Vehicle Surroundings-to-Map Comparison Using LLM Descriptions
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Solution Overview
Problem
Existing systems struggle to effectively compare and describe surrounding area data with digital maps, particularly for automated vehicles, leading to potential safety and operational inefficiencies.
Innovation Solution
A method utilizing a large language model (LLM) to create a description of the comparison between surrounding area data sets and digital maps, incorporating static and dynamic objects, enhanced by a neural network architecture, to provide a detailed correspondence analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional comparison methods are used to match surrounding area data with digital maps, then the system complexity remains manageable, but the precision and speed of discrepancy detection are insufficient
Solution Approach 1:
The patent introduces an intermediary processing layer that transforms raw sensor data and map data into standardized feature representations before comparison. This intermediary step includes extracting relevant features (road markings, signs, infrastructure elements), normalizing their formats, and creating a common representation space that enables precise comparison without requiring direct complex processing of raw data from multiple sources.
Solution Approach 2:
The comparison system is divided into multiple independent modules: data acquisition modules for different sensor types, feature extraction modules that identify specific elements (road markings, signs, barriers), comparison modules that match features against map data, and discrepancy identification modules. This segmentation allows each module to specialize in specific tasks, improving overall precision while managing complexity through modular architecture.
2Reliability
If detailed surrounding area data is continuously compared with digital map data, then navigation safety is improved, but the processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential and safety-critical features from surrounding area data for comparison with digital maps, rather than processing all available sensor data. This includes selectively identifying and comparing key elements such as road markings, traffic signs, barriers, and infrastructure features that directly impact navigation safety, while filtering out redundant or less critical information.
Solution Approach 2:
The system performs preliminary processing of digital map data to pre-identify and store expected features and their locations before vehicle arrival. This allows the comparison process to focus on verifying specific pre-identified elements rather than performing comprehensive real-time analysis of all map features, significantly reducing processing time while maintaining safety monitoring.
3Loss of information
If comprehensive feature extraction is performed on all sensor data, then the completeness of comparison is improved, but the computational load and energy consumption increase
Solution Approach 1:
The system applies different levels of feature extraction and processing intensity to different types of sensor data and spatial regions based on their relevance to navigation safety. High-priority areas such as road boundaries, traffic signs, and critical infrastructure elements receive comprehensive processing, while less critical regions use simplified processing. This local differentiation ensures comparison completeness for safety-critical features while reducing overall computational energy consumption.
Data Source
AI summary
Method and device for providing a description of a comparison of a surrounding area with a digital map. The method includes a step of acquiring surrounding area data value sets that represent the surrounding area of a vehicle, wherein this surrounding area includes static and/or dynamic objects, a step of reading in the digital map, wherein the digital map represents a digital image of the surrounding area of the vehicle, a step of creating the comparison of the surrounding area data value sets with the digital map, a step of creating the description of the comparison by means of a large language model, wherein the description represents at least a degree of correspondence of the static and/or dynamic objects with the digital image, and a step of providing the description.