LiDAR Waveform Embedding for Richer Feature Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional LIDAR systems for autonomous vehicles can only measure a subset of physical properties in an environment using predetermined equations, leaving additional information in waveforms unutilized, which limits the accuracy and completeness of environmental mapping and feature detection.
Innovation Solution
An embedding model is employed to generate vectors representing additional physical properties from LIDAR waveforms, which are then combined with traditional signal processing outputs to enhance feature detection and classification in autonomous navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If predetermined equations are used to extract physical properties from LIDAR waveforms, then the measurement process is simple and fast, but only a subset of physical properties can be measured, leaving information unutilized
Solution Approach 1:
An embedding model is introduced as an intermediary component between the LIDAR waveform data and the feature detection system. This embedding model processes the raw waveform information and generates enhanced feature representations, allowing more complete utilization of the waveform data while maintaining system modularity and manageable complexity.
Solution Approach 2:
The system transforms the waveform data through parameter changes by using the embedding model to generate new feature parameters that capture additional physical properties. This transformation enables extraction of more information from the same waveform input without requiring additional sensors or fundamentally changing the LIDAR hardware.
2Measurement precision
If more physical properties are measured using an embedding model, then feature detection accuracy improves, but processing complexity increases
Solution Approach 1:
The system replaces traditional mechanical signal processing methods with a data-driven embedding model approach. Instead of using complex mathematical equations and signal processing algorithms to extract features, the embedding model learns the transformations directly from data, achieving higher measurement precision while the computational complexity is managed through efficient neural network architectures.
3Adaptability or versatility
If conventional LIDAR systems are used, then the system structure is simple, but the completeness of environmental mapping is limited
Solution Approach 1:
The embedding model serves multiple functions within the LIDAR system: it enhances feature detection accuracy, extracts additional physical properties, and improves environmental mapping completeness. This multi-functionality is achieved through a single integrated component that processes waveform data to generate comprehensive feature representations usable across different application scenarios.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The embedding model allows for the utilization of more information in LIDAR waveforms, improving the accuracy of environmental mapping and feature detection, enabling better navigation and obstacle recognition for autonomous vehicles.
Implementation Method 1
At least a portion of the light pulses may be redirected back toward the LIDAR (e.g., due to reflection or scattering)
Implementation Method 2
At least a portion of the light pulses may be redirected back toward the LIDAR (e.g., due to reflection or scattering)
Implementation Method 3
The distance between the LIDAR device and a given object may be determined based on a time of flight of the corresponding light pulses
Data Source
AI summary
A system includes a light detection and ranging device configured to generate, for each respective point of a plurality of points in an environment, a corresponding waveform that represents physical characteristics of the respective point. The system also includes a signal processor configured to determine, based on the corresponding waveform of each respective point, a map of the environment that includes a representation of a corresponding position of the respective point. The system additionally includes an embedding model configured to determine, for each respective point and based on the corresponding waveform, a corresponding vector comprising a plurality of values representative of the physical characteristics of the respective point. The system further includes a feature detector configured to detect or classify a physical feature based on (i) the corresponding positions of one or more points of the plurality of points and (ii) the corresponding vectors of the one or more points.