Vehicle Sensor Data Annotation for Object Recognition Training
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Solution Overview
Problem
Existing object recognition systems in computer vision face challenges in efficiently training machine-learning programs due to the lack of annotated environmental data, which limits the accuracy and effectiveness of these systems.
Innovation Solution
The proposed solution involves a system that records environmental data from sensors on a vehicle and non-environmental data from onboard sources, such as audio or vehicle network data. These non-environmental data are then annotated and combined with environmental data to create annotated training data for machine-learning programs, enhancing the training process and potentially increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually annotated training data is used to train machine-learning programs, then accuracy of object recognition is improved, but time and cost for data preparation increase significantly
Solution Approach 1:
The system uses the vehicle's own operational data (audio recordings, sensor data, event logs) to automatically generate annotations for training data. The vehicle essentially annotates its own environmental data through self-service mechanisms like automatic speech recognition of passenger conversations and automated event detection from sensor fusion, eliminating the need for manual human annotation while maintaining high accuracy.
Solution Approach 2:
The patent introduces an automated annotation system that acts as an intermediary between raw environmental data and training data. This intermediary process combines multiple data sources (audio, sensors, event logs) and uses automated processing (speech-to-text conversion, event correlation algorithms) to generate annotations, thereby reducing direct human involvement while preserving annotation quality.
2Measurement precision
If manually annotated training data is used to train machine-learning programs, then accuracy of object recognition is improved, but cost of data preparation increases
Solution Approach 1:
The vehicle system automatically generates its own training data annotations using onboard resources including microphones for audio recording, existing sensors for environmental data, and processors for speech-to-text conversion and event detection. This self-service approach eliminates external annotation services and reduces costs while maintaining annotation quality through multi-source data fusion.
Solution Approach 2:
The system repurposes existing vehicle components and data sources for dual functions: normal vehicle operation and automated training data generation. The same microphones used for passenger entertainment also capture conversation for annotation; the same sensors used for vehicle control also provide environmental context for training data, thereby reducing overall system cost while improving annotation quality.
3Adaptability or versatility
If diverse training data is collected to improve machine-learning program performance, then generalization capability is improved, but data management complexity increases
Solution Approach 1:
The patent merges multiple diverse data sources (environmental sensor data, audio recordings, event logs, vehicle operational data) into a unified training dataset. By combining these sources and automatically correlating them through time synchronization and event matching, the system achieves diverse training data with improved generalization capability while managing complexity through automated integration processes rather than separate manual management of each data type.
Data Source
AI summary
A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive first environmental data recorded by an environmental sensor on board a vehicle, receive nonenvironmental data recorded on board the vehicle independently of the first environmental data, add a plurality of annotations derived from the nonenvironmental data to the environmental data, and train a machine-learning program to process second environmental data by using the first environmental data as training data and the annotations as ground truth for the first environmental data.