Lane Mark Mapping for Low-Storage Autonomous Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the sheer volume of data required for processing and storing information from various sources, such as camera images, GPS data, and sensor data, which can limit their navigation capabilities and pose daunting storage and update challenges for traditional mapping technologies.
Innovation Solution
The system employs cameras to analyze images and process data for autonomous vehicle navigation, using processors to detect lane marks, directional arrows, traffic lights, and free spaces, updating navigation models, and distributing them to other vehicles, enabling precise steering actions and navigation decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used to store and update map data, then navigation information can be provided, but the sheer volume of data poses daunting storage and update challenges
Solution Approach 1:
The patent extracts only the essential navigation elements (lane marks, directional arrows, traffic lights, free spaces) from the complete map data, storing only these critical features rather than entire map images. This extraction approach maintains navigation accuracy while dramatically reducing storage requirements.
Solution Approach 2:
The patent segments map data into discrete, identifiable elements (lane marks, arrows, traffic lights, free spaces) that can be independently detected, stored, and processed. This segmentation allows the system to handle navigation information in manageable units rather than as monolithic large-scale map data.
2Adaptability or versatility
If vast volumes of data are collected and processed by autonomous vehicles, then navigation decisions can be made, but this poses design challenges and can limit navigation capabilities
Solution Approach 1:
The system performs preliminary detection and classification of navigation elements (lane marks, arrows, traffic lights, free spaces) before navigation decisions are required. By pre-processing and organizing this data into structured formats with location identifiers, the system reduces the complexity of real-time decision-making while maintaining adaptability.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates raw sensor data into standardized navigation elements with defined parameters and locations. This intermediary representation simplifies the interface between data collection and navigation decision-making, reducing overall system complexity while preserving adaptability.
3Measurement precision
If cameras are used to capture environment images, then navigation information can be obtained, but real-time processing and accurate identification require sophisticated algorithms
Solution Approach 1:
The patent applies local quality by focusing image processing efforts specifically on regions containing navigation elements rather than analyzing entire images uniformly. The system identifies and processes lane marks, arrows, traffic lights, and free spaces with targeted algorithms appropriate to each element type, improving detection accuracy while reducing overall processing complexity.
Data Source
AI summary
Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.