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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...

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Related Experiment Video

Updated: Jun 3, 2025

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

Uncertainty-Aware Multimodal Trajectory Prediction via a Single Inference from a Single Model.

Ho Suk^1,2, Shiho Kim^1,2

¹Seamless Trans-X Lab (STL), School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.

Sensors (Basel, Switzerland)

|January 11, 2025

Summary

This summary is machine-generated.

This study introduces an uncertainty-aware multimodal trajectory prediction (UAMTP) model for autonomous driving. The UAMTP model efficiently quantifies uncertainties, improving safety and reaction time in complex driving scenarios.

Keywords:

advanced driver assistance systems autonomous driving edge platform multimodal trajectory prediction single forward pass uncertainty quantification

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Last Updated: Jun 3, 2025

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

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Published on: May 10, 2012

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Published on: February 25, 2013

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Published on: November 9, 2011

Area of Science:

Autonomous Driving
Robotics
Artificial Intelligence

Background:

Trajectory prediction is crucial for autonomous driving safety.
Existing methods often overlook uncertainty quantification, relying on computationally expensive deep ensembles.
Addressing uncertainty is vital for reliable navigation in complex environments.

Purpose of the Study:

To develop a novel uncertainty-aware multimodal trajectory prediction (UAMTP) model.
To quantify both aleatoric and epistemic uncertainties in a single forward inference.
To enhance computational efficiency and prediction accuracy for autonomous systems.

Main Methods:

The UAMTP model decomposes trajectory prediction into velocity and yaw components.
It quantifies uncertainties in both components to generate multimodal predictions.
The approach utilizes deterministic single forward pass methods for efficiency.

Main Results:

The UAMTP model outperforms Deep Ensembles in accuracy metrics like minFDE and miss rate.
It provides enhanced time to react for collision avoidance.
The model effectively accounts for environmental randomness and intention ambiguity.

Conclusions:

This research presents an efficient uncertainty quantification method for multimodal trajectory prediction.
The UAMTP model improves safety and reliability in autonomous driving.
The findings are significant for resource-constrained autonomous driving platforms.