Autonomous Vehicle Training Data Labeling Using Removable Sensor Pods
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Solution Overview
Problem
Current autonomous vehicle technologies face challenges in efficiently generating training data for machine learning models, particularly in complex environments, due to the time-consuming and error-prone process of manual data labeling and the difficulty in accurately labeling objects at varying distances and occlusions.
Innovation Solution
The use of additional vehicles equipped with removable hardware pods to collect and synchronize data with autonomous vehicles, enabling automatic generation of labeled training instances through temporal correlation and localization, which can then be used to train machine learning models for controlling autonomous vehicle actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data labeling is used for autonomous vehicle training, then training data can be generated, but the process is time-consuming and error-prone
Solution Approach 1:
The system uses additional vehicles equipped with sensors to automatically collect and generate labeled training data without human intervention. The additional vehicles autonomously perform the labeling task by detecting and recording attributes of target vehicles, eliminating the need for manual human labeling while improving both speed and consistency.
Solution Approach 2:
Additional vehicles serve as intermediary data collection platforms between the target autonomous vehicle and the training data generation process. These intermediary vehicles equiped with hardware pods collect sensor data and vehicle state information, which is then used to create labeled training instances without requiring direct human annotation.
2Productivity
If additional vehicles with hardware pods are deployed to collect data, then data generation speed increases, but system complexity increases
Solution Approach 1:
The hardware pod is designed as a universal, multi-functional unit that can be mounted on any additional vehicle. It integrates multiple sensor types (cameras, LIDAR, radar) and data collection capabilities into a single standardized platform, allowing the system to scale by simply adding more identical units rather than designing custom complex systems for each vehicle.
Solution Approach 2:
The data collection system is segmented into independent additional vehicles, each equipped with its own hardware pod. This modular approach allows the system to scale productivity by adding more independent units without proportionally increasing overall system complexity, as each unit operates autonomously with standardized interfaces.
3Reliability
If manual labeling is used for objects at varying distances and occlusions, then training data can be created, but human error increases
Solution Approach 1:
The manual human labeling process is replaced with automated sensor-based detection systems mounted on additional vehicles. These systems use computer vision, LIDAR, and radar to automatically detect, track, and label objects at various distances and occlusion levels, eliminating human error while maintaining consistent labeling standards across diverse scenarios.
Data Source
AI summary
Sensor data collected via an autonomous vehicle can be labeled using sensor data collected via an additional vehicle, such as a non-autonomous vehicle mounted with a vehicle agnostic removable hardware pod. A training instance can include an instance of data collected by an autonomous vehicle sensor suite and one or more corresponding labels.