V2X RF-Optical Association for Accurate Road Entity Identification
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Solution Overview
Problem
Existing vehicle-to-everything (V2X) systems face challenges in accurately associating camera-captured objects with RF messages due to factors like lighting conditions, location accuracy, and partial visibility.
Innovation Solution
The system employs optical signaling by modulating vehicle lights with signal information, which is then captured by cameras and fused with RF data to create a combined representation of road entities, enhancing the accuracy of object association.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical signaling is used to associate camera objects with V2V RF messages, then the accuracy of object association is improved, but the device complexity increases due to integration of camera, transceiver, and controller systems
Solution Approach 1:
The patent merges multiple sensing systems (camera, transceiver, controllers) into an integrated V2X communication system. The controllers receive both RF V2X messages and optical data from the camera, then fuse these data streams to associate camera-captured objects with RF messages. This combining of multiple data sources resolves the technical contradiction by achieving high association accuracy through integrated processing.
Solution Approach 2:
The controller acts as an intermediary that receives and processes both RF V2X messages and optical camera data. It performs data fusion by comparing location information from RF messages with object positions detected in camera images, thereby mediating between the two different data sources to achieve accurate object association while managing system complexity through centralized processing.
2Reliability
If RF messages alone are used for vehicle communication, then the system simplicity is maintained, but the reliability of situational awareness deteriorates due to lighting conditions and location accuracy issues
Solution Approach 1:
The patent changes the parameter space by adding optical sensing capabilities to complement RF communication. The camera captures visual data that provides additional parameters (object appearance, precise location in image space) beyond what RF messages alone can provide. This parameter enrichment improves reliability by providing multiple independent sources of information about road entities.
Solution Approach 2:
The camera system serves multiple functions: it captures images for visual recognition, detects object locations for association with RF messages, and provides redundancy when RF signals are insufficient. This multi-functionality approach improves situational awareness reliability without requiring completely separate systems, as the camera integrates with the existing V2X infrastructure.
3Measurement precision
If data fusion of RF and visual data is performed, then the precision of road entity identification is improved, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary processing by having the camera continuously capture images and the transceiver continuously receive RF messages in parallel. The controllers pre-process this data by extracting relevant features (object locations from images, position information from RF messages) before association is needed. This preliminary action reduces the computational burden during actual association operations, minimizing processing time while maintaining high precision.
Solution Approach 2:
The data fusion process is segmented into distinct processing stages: RF message reception and parsing, camera image processing and object detection, data association matching, and result integration. By dividing the complex fusion task into smaller sequential segments, the system can process each aspect independently and efficiently, reducing overall processing time while achieving accurate road entity identification through the combination of all segments.
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 approach improves the accuracy and reliability of associating visual and RF data, reducing errors caused by lighting conditions and location inaccuracies, and enabling more precise situational awareness in V2X systems.
Implementation Method 1
optical signaling by modulating vehicle lights with signal information, which is then captured by cameras
Data Source
AI summary
Data fusion of radio frequency (RF) and visual data is provided. Via a transceiver of a vehicle, a vehicle-to-everything (V2X) message is received from a remote road entity, the V2X message including one or more V2X data elements descriptive of the remote road entity. Using a camera of the vehicle, modulated light data is captured within a sensing area surrounding an ego road entity, the modulated light data being sent from the remote road entity. A modulated light transmission in the modulated light data is decoded to identify one or more light data elements in the modulated light data descriptive of the road entity. An association of the one or more V2X data elements is performed with the one or more light data elements to identify associated V2X camera objects where the V2X message and the modulated light data are fused into a combined representation of the remote road entity.