Autonomous Vehicle Lighting State Detection via Automated Training Data
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Solution Overview
Problem
Autonomous vehicles face challenges in determining the lighting state of other vehicles without human intervention, as training machine learning models requires significant human effort for labeling images.
Innovation Solution
A system and method that automatically extracts and labels training images from sensor data using a machine learning model, such as a convolutional neural network, to determine the lighting state of observed vehicles without human involvement, utilizing sensor data from lidar and map information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to determine vehicle lighting state, then the autonomous vehicle can predict behavior of other vehicles, but training the model requires significant human intervention and time
Solution Approach 1:
The system performs preliminary actions by collecting and storing sensor data (images, lidar, map data) in advance during normal vehicle operation. This pre-collected data is then used to automatically generate training images and labels without requiring real-time human intervention, thus reducing training time while maintaining prediction accuracy
Solution Approach 2:
The system enables self-service by automatically generating training data from its own sensor collections. The autonomous vehicle uses its previously collected sensor data to create labeled training images through automated processing, eliminating the need for external human annotators and significantly reducing the time required for model training
2Measurement precision
If extensive human labeling of training images is performed, then the machine learning model can accurately recognize lighting states, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs self-service by automatically generating training images and their corresponding labels from previously collected sensor data. The automated processing pipeline extracts relevant features and generates accurate labels without human intervention, maintaining recognition precision while dramatically improving training data preparation efficiency
Solution Approach 2:
The system takes preliminary action by collecting and storing diverse sensor data (multiple camera views, lidar point clouds, map information) in advance. This pre-collected data serves as the foundation for automatically generating high-quality training images with accurate labels, eliminating the need for manual labeling while ensuring measurement precision
Data Source
AI summary
Systems and method are provided for controlling a vehicle. In one embodiment, a vehicle lighting detection method includes receiving sensor data associated with operation of one or more vehicles, and extracting from the sensor data a plurality of images and a plurality of corresponding image labels, wherein the images each include at least a portion of an observed vehicle, and the image labels indicate the corresponding lighting state of the observed vehicle in each of the images. The method further includes training, with a processor, a machine learning model utilizing the plurality of images and the plurality of corresponding image labels.