Automated Semantic Map Generation via Probabilistic Inference
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Solution Overview
Problem
Existing map generation methods are labor-intensive and prone to inaccuracies due to the manual inclusion of driving information, which can be time-consuming and error-prone.
Innovation Solution
A method for generating a probabilistic road model based on geometric data, combined with a probabilistic traffic model, to compute a statistical inference result and create a semantic road model, which is then used to generate an automated semantic map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If handcrafted methods are used to include driving information in maps, then map accuracy can be maintained through manual verification, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual handcrafted map creation with an automated system that uses probabilistic road models and statistical inference algorithms. The processing system automatically generates semantic maps by computing probabilities of road elements and their relationships, eliminating the need for manual verification while maintaining or improving accuracy through systematic probabilistic reasoning.
Solution Approach 2:
The patent transforms the map generation process from a deterministic manual process to a probabilistic automated process. By introducing probability parameters for road elements, traffic models, and statistical inference, the system can automatically handle uncertainties and generate accurate maps without manual intervention, thus improving both efficiency and reliability.
2Productivity
If automated methods are used to generate maps, then productivity and speed are improved, but accuracy may deteriorate due to lack of manual verification
Solution Approach 1:
The patent incorporates feedback mechanisms through probabilistic traffic models that continuously refine the semantic map generation. The system uses observed traffic data to update probability distributions and validate inferred road elements, providing automated verification that maintains accuracy while preserving the efficiency benefits of automation.
Solution Approach 2:
The patent replaces manual verification with automated statistical inference and probabilistic validation. The processing system uses computational algorithms to verify map accuracy through mathematical probability calculations, substituting human verification with a more scalable automated process that maintains high accuracy standards.
3Adaptability or versatility
If detailed semantic information is included in maps, then the usefulness and adaptability of the map improves, but the complexity of map creation and data processing increases
Solution Approach 1:
The patent segments the complex task of semantic map generation into distinct probabilistic models for different road elements (lanes, intersections, traffic signals). Each element is modeled separately with its own probability distributions, allowing the system to handle complexity in a modular fashion while generating comprehensive semantic information.
Solution Approach 2:
The patent creates a universal probabilistic framework that handles multiple types of road elements and semantic information through a unified statistical inference process. This multi-functional approach allows the same processing system to generate diverse semantic map information without proportionally increasing complexity, as the underlying probabilistic model serves multiple purposes.
Data Source
AI summary
A system and method for automated semantic map generation includes at least a processing system with at least one processing device. The processing system is configured to generate a probabilistic road model for road elements. The processing system is configured to generate a probabilistic traffic model for the road model. The processing system is configured to perform statistical inference on the road model and the traffic model. The processing system is configured to generate an estimate of a semantic road model based on the statistical inference. The processing system is configured to generate a semantic map that includes the estimate of the semantic road model.