Semantic Road Mapping Using Trace Point Lane Relationships
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Solution Overview
Problem
Current methods for generating semantic road maps are labor-intensive, time-consuming, and resource-intensive, often involving manual labor and leading to inaccuracies.
Innovation Solution
A system and method that utilizes sensor data from vehicles to determine relationships between trace points and key points, such as lane boundaries, to generate a semantic map by determining probabilities of trace points being in the same, adjacent, or non-adjacent lanes, thereby reducing manual labor and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to generate semantic road maps, then detailed road information can be obtained, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical processes with an automated computer vision system that uses sensor data, trace points, and key points to generate semantic road maps. The system automatically determines relationships between trace points based on key points (lane boundaries) and extracts road characteristics without human intervention, thereby eliminating labor-intensive manual work while maintaining high accuracy through algorithmic processing.
2Manufacturing precision
If manual labor is used to create semantic road maps, then detailed characteristics can be captured, but resource consumption increases
Solution Approach 1:
The system performs self-service by automatically processing sensor data to generate semantic road maps without requiring external manual labor. The computer vision algorithms independently identify key points (lane boundaries), establish relationships between trace points, and extract road characteristics autonomously. This self-service automation reduces resource consumption by eliminating the need for human annotators and manual processing while maintaining high manufacturing precision through systematic algorithmic analysis.
3Reliability
If traditional methods are used to generate semantic road maps, then comprehensive road data can be collected, but the process becomes resource-intensive
Solution Approach 1:
The patent replaces traditional resource-intensive manual methods with an automated computer vision system that processes sensor data efficiently. The system uses trace points representing vehicle positions and key points representing lane boundaries to automatically determine spatial relationships and extract road characteristics. This substitution dramatically improves productivity by enabling rapid, large-scale map generation while maintaining high reliability through consistent algorithmic processing of comprehensive road data.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to generating a semantic map for a road portion. In one embodiment, a method includes receiving sensor data related to a road portion. 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 portion and the key points are related to lane boundaries. The method includes determining a relationship between the trace points based on the key points and determining characteristics of the road portion based on the relationship.