accurate labels. This iterative feedback process maintains consistency across multiple sensors and time points by continuously refining the labeling based on accumulated data
3Measurement precision
If large-scale manually labeled datasets are created for training, then training accuracy improves, but the cost and time requirements increase significantly
Solution Approach 1:
The patent creates training datasets by copying and using automatically generated segmentation labels instead of manually labeled data. The system generates large volumes of training data through automated neural network predictions, then uses these copies as training examples to improve model accuracy without the prohibitive cost of manual labeling
Solution Approach 2:
The system performs preliminary automated labeling of training data before the actual training process. By pre-generating segmentation labels using the neural network, the system prepares large-scale training datasets in advance, enabling efficient training without the need for subsequent manual labeling work
Data Source
AI summary
Methods, systems, and apparatus for generating a panoptic segmentation label for a sensor data sample. In one aspect, a system comprises one or more computers configured to obtain a sensor data sample characterizing a scene in an environment. The one or more computers obtain a 3D bounding box annotation at each time point for a point cloud characterizing the scene at the time point. The one or more computers obtain, for each camera image and each time point, annotation data identifying object instances depicted in the camera image, and the one or more computers generate a panoptic segmentation label for the sensor data sample characterizing the scene in the environment.