Object Localization Using Convolutional Neural Networks
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Solution Overview
Problem
Existing autonomous vehicle systems face challenges in accurately localizing objects relative to the vehicle, often requiring laborious hand-crafted feature engineering and consuming significant computational resources, which can limit their accuracy and efficiency in making autonomous driving decisions.
Innovation Solution
A system utilizing convolutional neural networks and localization neural networks processes sensor data from various sensors to generate feature representations and object localization data, allowing for implicit recognition of object-specific and contextual features without explicit programming, and can be co-trained to generate auxiliary outputs for improved performance with fewer training iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hand-crafted feature engineering is used for object localization, then the system can process sensor data, but the computational resources are significantly consumed and accuracy is limited
Solution Approach 1:
The patent replaces traditional hand-crafted feature engineering (mechanical processing approach) with deep learning-based automatic feature extraction. Convolutional neural networks automatically learn relevant features from raw sensor data, eliminating the need for manual feature design and reducing computational overhead while improving localization accuracy.
Solution Approach 2:
The system enables the neural network to automatically extract and learn object features directly from sensor data without human intervention in feature engineering. The model self-optimizes feature representation through training, reducing the need for laborious manual feature specification while improving adaptability and accuracy.
2Measurement precision
If hand-crafted feature engineering is used for object localization, then the system can process sensor data, but the feature engineering process is laborious and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of hand-crafted feature engineering with automated deep learning feature extraction. The convolutional neural network automatically learns relevant features from raw sensor data, eliminating the time-consuming manual feature design process while improving localization accuracy through data-driven feature representation.
Solution Approach 2:
The system performs feature learning in advance during the training phase, where the neural network automatically discovers and encodes relevant object features from training data. This preliminary automatic feature extraction eliminates the need for time-consuming manual feature engineering during deployment, improving both efficiency and accuracy.
3Extent of automation
If conventional object localization systems are deployed, then they can make autonomous driving decisions, but the system complexity and computational requirements are high
Solution Approach 1:
The patent merges multiple processing stages (feature extraction, object detection, and localization) into a unified deep learning framework. The convolutional neural network performs both feature extraction and localization in an integrated manner, reducing system complexity while maintaining autonomous driving decision-making capability.
Solution Approach 2:
The neural network model serves multiple functions simultaneously: it extracts features, detects objects, and localizes them within a single unified framework. This multi-functional approach reduces the number of separate system components needed, simplifying the overall system architecture while preserving autonomous driving capabilities.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a location of a particular object relative to a vehicle. In one aspect, a method includes obtaining sensor data captured by one or more sensors of a vehicle. The sensor data is processed by a convolutional neural network to generate a sensor feature representation of the sensor data. Data is obtained which defines a particular spatial region in the sensor data that has been classified as including sensor data that characterizes the particular object. An object feature representation of the particular object is generated from a portion of the sensor feature representation corresponding to the particular spatial region. The object feature representation of the particular object is processed using a localization neural network to generate the location of the particular object relative to the vehicle.