Vehicle Virtual Map Generation with Dynamic Resource Allocation
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently compiling and utilizing sensed properties from various sources, including themselves, other vehicles, and the environment, to generate accurate and dynamic virtual maps for navigation and control.
Innovation Solution
A vehicle system that includes processors configured to generate virtual maps based on local sensors and external reports, reallocating processing resources, controlling motors, and managing reports from external entities to ensure accurate mapping and navigation while ignoring or marking outlying data for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If processing resources are dedicated to generating virtual map from local sensors, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The system applies different processing resource allocations to different geographical areas of the virtual map based on their importance and dynamic characteristics. High-priority areas receive more processing resources for higher accuracy, while low-priority areas use fewer resources, resolving the contradiction between overall accuracy and processing efficiency.
Solution Approach 2:
The processing resource allocation is dynamically adjusted based on real-time conditions, vehicle location, and map update requirements. The system transitions from static full-resource processing to dynamic adaptive processing, maintaining accuracy where needed while improving overall productivity through resource optimization.
2Measurement precision
If processing resources are increased for virtual map generation, then measurement precision is improved, but loss of energy increases
Solution Approach 1:
Energy consumption is optimized by applying processing resources only where necessary for maintaining virtual map accuracy. The system identifies critical geographical areas that require high-fidelity mapping and allocates energy accordingly, rather than uniformly processing entire map areas, thus reducing overall energy loss while preserving measurement precision in essential regions.
3Reliability
If all external reports are processed, then reliability is improved, but device complexity increases
Solution Approach 1:
The system extracts and processes only the essential and reliable information from external reports, filtering out redundant or low-value data. By selectively processing reports based on their relevance and quality, the system maintains high reliability while reducing the complexity of the processing system compared to handling all reports comprehensively.
Solution Approach 2:
Rather than processing all external reports exhaustively, the system applies partial processing focused on high-priority, high-reliability data sources. This selective approach achieves sufficient reliability for autonomous vehicle operation without the excessive complexity of complete report processing.
4Measurement precision
If processing resources are allocated to generate virtual map at high frequency, then measurement precision is improved, but productivity of other tasks deteriorates
Solution Approach 1:
The system applies high-frequency processing only to specific critical areas of the virtual map where rapid updates are essential for safety and navigation, while using lower update frequencies for less critical regions. This localized high-frequency approach maintains measurement precision where needed while preserving overall system productivity by avoiding unnecessary high-frequency processing across the entire map.
Data Source
AI summary
A vehicle includes: motor(s), local sensors, processor(s) configured to: generate a virtual map based on the local sensors; receive a plurality of reports from a plurality of external entities, statistically compare the plurality of reports, and identify an outlying report based on the statistical comparison; instruct the external entity responsible for the outlying report to mark future reports; ignore marked reports; generate the virtual map based on unmarked reports; reallocate virtual map processing resources based on unmarked reports.