Intelligent Roadside Unit Sensor Fusion Architecture
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Solution Overview
Problem
The high cost and computational requirements of intelligent roadside units due to the need for advanced sensing capabilities and data processing hinder their widespread adoption in automatic drive systems.
Innovation Solution
An intelligent roadside unit comprising a radar, camera, radar-signal processor, image-signal processor, and general control processor, which distribute computation power across multiple processors to reduce processing capacity needs and costs, while enhancing sensing capabilities through obstacle detection and image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If various sensing detectors are disposed on the intelligent roadside unit to improve sensing capability, then the sensing capability is improved, but the device complexity and cost increase
Solution Approach 1:
The patent combines radar and camera systems into a single intelligent roadside unit, merging multiple sensing detectors into one integrated device. This consolidation improves sensing capability while managing device complexity through unified architecture rather than separate distributed sensors.
Solution Approach 2:
The intelligent roadside unit is designed as a multi-functional device that performs both radar-based obstacle detection and camera-based image capture simultaneously. This universal design allows a single device to fulfill multiple sensing functions, reducing the need for separate specialized detectors.
2Reliability
If advanced sensing detectors are disposed to improve active sensing, then the sensing capability is improved, but the cost of hardware increases
Solution Approach 1:
By merging radar and camera systems into one unit, the patent achieves advanced sensing capability without proportionally increasing cost. The shared infrastructure and integrated design reduce overall hardware costs compared to deploying separate advanced sensing systems.
Solution Approach 2:
The intelligent roadside unit processes its own sensing data through integrated signal processors, reducing the need for external high-cost processing equipment. The system serves itself by performing data fusion and processing within the same unit that collects the data.
3Productivity
If high computing hardware is used to process large amount of data, then the data processing capability is improved, but the cost of hardware increases
Solution Approach 1:
The radar-signal processor and image-signal processor are integrated within the intelligent roadside unit, allowing the system to process its own data independently. This self-service approach to data processing eliminates the need for expensive external computing hardware while maintaining high processing capability.
Solution Approach 2:
The patent segments data processing into specialized processors: a radar-signal processor for radar data and an image-signal processor for camera data. This segmentation allows each processor to be optimized for its specific task, improving overall data processing capability while keeping individual processor costs manageable.
4Productivity
If multiple processors are used to process radar and image signals, then the data processing capability is improved, but the device complexity increases
Solution Approach 1:
The patent divides data processing into specialized segments: a radar-signal processor dedicated to radar data and an image-signal processor dedicated to camera data. This functional segmentation improves processing capability by optimizing each processor for its specific task while managing complexity through clear separation of responsibilities.
Solution Approach 2:
While using multiple processors, the patent merges them into a unified intelligent roadside unit with integrated architecture. The processors work together as a coordinated system, sharing common control and output mechanisms, which manages overall device complexity despite having multiple processing components.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This configuration lowers the costs and processing power requirements of intelligent roadside units, improves sensing reliability, and facilitates the generation of point cloud images for enhanced obstacle detection and vehicle control, thereby supporting the development of autonomous driving technologies.
Implementation Method 1
a radar, configured to detect an obstacle within a first preset range of the intelligent roadside unit
Implementation Method 2
at least one camera, configured to capture an image within a second preset range of the intelligent roadside unit
Data Source
AI summary
The present disclosure provides an intelligent roadside unit. The intelligent roadside unit includes: a radar, configured to detect an obstacle within a first preset range of the intelligent roadside unit; a camera, configured to capture an image within a second preset range of the intelligent roadside unit; a radar-signal processor, coupled to the radar and configured to generate an obstacle detection signal according to obstacle information detected by the radar; an image-signal processor, coupled to the camera and configured to generate an image detection signal according to the image captured by the camera; and a general control processor, coupled to the radar-signal processor and the image-signal processor and generating a point cloud image according to the obstacle detection signal and the image detection signal.