Multi-Camera Light Signal Detection for Vehicle Driving Decisions
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Solution Overview
Problem
Existing autonomous vehicle systems struggle to efficiently detect and interpret light signals from surrounding objects, such as brake lights or emergency signals, using multiple cameras, which is crucial for safe navigation and operation.
Innovation Solution
A method involving multiple cameras on a vehicle processes images using deep neural networks to extract features, fuse these features across views, and perform temporal analysis to determine light signal characteristics, enabling the vehicle to perform driving operations based on these signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple cameras are used to detect light signals from surrounding objects, then the detection accuracy and reliability are improved, but the computational cost and system complexity increase
Solution Approach 1:
The patent segments the light signal detection task into distinct processing stages: individual camera image acquisition, separate feature extraction for each camera, and subsequent fusion of features. This segmentation allows each camera to be processed independently through standardized pipelines, reducing the complexity of managing multiple cameras while maintaining detection reliability through combined information.
Solution Approach 2:
The patent introduces feature extraction and feature fusion as intermediary processing steps between raw camera images and final light signal detection. These intermediaries transform complex multi-camera raw data into structured feature representations, simplifying the subsequent detection task and reducing overall system complexity while preserving detection accuracy.
2Productivity
If deep neural networks are used for feature extraction from multiple cameras, then the feature extraction efficiency is improved, but the computational cost increases
Solution Approach 1:
The patent segments the feature extraction process into separate deep neural network operations for each camera, allowing independent optimization and processing. This enables efficient parallel computation where each network processes one camera's data stream, improving overall extraction efficiency while managing computational load through modular architecture.
Solution Approach 2:
The patent merges the extracted features from multiple cameras into a unified feature representation. This combining step consolidates the computational output from multiple deep neural networks into a single integrated feature set, improving productivity by enabling comprehensive object analysis while managing computational cost through efficient feature fusion algorithms.
3Measurement precision
If temporal analysis is performed on image sequences to determine light signal characteristics, then the detection precision is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction on individual camera images before conducting temporal analysis. By pre-processing and extracting relevant features from each frame in advance, the system prepares structured data that accelerates the subsequent temporal analysis, improving measurement precision while reducing the time required for characteristic determination.
Solution Approach 2:
The patent replaces traditional temporal analysis methods with deep neural network-based feature extraction and fusion. This substitution uses learned patterns from training data to rapidly identify light signal characteristics from temporal sequences, improving precision through advanced pattern recognition while reducing processing time compared to conventional analytical methods.
Data Source
AI summary
Techniques are described for performing image processing on images of cameras located on or in a vehicle. An example technique includes receiving a first set of images obtained by a first camera and a second set of images obtained by a second camera; determining, for each image in the first set, a first set of features of a first object; determining, for each image in the second set, a second set of features of a second object; obtaining a third set of features of an object by combining the first set of features and the second set of features; obtaining a fourth set of features of the object by including one or more features of a light signal of the object; determining characteristic(s) indicated by the light signal; and causing a vehicle to perform a driving related operation based on the characteristic(s) of the object.