Event Clustering for Autonomous Vehicle Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amount of data they need to process, analyze, and store, including visual information, GPS data, sensor data, and map data, which can limit their navigation capabilities.
Innovation Solution
The system uses cameras to provide autonomous vehicle navigation features by analyzing images to construct and navigate with a crowdsourced sparse map, combining this with GPS data, sensor data, and other map data to enhance navigation responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mapping technology is used for autonomous vehicle navigation, then navigation accuracy can be maintained, but the volume of data required to store and update maps becomes overwhelming
Solution Approach 1:
The patent extracts only the essential event information (detected event type, location, and temporal data) from the vast amount of sensor data collected by autonomous vehicles. By filtering and extracting only relevant navigation events rather than storing all raw sensor data, the system maintains navigation accuracy while dramatically reducing data volume requirements for map updates.
Solution Approach 2:
The patent merges event information from multiple autonomous vehicles into a unified sparse map representation. By combining event reports from multiple sources and consolidating them into clustered event information, the system achieves comprehensive navigation data without requiring storage of duplicate or redundant information from individual vehicles.
2Reliability
If vast volumes of data are collected and processed by autonomous vehicles, then navigation decision-making can be improved, but processing time and computational requirements increase
Solution Approach 1:
The patent extracts only the most critical event attributes (event type, location, timestamp) from comprehensive sensor data. By processing only these essential extracted features rather than the full raw data, the system maintains reliable navigation decision-making while significantly reducing processing time and computational load.
Solution Approach 2:
The patent performs preliminary processing of event data by clustering and aggregating event information from multiple vehicles before it is needed for navigation decisions. This pre-processing organizes data into useful patterns and structures in advance, reducing real-time processing requirements when navigation decisions must be made.
3Reliability
If event information from multiple vehicles is aggregated to improve map accuracy, then navigation reliability improves, but data management complexity increases
Solution Approach 1:
The patent segments the complex task of map management into discrete event-based units. By organizing data as individual event reports that can be independently processed, validated, and aggregated, the system simplifies data management complexity while improving map accuracy through systematic consolidation of event information from multiple vehicles.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between raw event data from multiple vehicles and the final sparse map representation. This intermediary layer handles the complexity of data aggregation, validation, and integration, shielding the navigation system from the complexity of managing multi-source data while ensuring map accuracy.
Data Source
AI summary
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.