Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Probability Histograms

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.

Residual Plots

Residual Plots

A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...

Root-Locus Method

Root-Locus Method

A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block diagram,...

Residuals and Least-Squares Property

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Novel Counteraction Effect of H<sub>2</sub>O and SO<sub>2</sub> toward HCl on the Chemical Adsorption of Gaseous Hg<sup>0</sup> onto Sulfureted HPW/γ-Fe<sub>2</sub>O<sub>3</sub> at Low Temperatures: Mechanism and Its Application in Hg<sup>0</sup> Recovery from Coal-Fired Flue Gas.

Environmental science & technology·2021

Same author

An Early-Onset Advanced Rectal Cancer Patient With Increased KRAS Gene Copy Number Showed A Primary Resistance to Cetuximab in Combination With Chemotherapy: A Case Report.

Frontiers in oncology·2021

Same author

Construction of a risk assessment model of cardiovascular disease in a rural Chinese hypertensive population based on lasso-Cox analysis.

Journal of clinical hypertension (Greenwich, Conn.)·2021

Same author

Low fat mass index outperforms handgrip weakness and GLIM-defined malnutrition in predicting cancer survival: Derivation of cutoff values and joint analysis in an observational cohort.

Clinical nutrition (Edinburgh, Scotland)·2021

Same author

Development and validation of a Modified Patient-Generated Subjective Global Assessment as a nutritional assessment tool in cancer patients.

Journal of cachexia, sarcopenia and muscle·2021

Same author

Structural Basis of Pore Formation in the Mannose Phosphotransferase System by Pediocin PA-1.

Applied and environmental microbiology·2021

Search research articles

Related Experiment Videos

Passenger ride comfort estimation based on the recurrence plot technique and convolutional neural network.

Li Ma^1,2, Tingting Lan³, Rui Fu^3,4

¹School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China.

|May 18, 2026

Summary

This study introduces a new method for estimating passenger ride comfort in autonomous vehicles by fusing human and vehicle data. The approach achieves high accuracy in identifying discomfort states, improving safety and passenger experience.

Keywords:

Human factors deep learning recurrence plot technique ride comfort

Related Experiment Videos

Area of Science:

Automotive Engineering
Human-Computer Interaction
Machine Learning

Background:

Passenger ride comfort is a critical metric for autonomous vehicle (AV) development.
Current comfort estimation methods using single-source data and traditional machine learning have limitations in capturing complex human-vehicle interactions.

Purpose of the Study:

To develop an advanced passenger ride comfort state estimation method by integrating multi-source human-vehicle data.
To overcome the limitations of existing methods in accurately classifying comfort states.

Main Methods:

Utilized recurrence plot (RP) technique to transform multi-source time-series data (vehicle acceleration, passenger posture, individual characteristics) into 2D images.
Employed a 2D convolutional neural network (CNN) for training on image data to identify comfort states (discomfort/no-discomfort).

Main Results:

The proposed fusion-based method significantly improved state estimation accuracy compared to traditional approaches.
Achieved a high passenger ride comfort state estimation accuracy of 94.04%.

Conclusions:

The fusion of human-vehicle data using RP and CNN offers a superior approach for accurate passenger comfort state estimation in AVs.
This method enhances data utilization and captures intricate local temporal structures for better perception of subjective comfort.