Point-Based Prediction System for Vehicle Scene Analysis
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Solution Overview
Problem
Existing vehicle systems face computational inefficiencies due to the resource-intensive nature of image-based prediction methods, which consume significant CPU, GPU, and memory resources, particularly in processing large images and performing convolutions, leading to limited resources available for other critical tasks.
Innovation Solution
A point-based prediction system that represents the vehicle environment as a point cloud, using a point-based neural network (PBNN) instead of Convolutional Neural Networks (CNNs), reducing memory usage and allowing for more efficient processing by only storing relevant points, thereby freeing up resources for increased sensor resolution and accuracy improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-based prediction methods using CNNs are used, then prediction accuracy can be achieved, but computational resource consumption (CPU, GPU, memory) increases significantly
Solution Approach 1:
The patent extracts only the relevant features from the environment and represents them as a point cloud, removing unnecessary image data. This extraction approach maintains prediction accuracy by preserving essential spatial and semantic information while eliminating redundant data that consumes computational resources during CNN processing.
Solution Approach 2:
The patent changes the representation parameters from high-resolution images to point clouds with specific features (position, size, orientation, type). This parameter transformation reduces the data dimensionality and complexity, enabling efficient processing while maintaining the necessary information for accurate predictions.
2Loss of information
If large images are processed for predictions, then more environmental details are captured, but memory usage and processing time increase
Solution Approach 1:
The patent extracts essential environmental features (position, size, orientation, type) and represents them as discrete points in a point cloud. This extraction maintains comprehensive environmental detail capture while dramatically reducing memory requirements compared to storing and processing large image files.
Solution Approach 2:
The patent segments the environment into discrete objects represented by points, where each point contains specific feature information. This segmentation approach allows efficient memory management by storing only relevant features for each object rather than processing entire large-scale images.
3Measurement precision
If Convolutional Neural Networks are used for processing, then spatial feature extraction is effective, but processing speed decreases due to computational intensity
Solution Approach 1:
The patent replaces the mechanical convolution operations of CNNs with direct processing of point cloud data using simpler algorithms. This substitution maintains spatial feature extraction capability by working directly with the geometric properties of points while eliminating the computationally intensive convolution process, thereby improving processing speed.
4Measurement precision
If high computational budget is allocated for predictions, then more accurate predictions are achieved, but resources for other critical tasks are reduced
Solution Approach 1:
The patent extracts only the essential features needed for prediction, representing the environment as a compact point cloud. This extraction reduces the computational budget required for predictions, thereby freeing up resources that can be allocated to other critical tasks while maintaining prediction accuracy through efficient use of the reduced computational budget.
Data Source
AI summary
In one embodiment, a method includes, by a computing system associated with a vehicle, receiving sensor data from one or more sensors of the vehicle, wherein the sensor data is based on an environment of the vehicle, identifying, based on the sensor data, one or more objects in the environment, generating, based on the one or more objects, a set of points that represent the environment, wherein each object has one or more corresponding points in the set of points, and each of the points is associated with one or more features associated with the corresponding object, generating a prediction for at least one of the objects in the environment or the vehicle by processing the set of points using a machine-learning model, and causing the vehicle to perform one or more operations based on the prediction.