Cloud Radar Network Fusing for Urban Line-of-Sight Obstruction
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Solution Overview
Problem
Existing automotive radar systems are limited by line-of-sight constraints, particularly in urban areas where buildings obstruct visibility, and struggle to provide accurate information in dense environments without individual sensing capabilities.
Innovation Solution
A cloud-based system that collects and fuses data from multiple sensors, including radars, cameras, and LiDARs, installed on moving platforms to generate a high-resolution, enriched global map, overcoming line-of-sight limitations and enhancing sensing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected and processed in real-time from multiple moving platforms, then measurement precision and detection accuracy are improved, but device complexity and computational requirements increase
Solution Approach 1:
A cloud-based processing platform serves as an intermediary between multiple radar sensors and the final detection output. The platform receives raw data from distributed sensors, performs centralized joint processing to improve detection accuracy, and returns results to individual vehicles. This mediator approach enables complex computations to be distributed rather than requiring each vehicle to have high-end processing capabilities.
Solution Approach 2:
The patent combines data from multiple radar sensors mounted on different moving platforms into a unified detection system. By merging sensor inputs and performing joint processing in the cloud, the system achieves improved measurement precision and detection accuracy that would be difficult for individual sensors to achieve alone, while distributing computational complexity across the network.
2Reliability
If sensors are distributed across multiple moving platforms to overcome line-of-sight limitations, then reliability and coverage are improved, but coordination difficulty and data synchronization challenges increase
Solution Approach 1:
The cloud-based platform implements feedback mechanisms to coordinate sensors across multiple moving platforms. The system continuously receives data from distributed sensors, processes it centrally, and provides feedback to individual vehicles about detected objects and their reliability. This feedback loop enables the system to maintain high detection reliability by compensating for individual sensor limitations through networked coordination.
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
The system improves traffic safety by providing enhanced situational awareness, improving detection accuracy and resolution, and allowing vehicles to autonomously navigate, even in dense urban environments, while reducing mutual interference and cyber-attack vulnerabilities.
Implementation Method 1
radar signals that are reflected from the scanned objects may provide the missing information
Implementation Method 2
data containing detection maps from sensors (such as radars, cameras, LiDARs)
Implementation Method 3
sensors (such as radars, cameras, LiDARs) installed on a plurality of moving platforms
Data Source
AI summary
A system for generating and providing an enriched global map to subscribed moving platforms (such as vehicles, bikes, drones, scooters or pedestrians), comprising a plurality of sensors installed on a plurality of moving platforms (such as vehicles) in a given area, where each sensor views a target of an object of interest from a different angle; a data network for collecting data containing detection maps from the sensors; a central processor connected to the data network, which is adapted to generate an enriched and complete high-resolution global map of the given area by jointly processing and fusing the collected data; unify the detection capabilities of the moving platforms; transmit, over the data network, the complete high-resolution global map to at least one moving platform.