Autonomous Driving Data Pipeline for Multi-Layer Sensor Inputs
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Solution Overview
Problem
Existing deep learning systems for autonomous driving face challenges in maximizing signal information from captured sensor data due to conversion processes that can reduce fidelity and require adjustments for different sensors, necessitating a customized data pipeline to enhance input for deep learning analysis.
Innovation Solution
A data pipeline that extracts sensor data into separate components, such as feature and global data, and processes them differently to maintain targeted signal information, ensuring accurate feature detection by providing these components to appropriate layers of the deep learning network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensor data is converted from sensor format to deep learning input format, then compatibility with deep learning system is improved, but signal fidelity is reduced
Solution Approach 1:
The patent segments sensor data into multiple components (e.g., feature data, global data, different frequency bands) and processes each component separately through customized conversion pipelines. This allows different processing strategies for different data types, maintaining signal fidelity for critical components while ensuring compatibility for deep learning input.
Solution Approach 2:
The patent applies different conversion processes and quality parameters to different components of sensor data based on their specific characteristics and requirements. Critical features receive higher fidelity processing while less critical data undergoes more aggressive compression, optimizing the balance between information retention and system compatibility.
2Device complexity
If traditional conversion processes are used, then system simplicity is maintained, but signal information is lost
Solution Approach 1:
The patent introduces a segmented data pipeline that divides sensor data into multiple components, each processed through specialized conversion routines. This segmentation enables targeted information preservation for critical data components while maintaining overall system manageability through modular processing architecture.
Solution Approach 2:
The patent performs preliminary processing and component separation of sensor data before it enters the deep learning system. By pre-organizing data into meaningful components and applying appropriate conversion strategies in advance, the system preserves signal information without requiring complex real-time processing during inference.
3Adaptability or versatility
If different sensors require new conversion processes, then sensor adaptability is improved, but data pipeline complexity increases
Solution Approach 1:
The patent creates a universal data pipeline architecture that can handle multiple sensor types through a common framework. By defining standardized component types and conversion interfaces, the system achieves sensor adaptability without proportionally increasing pipeline complexity, as the same structural patterns are reused across different sensor modalities.
Solution Approach 2:
The patent achieves sensor adaptability by adjusting processing parameters and component separation strategies rather than fundamentally redesigning the conversion pipeline for each sensor type. This parameter-based approach allows the same data pipeline structure to accommodate diverse sensors with minimal complexity increase.
Data Source
AI summary
An image captured using a sensor on a vehicle is received and decomposed into a plurality of component images. Each component image of the plurality of component images is provided as a different input to a different layer of a plurality of layers of an artificial neural network to determine a result. The result of the artificial neural network is used to at least in part autonomously operate the vehicle.