Autonomous Vehicle HD Map Caching for Real-Time Route Access
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Solution Overview
Problem
Conventional maps for autonomous vehicles lack the accuracy and up-to-date information required for safe navigation, as they are often created using expensive and time-consuming survey methods, and high-resolution maps face challenges in storage and transmission due to their large size and frequent updates.
Innovation Solution
The system generates and maintains high-definition maps with 5 cm resolution, using data from autonomous vehicles themselves, and employs a caching system to efficiently transfer and store map tiles, allowing for real-time access to relevant map data, even with limited storage and bandwidth, by partitioning and compressing map data and using optimized data structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional survey methods are used to create maps, then map accuracy can be improved, but the time and cost required to create and update maps increases significantly
Solution Approach 1:
The patent uses autonomous vehicles to collect map data by having them drive through geographic regions and capture sensor data, creating copies of map information from multiple sources rather than relying on traditional survey teams. This allows parallel data collection across multiple regions simultaneously, reducing overall map creation time while maintaining accuracy through sensor fusion algorithms.
Solution Approach 2:
The system enables autonomous vehicles to contribute to map creation and updates by collecting sensor data during their normal operations. Vehicles automatically process their own sensor data and contribute it to the collective map database, eliminating the need for dedicated survey teams and enabling continuous, cost-effective map updates as vehicles naturally traverse different regions.
2Measurement precision
If high resolution sensors are used to capture detailed map data, then map detail and accuracy are improved, but the data size increases making storage and transmission challenging
Solution Approach 1:
The patent extracts only the essential and relevant features from raw sensor data for map creation, rather than storing complete high-resolution sensor feeds. The system identifies and extracts key navigational elements such as lane markings, road boundaries, and significant landmarks, discarding redundant information while maintaining sufficient detail for autonomous navigation.
Solution Approach 2:
The map data is segmented into discrete geographic regions or tiles that can be independently stored, managed, and transmitted. This segmentation allows the system to handle large volumes of map data in manageable units, enabling efficient storage and selective transmission of only the regions relevant to current vehicle operations rather than entire high-resolution maps.
3Reliability
If map data is frequently updated to reflect road changes, then navigation safety is improved, but the computational resources and time required for transmission increase
Solution Approach 1:
The system implements differential updates that transmit only the specific portions of map data that have changed, rather than redistributing entire maps. When road conditions or features change in a particular geographic region, only the affected map tiles or segments are updated and transmitted to vehicles passing through or near those regions, significantly reducing computational overhead and bandwidth requirements.
Solution Approach 2:
The system pre-processes and validates map updates before transmission, preparing only necessary change information in advance. By anticipating which regions may experience changes and pre-processing update data for those areas, the system reduces the computational burden during actual transmission and update operations, enabling frequent safety-critical updates with minimized resource consumption.
Data Source
AI summary
High definition maps for autonomous vehicles are very high resolution and detailed, and hence require storage of a great deal of data. A vehicle computing system provides multi-layered caching makes this data usable in a system that requires very low latency on every operation. The system determines which routes are most likely to be driven in the near future by the car, and ensures that the route is cached on the vehicle before beginning the route. The system provides efficient formats for moving map data from server to car and for managing the on-car disk. The system further provides real-time accessibility of nearby map data as the car moves, while providing data access at optimal speeds.