Navigation Information Fusion Using Sparse Maps for Autonomous Vehicles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in navigating effectively due to the vast amounts of data they need to process and store for navigation, including visual information, GPS data, and sensor data, which can lead to inefficiencies and safety concerns.
Innovation Solution
The use of cameras and image processing systems to provide navigational responses based on image analysis, combined with GPS and sensor data, and the implementation of a crowdsourced sparse map for optimized navigation, ensuring safety and comfort constraints are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mapping technology is used to navigate, then navigation accuracy is improved, but data storage requirements and system complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigational elements from complete maps, creating sparse maps that contain only critical information needed for navigation decisions. This reduces data storage requirements while maintaining navigation accuracy by focusing on key features rather than storing entire map datasets.
Solution Approach 2:
The navigation system segments map data into hierarchical levels, using detailed maps only when needed and sparse maps for general navigation. This segmentation allows the system to manage complexity by dividing map representation into manageable portions based on navigation requirements.
2Measurement precision
If detailed map data is stored and updated continuously, then navigation accuracy is improved, but data transmission and processing time increase
Solution Approach 1:
The system extracts only essential navigational information from detailed maps for transmission and processing, reducing data volume while maintaining navigation accuracy. Sparse maps contain only critical path information, eliminating unnecessary data transmission overhead.
3Reliability
If vast volumes of sensor data are collected and analyzed, then navigation reliability is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant features from sensor data that are critical for navigation decisions, rather than processing entire datasets. This selective extraction maintains navigation reliability by focusing on key indicators while reducing computational burden and processing time.
Solution Approach 2:
The patent applies partial processing to sensor data, analyzing only the portions necessary for current navigation decisions rather than comprehensively processing all available data. This approach achieves sufficient reliability for safe navigation while minimizing processing time and resource consumption.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
The present disclosure relates to apparatuses, systems and methods for navigating vehicles. In one implementation, at least one processing device receive a first output from a first data source and a second output from a second data source; identify a representation of a target object in the first output; determine whether a characteristic of the target object triggers at least one navigational constraint; if the at least one navigational constraint is not triggered by the characteristic of the target object, verify the identification of the representation of the target object based on a combination of the first output and the second output; if the at least one navigational constraint is triggered by the characteristic of the target object, verify the identification of the representation of the target object based on the first output; and in response to the verification, cause at least one navigational change to the vehicle.