Cloud-Based Calibration Model for Autonomous Vehicles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous vehicle calibration processes lack standardization in data quality and quantity, leading to inefficiencies and requiring individual vehicle calibration, which is time-consuming and not scalable for multiple vehicles.
Innovation Solution
A cloud-based system that collects and processes calibration data from multiple vehicles using machine learning to generate a calibration model, which can be deployed across multiple autonomous driving systems, standardizing the calibration process and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual vehicle calibration is performed using traditional methods, then each vehicle can be calibrated with specific parameters, but the calibration process becomes time-consuming and cannot be scaled to multiple vehicles
Solution Approach 1:
The patent combines calibration data from multiple vehicles into a centralized cloud-based system. Instead of calibrating each vehicle independently, the system aggregates sensor data, map data, and calibration parameters from multiple vehicles to create a shared calibration model that improves both accuracy and scalability.
Solution Approach 2:
The patent creates a universal calibration model that can be applied across multiple vehicle types and platforms. The cloud-based system generates calibration parameters that are not vehicle-specific but can be universally deployed to multiple autonomous vehicles, enabling scalable calibration while maintaining precision.
2Adaptability or versatility
If traditional ad-hoc calibration processes are used without standards, then flexibility in calibration approaches is maintained, but data quality and quantity lack consistency leading to inefficiencies
Solution Approach 1:
The patent establishes standardized parameters for data collection, including minimum data quantities, quality thresholds, and formatting requirements. These standardized parameters ensure consistent data quality across all vehicles while maintaining the flexibility to adapt to different vehicle configurations and sensor setups through configurable parameter ranges.
Data Source
AI summary
In one embodiment, a computer-implemented method for calibrating autonomous driving vehicles at a cloud-based server includes receiving, at the cloud-based server, one or more vehicle calibration requests from at least one user, each vehicle calibration request including calibration data for one or more vehicles and processing in parallel, by the cloud-based server, the one or more vehicle calibration requests for the at least one user to generate a calibration result for each vehicle. The method further includes sending, by the cloud-based server, the calibration result for each vehicle to the at least one user.