Multi-Frame Signal Classification Using Spatio-Temporal CNNs
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Solution Overview
Problem
Advanced driver assistance systems (ADAS) and autonomous vehicles face challenges in accurately identifying and classifying multi-frame semantic signals, such as traffic lights and emergency vehicle lights, relying on subjective human determinations rather than automated systems.
Innovation Solution
The implementation of a convolutional neural network (CNN) based system that processes sequential images to identify and classify multi-frame semantic signals by converting them into temporal images, maintaining spatio-temporal structure, thereby reducing computational and memory costs, and enabling automated vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a CNN-based system processes sequential images to identify multi-frame semantic signals, then identification accuracy is improved, but computational cost and memory requirements increase
Solution Approach 1:
The patent segments the sequential image processing into distinct functional modules: a spatio-temporal feature extractor that processes spatial features across frames, a temporal feature extractor that analyzes temporal patterns, and a classifier. This segmentation allows each module to be optimized independently, reducing overall computational cost while maintaining identification accuracy.
Solution Approach 2:
The patent transforms multi-frame sequential data into a spatio-temporal representation that adds a temporal dimension to the standard spatial image processing. By converting sequential frames into a spatio-temporal volume and applying 3D convolutional operations, the system efficiently captures both spatial and temporal features without requiring separate processing pipelines, thereby reducing computational overhead.
2Measurement precision
If a CNN-based system processes sequential images to identify multi-frame semantic signals, then identification accuracy is improved, but memory requirements increase
Solution Approach 1:
The patent extracts and processes only the essential spatio-temporal features from the sequential images rather than storing and processing all raw pixel data. By extracting key features at each frame and maintaining only necessary temporal relationships, the system achieves accurate signal identification while significantly reducing memory requirements compared to storing complete high-resolution sequential frames.
3Extent of automation
If automated CNN-based classification is implemented, then extent of automation is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal spatio-temporal CNN architecture that can classify multiple types of semantic signals (traffic lights, emergency vehicle lights, construction lights, etc.) using the same core processing pipeline. This multi-functional approach achieves high extent of automation across diverse signal types without proportionally increasing system complexity, as the same feature extraction and classification mechanisms handle various signal categories.
Data Source
AI summary
The present subject matter provides various technical solutions to technical problems facing advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems. In particular, disclosed embodiments provide systems and methods that may use cameras and other sensors to detect objects and events and identify them as predefined signal classifiers, such as detecting and identifying a red stoplight. These signal classifiers are used within ADAS and AV systems to control the vehicle or alert a vehicle operator based on the type of signal. These ADAS and AV systems may provide full vehicle operation without requiring human input. The embodiments disclosed herein provide systems and methods that can be used as part of or in combination with ADAS and AV systems.