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

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

Rolling Resistance: Problem Solving

Rolling Resistance: Problem Solving

Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...

Decision Making

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...

Decision Making: Traditional Method

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

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.

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

You might also read

Related Articles

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

Sort by

Same author

Enteropathy-associated T-cell lymphoma: molecular clonal evolution and its clinical consequences.

Haematologica·2026

Same author

Association between dietary patterns and sleep quality in Chinese children and adolescents: large-scale cross-sectional network analysis.

Frontiers in nutrition·2026

Same author

Cryptanalysis and improvement of a distributed zero trust scheme for airborne wireless sensor networks.

Scientific reports·2026

Same author

The state-dependent causal effect of a V2X-based beyond-line-of-sight warning system on young driver response: a causal machine learning approach.

Accident; analysis and prevention·2026

Same author

A fatty acid amide activates myeloid cells and improves neurovascular outcomes in retinal degeneration.

Nature neuroscience·2026

Same author

Impact of the compact county medical service community policy on healthcare resource allocation and healthcare service utilization in village clinics: a difference-in-differences analysis in rural Sichuan, China.

Frontiers in public health·2026

Search research articles

Related Experiment Video

Updated: Oct 12, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

A proactive lane-changing risk prediction framework considering driving intention recognition and different

Qiangqiang Shangguan¹, Ting Fu¹, Junhua Wang¹

¹The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, China; College of Transportation Engineering, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China.

Accident; Analysis and Prevention

|November 25, 2021

Summary

This study introduces a new framework for proactive lane-changing risk prediction by integrating driver intention recognition. It accurately predicts lane-changing intentions and risks, enhancing driving safety in advanced driver-assistance systems.

Keywords:

Driving intention recognition Lane-changing risk prediction Long Short-term Memory (LSTM)Trajectory data

More Related Videos

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Related Experiment Videos

Last Updated: Oct 12, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Area of Science:

Road safety
Artificial intelligence in transportation
Driver behavior analysis

Background:

Proactive lane-changing (LC) risk prediction is crucial for driver safety but often lacks timely intention recognition.
Existing models rarely differentiate risks between left and right lane changes.

Purpose of the Study:

To develop a proactive LC risk prediction framework integrating driver intention recognition.
To analyze differences in risk factors for left (LCL) and right (LCR) lane changes.
To identify key features influencing LC risk.

Main Methods:

Utilized Long Short-term Memory (LSTM) for driver intention recognition (LC, LCL, LCR, lane-keeping).
Employed Light Gradient Boosting Machine (LGBM) for LC risk prediction.
Validated the framework using the highD trajectory dataset.

Main Results:

Achieved high accuracy in intention recognition: 97% for LCL, 96% for LCR, and 97% for lane-keeping.
LGBM demonstrated superior performance over other machine learning algorithms for risk prediction.
Vehicle interaction characteristics in the current lane were identified as the most significant risk factors.

Conclusions:

The integrated framework effectively predicts LC intentions and risks.
The findings highlight the importance of intention recognition for practical LC risk assessment.
The framework offers potential for integration into advanced driver-assistance systems (ADAS) and autonomous driving for enhanced safety.