Precision Vehicle Positioning via Road Object Alignment
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Solution Overview
Problem
Current vehicle positioning systems are inadequate for highly automated driving (HAD) due to insufficient detailed and fresh road data, leading to reliance on expensive and error-prone scanning methods, and are impaired by environmental conditions, which limits the effectiveness of sensors and increases the complexity and cost of maintaining accurate digital maps.
Innovation Solution
A two-step object data processing method involving a vehicle collecting ambient data and communicating with a precision road property database to enhance data quality, using a combination of sensors like cameras, radar, and laser sensors, and a server database for updating and aligning data sets to provide detailed and reliable road information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized scanning vehicles with expensive equipment are used to generate road maps, then measurement precision of road data is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent makes ordinary production vehicles serve multiple functions: they continue their normal transportation role while simultaneously collecting road property data for mapping. This eliminates the need for specialized scanning vehicles by using the vehicle fleet already present in the environment, thereby reducing device complexity while maintaining data collection capabilities
Solution Approach 2:
Instead of using expensive specialized scanning equipment, the patent uses standard sensors already present in ordinary vehicles to create copies of road data. These sensor measurements are then processed and stored to create digital road maps, replacing the need for dedicated scanning apparatus
2Manufacturing precision
If manual processing of acquired road information is performed, then manufacturing precision of digital maps is improved, but loss of time and productivity decrease due to expensive and labour intensive processes
Solution Approach 1:
The patent replaces manual processing with automated computer-based processing systems. Algorithms automatically process the sensor data collected by vehicles, perform quality checks, and generate updated road maps without human intervention, thereby dramatically reducing processing time while maintaining or improving accuracy through systematic automated validation
Solution Approach 2:
The system enables self-service processing where the data processing and map generation is performed automatically by the system itself using predefined algorithms and quality criteria. The processed data from vehicles is automatically validated, integrated, and used to update the road property database without requiring external manual processing
3Productivity
If sensors are used to collect road data, then productivity of data collection is improved, but reliability decreases under adverse environmental conditions such as fog or heavy rain
Solution Approach 1:
The patent merges data from multiple different sensor types (cameras, radar, laser sensors, ultrasonic sensors) to compensate for the limitations of individual sensors in adverse conditions. By combining the strengths of different sensing modalities, the system maintains reliable data collection across varying environmental conditions where no single sensor type would be sufficient
Solution Approach 2:
The system uses feedback mechanisms where collected data is continuously validated against existing road map data and quality criteria. When data quality falls below thresholds due to environmental conditions, the system can request re-collection or use alternative data sources, thereby maintaining overall reliability while allowing continuous operation in diverse conditions
4Measurement precision
If detailed and fresh road data is provided to vehicles, then precision vehicle positioning is improved, but device complexity and cost of maintaining accurate digital maps increase
Solution Approach 1:
The system implements self-service maintenance where the digital road maps are automatically updated by processing data collected from ordinary vehicles during their normal operation. This continuous automated updating eliminates the need for manual map maintenance operations while keeping the data fresh and accurate for precision positioning applications
Solution Approach 2:
The system establishes continuous data collection and processing operations using the ongoing movement of vehicle fleets. Rather than periodic specialized scanning campaigns, the road data is continuously updated as vehicles naturally traverse the roads, providing fresh data without interrupting normal traffic flow or requiring dedicated maintenance operations
Data Source
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AI summary
A method and a system for precision vehicle positioning based on a digital road description database containing fourth data sets with object-based information about at least one of a road object, a road furniture object and a geographic object is disclosed. First data sets based on the fourth data sets are forwarded to a vehicle. The vehicle is collecting ambient data like images along its path, generating second data sets comprising at least location information and detailed object-based information. During driving and after generating second data sets, the vehicle tries to identify in the second data sets the same driving-relevant objects and/or object reference points and/or groups of driving-relevant objects and/or object reference points as are contained in the first data sets. Furthermore, the vehicle aligns the same driving-relevant objects and/or object reference points and/or groups of driving-relevant objects and/or object reference points between the second data sets and the first data sets, and derives a position value for its own position.