Edge V2X Navigation for Real-Time Lane-Level Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing navigation systems lack the ability to provide micro-level traffic management due to latency and processing delays in cloud-based systems, making them ineffective for handling local traffic events and intersections.
Innovation Solution
Implementing a distributed system of edge network devices that collect traffic data locally and perform real-time navigation optimizations using V2X communication, reducing latency and providing lane-level and route recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud-based navigation systems are used for macro-level route planning, then route optimization capability is improved, but response time and latency increase
Solution Approach 1:
The navigation system is segmented into two parts: cloud-based macro-level route planning and edge-based micro-level local navigation optimization. This allows each component to handle specific tasks efficiently, with the edge device processing local traffic events in real-time while the cloud handles overall route optimization.
Solution Approach 2:
An edge computing device acts as an intermediary between V2X devices and the cloud-based navigation system. The edge device receives V2X messages from multiple vehicles, processes local traffic information, and provides real-time navigation recommendations without requiring constant cloud communication, thus reducing latency.
2Loss of information
If cloud-based systems process all navigation data, then comprehensive route analysis is achieved, but processing delays occur
Solution Approach 1:
The system implements local quality by enabling the edge device to independently process and analyze local traffic information without requiring all data to be sent to the cloud. The edge device maintains local traffic information and provides real-time recommendations based on local conditions, reducing processing delays while maintaining information completeness.
3Productivity
If existing navigation systems provide macro-level routing, then overall route planning is effective, but micro-level local traffic management is unrealistic
Solution Approach 1:
The navigation system is divided into macro-level cloud-based route planning and micro-level edge-based local traffic management. This segmentation enables the edge device to handle local traffic events, intersections, and real-time recommendations independently, making micro-level management practical and effective.
4Loss of time
If V2X devices communicate with edge network devices locally, then real-time navigation recommendations are provided, but system complexity increases
Solution Approach 1:
The edge computing device serves as an intermediary that simplifies the system architecture by handling local processing and communication. V2X devices communicate with the edge device using standard protocols, and the edge device manages the complexity of local traffic analysis and recommendation generation, keeping the overall system manageable.
Data Source
AI summary
Techniques described herein provide for enhanced ultra-local navigation services for V2X devices (e.g., smartphones incorporating V2X chip sets). The V2X devices can transmit vehicle information to edge network devices (e.g., roadside units). The roadside units can be deployed at intersections or along roads to collect traffic information through various sensor inputs and V2X communications with multiple vehicles. The communication between V2X devices and the edge network devices can be accomplished through wireless communication (e.g., direct PC5 interface or through local Uu interface with edge computing. The edge network devices can perform local route optimization and compute one or more recommendations (e.g., a recommend route, a recommended speed, a recommended lane). The edge network devices can transmit the one or more recommendations via a wireless communication to the V2X devices. The V2X devices can display the recommendations to a user.