Clustering event information for vehicle navigation

Abstract

Systems and methods are disclosed for aggregating informational reports. In one implementation, at least one processor may be programmed to receive an informational vehicle report identifying a detected event; store the report in a database in association with a first cell; query a second cell within a predetermined distance of the first cell; and determine whether the second cell is associated with the detected event. When the second cell is associated with the detected event the processor may aggregate information from the first and second cells to provide an aggregated cluster and generate an event report based on the aggregated cluster. When the second cell is not associated with an information cluster associated with the detected event, the processor may generate the event report based on the stored informational vehicle report. The processor may then transmit the event report to one or more vehicles.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to U.S. Provisional Application No. 62 / 796,355, filed January 24, 2019, and U.S. Provisional Application No. 62 / 956,929, filed January 3, 2020. The foregoing applications are hereby incorporated by reference in their entirety. technical field [0003] The present disclosure generally relates to systems and methods for clustering event information for use in vehicle navigation. Background technique [0004] As technology continues to advance, the goal of fully autonomous vehicles capable of navigating the road is on the horizon. An autonomous vehicle may need to consider a wide variety of factors and make appropriate decisions based on those factors in order to reach the intended destination safely and accurately. For example, an autonomous vehicle may need to process and interpret visual information (e.g., captured from a camera), information from radar or lidar, and may also use info...

AI Technical Summary

Problems solved by technology

Harnessing and interpreting the vast amount of information collected by an autonomous vehicle as it travels to its destination poses many design challenges

The vast amount of data that autonomous vehicles may need to analyze, access, and / or store (e.g., captured image data, map data, GPS data, sensor data, etc.) creates challenges that may actually limit or even adversely affect autonomous navigation

Furthermore, if the autonomous vehicle relies on traditional mapping techniques to navigate, the massive amount of data required to store and update the map poses a formidable challenge

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