Dual-Camera Perception With Distance-Range Models for ADAS
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Solution Overview
Problem
In advanced driver assistance systems (ADAS), multi-scale feature extraction and object identification based on high-resolution images lead to high computing power requirements, making it difficult to deploy perception models in vehicle terminals, and result in low recall rates for distant-range objects.
Innovation Solution
Utilize a combination of wide-angle and narrow-angle cameras to capture images, where the narrow-angle camera supplements the wide-angle camera's field of view, enabling separate perception task models for different distance ranges, thereby improving recall rates for distant-range objects without the need for multi-scale feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-scale feature extraction is performed based on high-resolution images, then object identification accuracy is improved, but computing power requirements increase significantly
Solution Approach 1:
The patent segments the field of view into multiple distance ranges (e.g., near-range, mid-range, far-range) and assigns different perception task models to each range. This segmentation allows the system to process different regions with appropriate computational complexity, avoiding the need for uniform high-computation multi-scale feature extraction across the entire image, thus reducing overall computing power requirements while maintaining identification accuracy.
Solution Approach 2:
The patent applies different perception task models with different computational characteristics to different distance ranges. For example, near-range objects may use simpler models while far-range objects use models optimized for distance. This local quality approach ensures that each region is processed with the appropriate level of computational resources, improving efficiency without sacrificing accuracy.
2Adaptability or versatility
If multi-scale feature extraction is performed based on high-resolution images, then object identification capability is improved, but model deployment in vehicle terminals becomes difficult
Solution Approach 1:
By segmenting the perception task into multiple distance-range-specific models, the patent reduces the computational burden of each individual model. This segmentation enables deployment in vehicle terminals with limited computational resources, as each model can be smaller and more efficient while collectively covering the full range of identification needs.
Solution Approach 2:
The patent changes the parameter of model input resolution and complexity based on distance range. Instead of uniformly applying high-resolution multi-scale feature extraction to all objects, the system adjusts the processing parameters according to the distance range, enabling efficient deployment on terminal devices with varying computational capabilities.
3Measurement precision
If wide-angle camera is used for close-range object perception, then perception precision for close-range objects is improved, but recall rate for distant-range objects decreases
Solution Approach 1:
The patent segments the field of view into different distance ranges and processes each range with specialized perception task models. The wide-angle camera captures the entire scene, but the system segments processing by distance, applying appropriate models to near-range and far-range objects separately. This ensures high precision for close-range objects while maintaining reliable detection of distant objects through range-appropriate processing.
Solution Approach 2:
The patent adds the dimension of distance range as a new processing variable. Instead of treating all objects uniformly, the system introduces distance range as an additional dimension for model selection and processing, allowing simultaneous optimization for both close-range precision and far-range recall through multi-dimensional processing strategies.
Data Source
AI summary
Disclosed in embodiments of this disclosure are a method for determining a perception result, a medium, and a device. The method includes: determining a first image captured by a wide-angle camera and a second image captured by a narrow-angle camera, where a field of view (FOV) of the narrow-angle camera is smaller than a FOV of the wide-angle camera; determining, based on the first image, the second image, and perception task models corresponding to distance ranges, first perception results corresponding to distance ranges; and determining an object perception result based on the first perception results corresponding to distance ranges.