Object Classification Using Extra-Regional Context Embeddings
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately classifying nearby objects using neural networks due to limited computational resources and the need for efficient processing of both narrowly focused and wider environmental context data.
Innovation Solution
A system that processes both narrowly focused sensor data patches and wider environmental feature vectors using a convolutional neural network to generate accurate object classifications, incorporating a context embedding neural network to provide additional environmental context without increasing computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system processes both narrowly focused sensor data patches and wider environmental context data, then object classification accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system segments the environmental data processing into two distinct components: (1) narrowly focused sensor data patches that capture detailed local object features, and (2) wider environmental context data that provides global scene understanding. This segmentation allows the neural network to process different types of data separately and combine them effectively, improving classification accuracy while managing computational resources by dividing the processing task into manageable segments with different computational requirements.
2Loss of information
If the system processes wider environmental context data, then understanding of surroundings is enhanced, but processing time increases
Solution Approach 1:
The system performs preliminary processing of the wider environmental context data to generate a compressed contextual representation before the main classification task. This preliminary action includes downsampling, feature extraction, and creating a condensed context vector that captures essential environmental information. By performing this compression and feature extraction in advance, the system reduces the time required for the main classification process while still preserving the essential contextual understanding needed for accurate object classification.
3Reliability
If the system uses both focused and contextual data, then classification reliability is improved, but device complexity increases
Solution Approach 1:
The system introduces a contextual representation layer that acts as an intermediary between the focused sensor data patches and the classification output. This intermediary component processes the wider environmental context data and generates a condensed contextual vector that can be seamlessly integrated with the focused object data. This mediator layer simplifies the overall system architecture by providing a standardized interface for combining different data types, reducing the complexity that would otherwise arise from directly integrating multiple data sources with different formats and dimensions.
Data Source
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AI summary
Some aspects of the subject matter disclosed herein include a system implemented on one or more data processing apparatuses. The system can include an interface configured to obtain, from one or more sensor subsystems, sensor data describing an environment of a vehicle, and to generate, using the sensor data, (i) one or more first neural network inputs representing sensor measurements for a particular object in the environment and (ii) a second neural network input representing sensor measurements for at least a portion of the environment that encompasses the particular object and additional portions of the environment that are not represented by the one or more first neural network inputs; and a convolutional neural network configured to process the second neural network input to generate an output, the output including a plurality of feature vectors that each correspond to a different one a plurality of regions of the environment.