LLM-Based Map Data Update for Autonomous Navigation
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Solution Overview
Problem
Existing approaches for generating realistic digital and virtual environments in autonomous systems are time-consuming and resource-intensive, particularly in updating maps for autonomous navigation, due to the complexity of integrating diverse sensor data from various sources, which often results in outdated maps and high computational costs.
Innovation Solution
The use of large language models (LLMs) to generate tokenized descriptions of environments, which can identify differences between map data and sensor data, allowing for real-time updates and accurate representation of physical environments, even with incomplete or inaccurate data, by correlating objects and features across multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor data collection and processing methods are used to update map data, then map data accuracy can be improved, but the time required for updates and computational resource requirements increase significantly
Solution Approach 1:
The patent extracts and utilizes pre-existing language models that have been trained on vast amounts of textual data. Instead of building new processing systems from scratch, the invention extracts useful pattern recognition capabilities from pre-trained models, significantly reducing training time and computational resources while maintaining high accuracy in identifying map changes from sensor data
Solution Approach 2:
The patent introduces language models as an intermediary layer between raw sensor data and map update decisions. These models translate sensor observations into natural language descriptions that can be compared against existing map data, enabling efficient change detection without requiring complex direct comparison algorithms
2Loss of information
If diverse sensor data from multiple vehicles is collected and processed to ensure comprehensive map coverage, then map completeness improves, but computational cost and network resource requirements increase
Solution Approach 1:
The patent merges observations from multiple vehicles by translating their sensor data into unified natural language descriptions. This consolidation approach allows the system to process diverse sensor inputs through a common language model interface, reducing computational overhead compared to processing each vehicle's data separately through multiple specialized algorithms
Solution Approach 2:
The patent uses language models to create textual representations (copies) of sensor observations. Instead of processing and storing all raw sensor data from multiple vehicles, the system creates compact language-based copies that capture essential information, significantly reducing storage and processing requirements while maintaining map completeness
3Reliability
If high-precision sensor data is collected from multiple sources to ensure accurate map updates, then data reliability improves, but the complexity of data integration and processing increases
Solution Approach 1:
The patent applies a universal language model approach that can handle multiple types of sensor data (visual, LiDAR, radar) through a single processing framework. This multi-functional system translates diverse data types into common language descriptions, eliminating the need for separate processing pipelines for each sensor type and reducing overall system complexity
Data Source
AI summary
Approaches presented herein provide for the identification of differences between local map data, for a region of a physical environment, and observation or perception data generated by one or more machines or other such sources. In at least one embodiment, sensors on an ego machine can capture sensor data for a region in which the ego machine is located, and a language model on the ego machine can compare this sensor data, or perception data generated using the sensor data, against the local map data. The language model can generate a tokenized description of identified differences, in a domain-specific language. The tokenized description can be transmitted to a map management service that can compare these differences against differences identified by other machines, for example, to determine whether to update and redistribute at least a portion of the map data.