Crowd-Sourced Map Data Verification for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles rely heavily on sensor data for navigation, which requires significant bandwidth and processor power, and maintaining accurate high-resolution maps is costly and logistically impractical due to the need for specialized equipment and frequent updates in response to changing conditions.
Innovation Solution
A system that utilizes crowd-sourced local map data from electronic devices to update global maps, comparing local data to existing global maps to identify differences and schedule remapping by specialized vehicles, thereby reducing the burden on onboard sensors and improving map accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles rely heavily on sensor data for navigation, then navigation capability is improved, but bandwidth consumption and processor power requirements increase
Solution Approach 1:
The system performs preliminary mapping actions by crowd-sourced vehicles before autonomous vehicles need to navigate. Map data is collected and updated in advance by vehicles equipped with sensors, so that when an autonomous vehicle needs navigation information, it is already available, reducing the need for real-time sensor processing and heavy computational load during actual navigation operations.
2Measurement precision
If high-resolution maps are maintained through specialized vehicles, then map accuracy is improved, but cost and logistical complexity increase
Solution Approach 1:
Vehicles in the fleet serve multiple functions: they perform regular delivery routes while simultaneously collecting map data for areas they traverse. This multi-functionality eliminates the need for dedicated specialized mapping vehicles, reducing logistical complexity while maintaining map accuracy through crowd-sourced data collection from regular fleet operations.
3Measurement precision
If frequent map updates are performed to reflect changing conditions, then map accuracy is improved, but processing load and cost increase
Solution Approach 1:
The system implements self-service map updates where vehicles automatically contribute their sensor data to update maps of areas they travel through. This eliminates the need for centralized scheduling of dedicated mapping operations, reducing processing overhead while ensuring maps are continuously updated with fresh data from the fleet's regular operations.
Data Source
AI summary
Systems and methods to provide accurate and timely maps to autonomous vehicles. The system can utilize local map data from a plurality of electronic devices via a data gathering application (“app”). The system can compare local map data to existing global maps to identify differences and update the global maps and/or indicate that the global maps need to be updated. The system can receive camera, GPS, cellular location services, accelerometer, magnetometer, and other sensor data from the plurality of electronic devices. The system can provide incentives to users to drive on routes or proximate areas-of-interest. This can include routes or areas-of-interest that are frequently used by the autonomous vehicles in the system. This can also include routes that include accidents, construction sites, and other differences that can receive more frequent updates. The app can include a user interface (UI) to enable users to provide updates related to certain conditions.