Sensor Data Uncertainty Modeling With Bayesian SDE Prediction
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Solution Overview
Problem
Existing methods for processing sensor data in applications like autonomous driving fail to effectively quantify and account for prediction uncertainties, which is crucial for making safe and informed control decisions.
Innovation Solution
The method involves modeling the flow dynamics as a stochastic differential equation (SDE) and using Bayesian neural networks (BNNs) to assign uncertainty to the drift and diffusion terms, thereby introducing additional stochasticity and improving prediction accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a deterministic neural network is used for regression analysis of sensor data, then the model is simple and computationally efficient, but it cannot express or quantify uncertainties in predictions
Solution Approach 1:
The patent transforms the deterministic neural network parameters into probabilistic parameters with distributions. Instead of fixed weights, the network uses distributions over weights, allowing it to express uncertainty in predictions while maintaining the same network architecture and computational framework.
Solution Approach 2:
The patent introduces dynamic behavior to the neural network by allowing the parameters to vary and be sampled from distributions during inference. This enables the model to adapt its behavior based on the input data, expressing different levels of uncertainty for different predictions.
2Reliability
If dropout is used to introduce stochasticity, then uncertainty can be captured, but manual dropout rate tuning is required and the solution family is limited
Solution Approach 1:
The patent enables the model to automatically learn and adjust its own stochastic behavior through the learned distribution parameters. The system self-adjusts the amount of stochasticity introduced based on the data, eliminating the need for manual tuning of dropout rates or other hyperparameters.
Solution Approach 2:
Instead of using fixed dropout rates, the patent employs dynamic stochasticity where the amount of noise injected is adaptively determined by the learned distribution parameters, allowing the model to optimize its own uncertainty expression without manual intervention.
Data Source
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AI summary
According to various embodiments, a method for processing sensor data is provided, the method comprising receiving input sensor data, determining, starting from the input sensor data as initial state, a plurality of end states, comprising determining, for each end state, a sequence of states, wherein determining the sequence of states comprises, for each state of the sequence beginning with the initial state until the end state, a first Bayesian neural network determining a sample of a drift term in response to inputting the respective state, a second Bayesian neural network determining a sample of a diffusion term in response to inputting the respective state and determining a subsequent state by sampling a stochastic differential equation comprising the sample of the drift term as drift term and the sample of the diffusion term as diffusion term. The method further comprises determining an end state probability distribution from the determined plurality of end states and determining a processing result of the input sensor data from the end state probability distribution.