Road Segment Alignment for Crowdsourced Vehicle Navigation Data
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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 to provide navigation features, construction and navigation with crowdsourced sparse maps, and the fusion of data from various sources like GPS, sensors, and maps to optimize navigation while ensuring safety, including a system that aligns navigation information from multiple vehicles to improve route alignment and data efficiency.
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 and utilizes only the essential navigational features from road environments (lane markings, road geometry, signage) to create sparse maps, rather than storing complete traditional map data. This selective extraction maintains navigation accuracy while dramatically reducing data storage requirements.
Solution Approach 2:
Instead of vehicles receiving pre-stored traditional maps from servers, the system inverts the approach by having vehicles contribute their captured road information to collectively build sparse maps. This crowdsourced approach reduces individual vehicle storage needs while maintaining comprehensive coverage.
2Reliability
If complete map data is stored and updated continuously, then navigation reliability is improved, but system complexity and update challenges increase
Solution Approach 1:
The patent merges navigational data from multiple vehicles to create collective sparse maps. By combining observations from various vehicles traveling different routes, the system achieves comprehensive and reliable map coverage without requiring complex centralized update mechanisms.
Solution Approach 2:
Vehicles autonomously contribute their captured road information to the sparse map system without requiring manual updates or complex server-mediated synchronization. Each vehicle self-services the collective knowledge base by sharing its observations, simplifying the overall system architecture.
3Productivity
If navigation relies solely on pre-stored maps, then route planning is improved, but adaptability to real-time environmental changes deteriorates
Solution Approach 1:
The system implements feedback loops where vehicles continuously capture and share real-time road condition information, which is then integrated into the sparse maps. This allows the navigation system to adapt to environmental changes while maintaining efficient route planning through updated collective knowledge.
Data Source
AI summary
The present disclosure relates to systems and methods for aligning navigation information from a plurality of vehicles. In one implementation, at least one processing device may receive first navigational information from a first vehicle and second navigational information from a second vehicle. The first and second navigational information may be associated with a common road segment. The processor may divide the common road segment into a first road section and a second road section that join at a common point. The processor may then align the first and second navigational information by rotating at least a portion of the first navigational information or the second navigational information relative to the common point. The processor may store the aligned navigational information in association with the common road segment and send the aligned navigational information to vehicles for use in navigating along the common road segment.