Free-Space Road Mapping for Low-Data Autonomous Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amount of data they need to process and store, including image data, map data, and sensor data, which can limit navigation accuracy and efficiency, especially when relying on traditional mapping technologies.
Innovation Solution
The use of cameras to monitor the vehicle's environment, analyze images, and update autonomous vehicle navigation models with location identifiers and directional indicators, allowing for real-time navigation adjustments and crowd-sourced data sharing among vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used for autonomous vehicle navigation, then comprehensive map data can be provided, but the sheer volume of data needed to store and update the map poses daunting challenges
Solution Approach 1:
The patent segments the comprehensive map data into specific navigation-relevant elements such as lane marks, road segments, and geometric features. Instead of storing and processing entire high-resolution maps, the system extracts and processes only the essential geometric and topological information needed for navigation decisions, significantly reducing data volume while maintaining navigation accuracy.
Solution Approach 2:
The system extracts only the critical navigation information from traditional map data, such as lane mark positions, road segment geometries, and intersection configurations. This extraction process removes unnecessary data while retaining the essential elements required for autonomous navigation, thereby reducing storage requirements and processing loads.
2Reliability
If vast volumes of information are collected and analyzed for navigation decisions, then navigation accuracy can be improved, but the challenges can limit or even adversely affect autonomous navigation
Solution Approach 1:
The system performs preliminary processing of map data during off-peak times or in advance, pre-computing navigation-relevant information such as lane mark geometries, road segment connections, and intersection layouts. This pre-processing reduces the real-time computational burden during actual navigation, allowing accurate decision-making without overwhelming system complexity during critical driving moments.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates complex map data into simplified navigation instructions. This intermediary layer processes raw map information and converts it into actionable navigation commands, reducing the complexity of data that needs to be handled by the core navigation system while maintaining decision accuracy.
3Adaptability or versatility
If real-time map updates are implemented for changing road conditions, then navigation adaptability can be improved, but data processing and distribution challenges increase
Solution Approach 1:
The system implements local quality updates by transmitting only the specific portions of map data that have changed, such as modified lane marks or updated road segments, rather than distributing entire map datasets. This approach allows the fleet to adapt to changing road conditions efficiently by processing only relevant local updates, maintaining high adaptability while preserving data processing efficiency.
Data Source
AI summary
A system for mapping road segment free spaces for use in autonomous vehicle navigation. The system includes at least one processor programmed to: receive from a first vehicle one or more location identifiers associated with a lateral region of free space adjacent to a road segment; update an autonomous vehicle road navigation model for the road segment to include a mapped representation of the lateral region of free space based on the received one or more location identifiers; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.