Adaptive-Directional Transformer for Radar Semantic Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing radar-based semantic segmentation models face challenges due to the noisy and sparse nature of radar data, as well as the high imbalance between foreground and background, which limits their ability to capture global information effectively.
Innovation Solution
The proposed TransRadar system employs an adaptive-directional transformer architecture that processes Angle-Doppler (AD), Range-Doppler (RD), and Range-Angle (RA) feature maps from radar signals. It incorporates an adaptive-directional attention block and a tailored loss function to address the sparse and imbalanced nature of radar data, enabling efficient multi-view processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing radar-based semantic segmentation models are used, then the model structure is relatively simple, but the ability to capture global information is limited due to the noisy and sparse nature of radar data
Solution Approach 1:
The model segments radar data processing into multiple views (Range-Angle, Range-Doppler, Angle-Doppler) and processes each view separately through dedicated encoder-decoder pathways, then fuses the results. This segmentation allows the model to capture global information from different dimensional perspectives while managing complexity through modular architecture.
Solution Approach 2:
The model transforms the radar data processing from a single-view approach to a multi-view approach by creating three different dimensional representations (RA, RD, AD views) of the same radar data. This dimensional transformation enables comprehensive global information capture by analyzing the data from multiple geometric perspectives simultaneously.
2Loss of information
If the model processes all radar data densely, then global information is captured, but computational power requirements increase significantly
Solution Approach 1:
The model segments the computational workload across three separate encoder-decoder pathways, each handling a specific view. This segmentation distributes the computational burden and allows for more efficient processing compared to a single dense processing approach, as each pathway can be optimized independently and processes only relevant features for its specific view.
Solution Approach 2:
The model applies partial processing by focusing computational resources on the most informative features within each view rather than processing all data points with equal intensity. The attention mechanisms selectively focus on relevant regions, performing partial actions that capture essential global information while reducing overall computational requirements.
3Measurement precision
If the model size is increased to improve segmentation performance, then mIoU and mDice scores improve, but the model becomes less suitable for vehicle computing systems with power and space limitations
Solution Approach 1:
The model segments the architecture into three specialized encoder-decoder pathways that process different views in parallel. This segmentation achieves high segmentation accuracy through multi-view fusion while maintaining computational efficiency, as each pathway processes information independently and can be optimized for the specific characteristics of its view, reducing the need for an overall larger model.
Solution Approach 2:
The model employs universal encoder and decoder components that are reused across all three views, with each view sharing the same architectural blueprint. This multi-functionality allows the model to achieve high accuracy through versatile components that can handle different types of radar data, reducing the need for view-specific large models and thereby lowering overall power consumption.
Data Source
AI summary
An automotive control system and method, includes a radar sensor, attached to a vehicle body panel, for receiving radar signals having a frequency, and processing circuitry configured with neural network encoders for encoding multiple frames of Angle-Doppler (AD), Range-Doppler (RD), and Range-Angle (RA) feature maps from the radar signals, an adaptive-directional attention block to sample rows and columns and apply self attention after each sampling instance, and a RD decoder and a RA decoder that generate RD and RA probability maps. Each map is a colorized feature map, with each pixel color representing a predicted class label for objects. An object detection component identifies the objects, and an object distance analysis component predicts a distance to the identified objects. An object velocity component predicts a velocity of the identified objects.