Connectivity Augmented Mapping for Vehicle Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in ensuring reliable connectivity due to varying network technologies, deployment coverage, terrain, and time-dependent factors, which affects their ability to provide consistent infotainment services and route planning.
Innovation Solution
A system where vehicles sense connectivity Key Performance Indicators (KPIs) such as throughput and latency, upload this data to a server, and use it to build a connectivity-augmented map, allowing vehicles to plan routes that meet the connectivity needs of occupants by filtering data based on modem capabilities and route criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicles collect and use KPI data from multiple sources to improve route planning, then connectivity quality for infotainment services is improved, but data processing complexity and time increase
Solution Approach 1:
The system pre-collects and stores KPI data from multiple vehicles in a database before route planning is needed. This preliminary data collection and organization reduces the complexity of real-time data processing while maintaining reliable connectivity information for infotainment services.
Solution Approach 2:
A centralized server acts as an intermediary between multiple vehicles and the route planning system. The server aggregates, validates, and processes KPI data from numerous vehicles, then provides processed connectivity information to individual vehicles. This intermediary approach simplifies individual vehicle processing complexity while improving overall connectivity reliability.
2Measurement precision
If vehicles filter KPI data based on modem capabilities and route criteria, then route planning accuracy is improved, but processing time increases
Solution Approach 1:
The system applies different filtering criteria and processing levels to different portions of the KPI data based on specific route requirements and vehicle modem capabilities. Rather than uniformly processing all data with the same level of detail, the system tailors the filtering approach to local needs, improving accuracy where required while reducing processing time in less critical areas.
Solution Approach 2:
The system dynamically adjusts filtering parameters and data selection criteria based on route characteristics, vehicle capabilities, and connectivity requirements. By changing parameters such as data sampling frequency, filtering thresholds, and selection criteria according to specific conditions, the system achieves accurate route planning while minimizing processing time.
3Reliability
If the system compiles KPI data per road segment and time period, then connectivity awareness is improved, but data storage requirements increase
Solution Approach 1:
The system segments KPI data by road segments and time periods, organizing connectivity information into discrete, manageable units. This segmentation allows the system to store only relevant data for specific locations and times rather than maintaining continuous comprehensive data, reducing overall storage requirements while improving connectivity awareness for route planning.
Solution Approach 2:
The system stores KPI data at aggregated levels (per road segment and time period) rather than maintaining complete individual vehicle data points. This partial action approach retains sufficient connectivity awareness information for effective route planning while discarding redundant detailed data, thereby reducing storage requirements.
Data Source
AI summary
Using key performance indicator (KPI) data sensed by vehicles is provided. A data server is programmed to receive, over a wide-area network from a plurality of vehicles, connectivity data of modems of the plurality of vehicles to the wide-area network, the connectivity data indicating KPI data, which road segment was being traversed when the KPI data was captured, and a time period during which the KPI data was captured. The data server is further programmed to identify outlier data elements in the KPI data using outlier detection criteria; and compile the KPI data per road segment and time period excluding the outlier data elements.