Map Tile Segmentation for Autonomous Vehicle Data Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to limitations in sensor data coverage, occlusions, and the absence of visible road signs, which existing map systems struggle to address effectively.
Innovation Solution
The system organizes map data into discretized, easily distributable segments called 'map tiles' using an immutable tree data structure stored in a distributed hash table, allowing for efficient distribution and updates of map data through a content delivery network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If map data is distributed as complete files, then data consistency is maintained, but download time and bandwidth consumption increase significantly
Solution Approach 1:
The patent divides map data into discrete map tiles organized in a hierarchical structure (e.g., 1km×1km, 500m×500m, 250m×250m levels). Each tile is independently downloadable, allowing vehicles to request only the specific tiles needed for their current location and route rather than downloading entire map files, thus reducing download time while maintaining data consistency through versioned tile identifiers.
Solution Approach 2:
The system pre-organizes map data into a hierarchical tile structure with version identifiers embedded in tile URLs (e.g., /v1/tiles/15/1234/5678.png). This preliminary organization allows vehicles to directly access current version tiles without downloading unnecessary data, and enables efficient updates by only replacing specific tiles that have changed while maintaining overall map consistency.
2Manufacturing precision
If map data is updated frequently, then navigation accuracy improves, but data distribution complexity increases
Solution Approach 1:
By segmenting the map into independent tiles with hierarchical organization, the system enables granular updates where only specific tiles need to be modified and redistributed when changes occur. This reduces distribution complexity compared to updating entire map files, while maintaining high navigation accuracy through frequent, targeted updates of relevant tiles.
Solution Approach 2:
The patent uses version parameters in tile URLs (e.g., /v1/, /v2/) to track and manage updates. When map data changes, the system updates specific tiles and increments version identifiers, allowing client devices to efficiently check for and download only the tiles that have changed. This parameter-based versioning simplifies the distribution of frequent updates while maintaining navigation accuracy.
3Manufacturing precision
If all map tiles are downloaded for potential routes, then route planning accuracy improves, but data storage requirements increase
Solution Approach 1:
The hierarchical tile structure allows the vehicle to download only the tiles corresponding to its current location and immediate surroundings at high resolution, with lower-resolution tiles for broader areas. This segmentation enables accurate route planning for the relevant area while minimizing storage requirements by not downloading unnecessary tiles for distant or unlikely routes.
Solution Approach 2:
The system implements local quality by providing high-resolution map tiles for the vehicle's current location and planned route, while using lower-resolution tiles for broader geographic areas. This allows accurate route planning where needed while reducing overall data storage requirements by adapting tile resolution to the local requirements of each geographic area.
Data Source
AI summary
In various examples, a method to manage map data includes storing a map of a geographic area using an immutable tree. The immutable tree comprises a plurality of nodes stored using a distributed hash table. The plurality of nodes include a plurality of map tiles. At least two map tiles of the plurality of map tiles cover different geographic subregions of the geographic area of the map. The method includes hosting one or more binary large objects (BLOBs) that correspond to the plurality of map tiles in an origin data plane. The method includes making the one or more BLOBs available for distribution to one or more client devices using a content delivery network (CDN).