Autonomous Vehicle Sensor Label Transfer for Faster Training Data
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Solution Overview
Problem
Current autonomous vehicle technologies face challenges in collecting and interpreting diverse environmental data, leading to limitations in safely navigating complex environments and requiring extensive manual labeling of sensor data for training models, which is time-consuming and inefficient.
Innovation Solution
The method involves generating labeled autonomous vehicle data by utilizing data from additional vehicles, including non-autonomous vehicles equipped with removable hardware pods, to automatically label sensor data from autonomous vehicles, providing diverse perspectives and reducing the need for manual labeling through data overlap and synchronization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of sensor data is performed to train autonomous vehicle models, then labeling accuracy can be ensured, but the time consumption and labor resources required increase significantly
Solution Approach 1:
The patent applies the copying principle by transferring labels from a first sensor data set to a second sensor data set. Instead of manually labeling each data set, the system copies labels from one labeled data set to another data set captured from a different viewpoint, significantly reducing labeling time while maintaining accuracy through the copying process
Solution Approach 2:
The patent implements preliminary action by pre-labeling a first sensor data set, which can then be used to automatically label subsequent second sensor data sets. This preliminary labeling effort serves as a foundation that eliminates the need for repeated manual labeling of similar data from different perspectives
2Adaptability or versatility
If diverse sensor data from multiple vehicles and perspectives is collected for training, then the richness and diversity of training data improve, but the complexity of data processing and integration increases
Solution Approach 1:
The patent applies universality by creating a labeling system that works across multiple vehicles and sensor data sets. The same labeling approach can be applied to data from different vehicles, sensor types, and viewpoints, making the system universally applicable to diverse autonomous vehicle training data while simplifying the processing complexity through a unified approach
3Productivity
If sensor data from additional vehicles is utilized to label autonomous vehicle data, then the efficiency of data labeling improves, but the requirements for data synchronization and coordination increase
Solution Approach 1:
The patent uses an intermediary approach by introducing a coordinate system transformation mechanism that mediates between data from additional vehicles and the autonomous vehicle data. This intermediary transformation layer handles the synchronization and coordination requirements, allowing efficient label transfer while managing the complexity through a standardized transformation process
Data Source
AI summary
One or more instances of sensor data collected using an autonomous vehicle sensor suite can be labeled using corresponding instance(s) of sensor data collected using an additional sensor suite. The additional vehicle can include a second autonomous vehicle as well as a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, an object in the sensor data captured using the autonomous vehicle can be labeled by mapping a label corresponding to the same object captured using the additional vehicle. In various implementations, labeled instances of sensor data can be utilized to train a machine learning model to generate one or more control signals for autonomous vehicle control.