Semantic Road Mapping Using Scored Lane Configurations
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Solution Overview
Problem
Current methods for generating semantic road maps are labor-intensive, resource-intensive, and prone to inaccuracies, making them inefficient for autonomous vehicle operations.
Innovation Solution
A system and method that uses sensor data from vehicles to generate semantic maps by creating hypothetical lane configurations, scoring their accuracy, and selecting the best fit, thereby determining road segment characteristics such as lane number, width, and orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to generate semantic road maps, then comprehensive road information can be obtained, but the process becomes labor-intensive and resource-intensive
Solution Approach 1:
The system uses sensor data from vehicles themselves to generate semantic maps, eliminating the need for manual data collection. Vehicles automatically capture trace points and key points during normal operation, and the processing system autonomously generates lane configurations without human intervention, achieving both high accuracy and efficiency
Solution Approach 2:
The system transforms raw sensor data into structured semantic map information by changing parameters through hypothetical lane configuration generation. Multiple hypothetical configurations are scored and compared to determine the most accurate representation of road characteristics, achieving precise measurement without manual labor
2Measurement precision
If multiple hypothetical lane configurations are generated and scored, then accuracy of lane characteristics is improved, but computational requirements increase
Solution Approach 1:
The system generates multiple hypothetical lane configurations (excessive action) to ensure accuracy, but applies scoring mechanisms to evaluate and rank these configurations. This allows the system to achieve high measurement precision through comprehensive analysis while using computational resources efficiently by focusing on scoring and selecting the best configurations rather than exhaustive processing
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to generating a semantic map for a road segment. In one embodiment, a method includes receiving sensor data related to a road segment. The sensor data includes trace points and key points associated with the trace points. The trace points are related to positions of a vehicle in the road segment and the key points are related to lane boundaries. The method includes generating hypothetical lane configurations, generating scores based on how accurately the key points match the hypothetical lane configurations, and selecting one hypothetical lane configuration from the hypothetical lane configurations based on a score among the scores. The score indicating most accurate match. The method includes determining characteristics of the road segment based on the one hypothetical lane configuration.