Deterministic Latent Feature Sampling for Traffic Scene Prediction
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Solution Overview
Problem
Classic prediction methods for future developments in traffic scenes, such as those used in traffic control systems and automated vehicles, are limited in modeling interactions between road users and provide short-term predictions, often resulting in suboptimal inference quality due to random sampling of latent features, which can lead to unspecific or distorted output sets.
Innovation Solution
The method involves deterministic or semi-deterministic sampling of latent features in a raster-like manner, ensuring that essential regions of the probability distribution are accounted for, even with a limited number of samples, using techniques like unscented Kalman filter or Gauss-Hermite quadrature Kalman filter sampling, to improve inference quality and approximation of the underlying probability distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If random sampling of latent features is used in VAE/CVAE architectures, then the approximation of the underlying probability distribution improves with more samples, but the processing time increases and inference becomes non-reproducible
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing the parameters of the multivariate probability distribution (mean vector and covariance matrix) from the encoder network before sampling. This allows the system to perform deterministic sampling using unscented Kalman filter or Gauss-Hermite quadrature methods, which systematically select sample points based on the distribution parameters rather than random selection. The sampling points and their weights are determined in advance according to the probability distribution characteristics, enabling reproducible inference with controlled computational effort.
2Measurement precision
If random sampling of latent features is used, then more samples improve the approximation of the probability distribution, but the inference results become non-reproducible across different runs
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing the parameters of the multivariate probability distribution (mean vector and covariance matrix) from the encoder network before sampling. This allows the system to perform deterministic sampling using unscented Kalman filter or Gauss-Hermite quadrature methods, which systematically select sample points based on the distribution parameters rather than random selection. The sampling points and their weights are determined in advance according to the probability distribution characteristics, enabling reproducible inference with controlled computational effort.
Solution Approach 2:
The patent replaces the random mechanical sampling process with a deterministic mathematical sampling approach. Instead of using random number generators to select samples from the latent feature distribution, the system employs unscented Kalman filter or Gauss-Hermite quadrature methods that deterministically compute optimal sample points and weights based on the distribution's mean and covariance. This substitution of random mechanical processes with deterministic mathematical algorithms ensures both reproducibility and accuracy in the inference results.
3Productivity
If a limited number of samples are processed due to processing time constraints, then the computational effort is manageable, but the approximation of the underlying probability distribution becomes suboptimal
Solution Approach 1:
The patent applies parameter changes by transforming the sampling problem from the original high-dimensional latent feature space to a parameterized representation using the mean vector and covariance matrix of the multivariate probability distribution. By changing the parameters from individual latent features to distribution parameters (mean and covariance), the system can efficiently compute optimal sample points and weights using unscented Kalman filter or Gauss-Hermite quadrature methods. This parameter transformation enables accurate probability distribution approximation with a limited number of samples, maintaining inference quality while respecting computational constraints.
Data Source
AI summary
A computer-implemented method for predicting future developments of a traffic scene includes aggregating scene-specific information about a traffic scene, and using a pre-trained encoder network to transform the aggregated scene-specific information into parameters of a multivariate probability distribution of latent features. The method further includes selecting samples of the multivariate probability distribution of latent features determined by the parameters, and using a pre-trained decoder network to transform each of the selected samples into an output set. The samples are selected deterministically, such that each selected sample represents a separate region of the multivariate probability distribution of the latent features, and the multivariate probability distribution of latent features is sampled in a raster-like manner via the totality of the selected samples.