Optical Neural Network for LIDAR Processing Speed
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Solution Overview
Problem
Conventional LIDAR systems face limitations in efficiently processing and measuring the physical characteristics of objects in an environment, as they rely on predetermined algorithms that may not capture all information in the return light pulses and can be computationally expensive or impractical.
Innovation Solution
An optical neural network (ONN) is integrated along the transmission path between the transmitter and detector array of a LIDAR system, processing the reflected electromagnetic radiation to generate an embedding vector that represents physical characteristics, using optical components for faster and more energy-efficient computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional predetermined algorithms are used to process return light pulses, then the system structure remains simple, but the processing speed is slow and computational complexity is high
Solution Approach 1:
The patent replaces conventional electronic computational systems with an optical neural network that processes light pulses directly in the optical domain. This substitution enables parallel processing of multiple features simultaneously, dramatically increasing processing speed while reducing the need for complex electronic computation hardware.
Solution Approach 2:
The optical neural network acts as an intermediary component between the transmitter and detector array, processing the optical signals in real-time before detection. This intermediary processing stage extracts multiple physical characteristics concurrently, improving productivity without proportionally increasing overall system complexity.
2Measurement precision
If conventional detector subsystems with multiple detectors are used, then measurement precision can be achieved, but energy consumption increases
Solution Approach 1:
The patent merges multiple detection functions into a single optical neural network that simultaneously extracts multiple physical characteristics from the return light pulses. This consolidation maintains measurement precision for various features while reducing the total number of separate detectors and associated energy consumption.
Solution Approach 2:
The optical neural network performs self-processing of the optical signals, extracting multiple features directly from the light pulses without requiring separate detection and computation stages. This self-service approach reduces energy consumption by eliminating redundant processing steps while maintaining comprehensive measurement capabilities.
3Loss of information
If predetermined algorithms are used for feature extraction, then device complexity remains low, but information completeness is lost
Solution Approach 1:
The patent transitions from conventional one-dimensional sequential processing to multi-dimensional parallel processing within the optical neural network. This dimensional change enables simultaneous extraction of multiple physical characteristics (distance, velocity, temperature, etc.) from the same light pulses, preserving comprehensive information while managing complexity through optical parallelism.
Data Source
AI summary
A sensor system includes a transmitter configured to emit electromagnetic radiation towards a portion of an environment and an optical neural network configured to receive a reflection of the electromagnetic radiation from the portion of the environment and generate an array of electromagnetic signals. A property of each respective electromagnetic signal of the array of electromagnetic signals represents a corresponding physical characteristic of the portion of the environment. The sensor system also includes a detector array configured to receive the array of electromagnetic signals and including a plurality of electromagnetic signal detectors. Each respective electromagnetic signal detector is configured to measure the property of a corresponding electromagnetic signal of the array of electromagnetic signals and generate, based on the measured at least one property of the corresponding electromagnetic signal, a value representing the corresponding physical characteristic.