Vehicle Object Classification Using Extra-Regional Sensor Context
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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
The system 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
1Measurement precision
If the system 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 discrete region feature vectors that can be selectively processed. Each region's features are extracted independently and then integrated with the object patch features, allowing the system to handle complex environmental context without overwhelming computational resources.
Solution Approach 2:
The system performs preliminary processing of the environmental context by extracting region features from the full sensor data before integrating them with object classification. This preliminary extraction of contextual features prepares the data in advance, enabling efficient integration without increasing real-time processing burden.
2Measurement precision
If the system processes wider environmental context, then object classification accuracy is improved, but processing time increases
Solution Approach 1:
The system extracts only the relevant region features from the complete environmental context that are necessary for accurate object classification. By selectively extracting and integrating only pertinent contextual information rather than processing the entire environment, the system maintains high classification accuracy while minimizing additional processing time.
3Loss of information
If the system uses more computational resources for context processing, then understanding of the vehicle's environment is improved, but resource efficiency deteriorates
Solution Approach 1:
The system applies partial action by processing only the portion of environmental context that is sufficient for accurate object classification. Rather than comprehensively analyzing all environmental factors, the system identifies and processes the critical regional features needed for classification, achieving good environmental understanding without excessive resource consumption.
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.