Object Classification Using Environmental Context Feature Vectors
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately classifying objects in their environment using neural networks, particularly due to limited computational resources and the need for efficient processing of sensor data to make timely driving decisions.
Innovation Solution
A system that processes both narrowly focused sensor data patches and wider environmental context using a convolutional neural network and an object classifier neural network, generating feature vectors to improve object classification accuracy without increasing prediction time or resource usage, by leveraging context information to classify multiple objects efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the neural network processes only narrowly focused sensor data patches, then computational resources and processing time are reduced, but object classification accuracy deteriorates due to lack of environmental context
Solution Approach 1:
The system segments the environmental context into multiple regions and generates separate feature vectors for each region. The convolutional neural network divides the wide-field sensor data into spatial regions, creating a structured representation that can be selectively processed. This segmentation allows efficient handling of large context data while maintaining comprehensive environmental awareness for accurate object classification.
2Measurement precision
If the neural network processes wide environmental context data, then object classification accuracy is improved, but computational resource usage and processing time increase
Solution Approach 1:
The system performs preliminary processing of the wide environmental context data through the convolutional neural network to generate a set of region-based feature vectors before the final classification stage. This preliminary action pre-computes and organizes contextual information in advance, making it readily available for efficient integration during object classification without adding significant computational burden during real-time operation.
Solution Approach 2:
The system transforms the spatial dimension of wide-field sensor data into a multi-dimensional feature vector space through the convolutional neural network. By converting spatial context into a structured feature representation with multiple dimensions (one for each region), the system enables efficient mathematical operations and integration with object-specific features while preserving comprehensive environmental context for accurate classification.
Data Source
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.