Road Network Mapping Using Geohash Encoded Vehicle Telemetry
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Solution Overview
Problem
Existing vehicles with telematics units do not optimally utilize vehicle telemetry data for identifying road networks, leading to inefficiencies in data utilization.
Innovation Solution
A method and system that transform vehicle telemetry data into a geohash encoded format, allowing for the identification of road junctions and segments, and the generation of road network mappings using a processor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicle telemetry data is collected and processed using traditional methods, then road network mapping can be achieved, but data utilization efficiency is suboptimal and processing complexity increases
Solution Approach 1:
The patent introduces geohash encoding as an intermediary representation format between raw vehicle telemetry data and road network mapping structures. This intermediary encoding simplifies the processing complexity by providing a standardized, compact representation of geographic coordinates that facilitates efficient clustering and road segment identification algorithms.
Solution Approach 2:
The patent transforms vehicle telemetry data by changing the parameter representation from raw latitude/longitude coordinates to geohash encoded strings. This parameter transformation enables more efficient data aggregation, clustering, and processing operations, thereby improving data utilization efficiency while reducing computational complexity.
2Measurement precision
If geohash encoding is applied to vehicle telemetry data, then identification of road junctions and segments is improved, but data transformation complexity increases
Solution Approach 1:
The geohash encoding scheme is self-organizing and self-descriptive, where the encoding structure itself provides hierarchical geographic information without requiring additional indexing or metadata. This self-service property enables direct use of encoded values for clustering and spatial queries, improving measurement precision while the standardized algorithm keeps transformation complexity manageable.
Solution Approach 2:
The transformation from geographic coordinates to geohash encoding changes the parameter representation in a way that inherently preserves spatial relationships and enables precise road feature identification. The encoding's hierarchical structure allows for adjustable precision levels, balancing identification accuracy with transformation complexity.
3Manufacturing precision
If comprehensive vehicle telemetry data is processed from multiple vehicles, then road network mapping accuracy is improved, but data processing time increases
Solution Approach 1:
The patent segments the road network mapping problem into distinct components: road segment identification, road junction identification, and road network construction. By processing vehicle telemetry data through geohash encoding and applying clustering algorithms to each segment type separately, the system achieves comprehensive mapping accuracy from multiple vehicles while reducing overall processing time through modular, parallelizable operations.
Data Source
AI summary
In exemplary embodiments, methods and systems are provided that include: obtaining, via telematics systems of a plurality of vehicles, vehicle telemetry data as the plurality of vehicles travel through one or more geographic regions; transforming, from a computer processor, the vehicle telemetry data into a geohash encoded format pertaining to the one or more geographic regions; identifying, via a processor, a plurality of road junctions using the vehicle telemetry data that is transformed into a geohashed encoding; identifying, via the processor, a plurality of road segments using the vehicle telemetry data that is transformed into a geohashed encoding for the one or more geographic regions; and generating, via the processor, a road network mapping for the one or more geographic regions that includes the plurality of road junctions and the plurality of road segments utilizing the vehicle telemetry data that is transformed into a geohashed encoding.