Removable Sensor Pods for Faster Autonomous Vehicle Data Labeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous vehicle technologies face challenges in efficiently collecting and interpreting environmental data, particularly in complex scenarios, which hinders their ability to safely navigate and respond to various interactions within their environment.
Innovation Solution
The generation of labeled autonomous vehicle data through the use of additional vehicles equipped with removable hardware pods, which collect and synchronize data with autonomous vehicles, enabling the creation of large datasets for training machine learning models to improve navigation and control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If autonomous vehicles collect and interpret environmental data using their own sensor suites, then they can operate independently, but the amount of data required for comprehensive training is insufficient and collection is time-consuming
Solution Approach 1:
The patent combines data collection efforts by deploying sensor suites on multiple vehicles (both autonomous and non-autonomous) to collectively gather environmental data. This merging of collection resources accelerates data accumulation while maintaining comprehensive coverage of diverse driving scenarios.
Solution Approach 2:
The sensor suites are designed to be universal and can be deployed on different vehicle types (autonomous and non-autonomous). This multi-functionality allows the same data collection infrastructure to serve multiple purposes: training autonomous vehicles, studying human driving behavior, and gathering environmental information across various vehicle platforms.
2Measurement precision
If autonomous vehicles use full sensor suites for data collection, then data accuracy is high, but device complexity and cost increase
Solution Approach 1:
The patent applies different sensor configurations to different vehicles based on their specific needs and roles. Autonomous vehicles use full sensor suites for high-accuracy autonomous operation, while non-autonomous vehicles use simplified sensor suites for specific data collection tasks like human behavior analysis or supplementary environmental data, optimizing the complexity-accuracy trade-off for each application.
Solution Approach 2:
Non-autonomous vehicles with simplified sensor suites act as intermediaries that collect and contribute data to the overall training dataset. These intermediary data sources provide valuable supplementary information without requiring the full complexity of autonomous vehicle sensor systems, thereby reducing overall system complexity while maintaining data accuracy.
3Adaptability or versatility
If autonomous vehicles collect data from multiple sources including non-autonomous vehicles, then data diversity increases, but data synchronization and correlation complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where data from multiple vehicles is continuously correlated and synchronized. The system uses temporal and spatial relationships to align data streams, providing feedback loops that refine the correlation process and improve the integration of diverse data sources over time.
Solution Approach 2:
The patent transforms diverse data from different vehicle sources into a standardized format by changing parameters such as time synchronization references, coordinate systems, and data representation formats. This parameter standardization enables efficient processing and correlation of data from heterogeneous sources without requiring complex custom processing for each data type.
Data Source
AI summary
Sensor data collected from an autonomous vehicle data can be labeled using sensor data collected from an additional vehicle. The additional vehicle can include a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, removable hardware pods can be vehicle agnostic. In many implementations, generated labels can be utilized to train a machine learning model which can generate one or more control signals for the autonomous vehicle.