Autonomous Vehicle Data Labeling With Removable Sensor Pods
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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 without human intervention.
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
1Reliability
If autonomous vehicles collect and interpret environmental data independently, then they can operate autonomously, but the accuracy and reliability of data interpretation is limited in complex scenarios
Solution Approach 1:
The patent combines data from multiple sources including autonomous vehicle sensors, additional vehicle sensor suites, and removable hardware pods to create a comprehensive labeled dataset. This merging of multiple data streams improves reliability by providing redundant and complementary information about the environment, while the coordinated system manages complexity through integrated data processing.
Solution Approach 2:
The patent introduces removable hardware pods as intermediary devices that can be mounted on additional vehicles to collect sensor data. These pods act as mediators between the additional vehicles and the autonomous vehicle system, enabling data collection without requiring permanent modifications to the additional vehicles and simplifying the overall system architecture.
2Measurement precision
If large amounts of labeled training data are collected manually, then model accuracy improves, but the time and resources required increase significantly
Solution Approach 1:
The system enables additional vehicles to automatically collect and provide labeled sensor data without requiring manual intervention. The hardware pods on additional vehicles autonomously capture environmental data and vehicle state information, and this data is automatically synchronized with the autonomous vehicle data to create labeled training datasets, eliminating the need for manual data annotation.
Solution Approach 2:
The patent collects and stores sensor data from additional vehicles and hardware pods in advance, creating a reservoir of pre-collected labeled data. This preliminary data collection allows the system to have ready-to-use training data available when needed, rather than collecting and labeling data on-demand, significantly reducing the time required for model training.
3Productivity
If multiple additional vehicles with hardware pods are deployed to collect data rapidly, then the quantity of training data increases, but the system complexity and coordination requirements increase
Solution Approach 1:
The removable hardware pods are designed to be universal and can be mounted on various types of additional vehicles. The pods perform multiple functions including sensor data collection, vehicle state monitoring, and automatic data synchronization. This multi-functionality allows the system to deploy diverse vehicle types without increasing system complexity, as the standardized pods handle all data collection and communication tasks.
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.