Autonomous Vehicle Map Region Updates for Faster Navigation Refresh
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Solution Overview
Problem
Existing map updating methods for autonomous vehicles are inefficient as they often update the entire map, including outdated regions, which can be slow and wasteful, especially when only specific regions require updates due to changes in topography such as new roads or constructions.
Innovation Solution
An autonomous vehicle system that identifies discrepancies between sensed environmental properties and stored map data, generates annotations for the affected regions, and transmits these annotations to a remote compute device to create targeted map updates, ensuring only the necessary regions of the map are updated, reducing data transmission and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire map is updated using traditional methods, then map accuracy is improved, but update time and data transmission are significantly increased
Solution Approach 1:
The map is divided into multiple discrete regions, and only the specific region containing the discrepancy is updated rather than the entire map. This segmentation allows selective updating of map portions, reducing update time while maintaining accuracy in the affected area.
Solution Approach 2:
The discrepancy region is extracted and identified from the full map using sensor data comparison. By isolating only the affected region and updating that specific portion, the system achieves accurate map updates without the time penalty of processing the entire map.
2Reliability
If the entire map is updated using traditional methods, then map accuracy is improved, but data transmission requirements are significantly increased
Solution Approach 1:
The map update data is segmented into region-specific portions. Only the data corresponding to the identified discrepancy region is transmitted and updated, dramatically reducing the quantity of data that needs to be processed and transmitted while ensuring accuracy in the affected area.
Solution Approach 2:
The relevant map data for the discrepancy region is extracted from the full map dataset. This extraction enables transmission of only the necessary data portion, reducing data transmission requirements while maintaining map accuracy where it matters.
3Reliability
If traditional map updating methods are used, then complete map coverage is ensured, but processing efficiency is reduced
Solution Approach 1:
The map updating process is segmented to focus only on the identified discrepancy region. This selective processing approach maintains complete coverage of areas that need updating while dramatically improving processing efficiency by excluding already-accurate map regions from the update process.
Solution Approach 2:
Instead of performing a complete full-map update, the system applies partial action by updating only the specific region where a discrepancy was detected. This partial updating approach achieves sufficient map coverage for accurate navigation while significantly improving processing efficiency.
Data Source
AI summary
In an embodiment, a method comprises detecting, at a processor of an autonomous vehicle, a discrepancy between a map and a property sensed by at least one sensor onboard the autonomous vehicle, the property being associated with an external environment of the autonomous vehicle. In response to detecting the discrepancy, and based on the discrepancy, an annotation for the map is generated via the processor. A signal representing the annotation is caused to be transmit to a compute device that is remote from the autonomous vehicle. A signal representing a map update is received from the compute device that is remote form the autonomous vehicle. The map update is generated based on the annotation, the map update (1) including replacement information for a region of the map associated with the annotation, and (2) not including replacement information for a remainder of the map.