Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

PD Controller: Design01:26

PD Controller: Design

409
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
409
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

538
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...
538
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

207
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
207

You might also read

Related Articles

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

Sort by
Same author

Evaluation of the safety improvement effects and adaptability of speed limit measures on downhill curved sections of mountainous freeways.

Accident; analysis and prevention·2026
Same author

Evaluating the safety and speed impacts of the 20mph speed limit in the UK: Evidence and insights.

Accident; analysis and prevention·2025
Same author

Heterogeneous and differential treatment effect analysis of safety improvements on freeways using causal inference.

Accident; analysis and prevention·2025
Same author

City-scale GPS data reveals impact of spatial configuration and dedicated infrastructure on e-scooter route choice.

Scientific reports·2025
Same author

Validating self-reported driving behaviours as determinants of real-world driving speeds.

Ergonomics·2024
Same author

A hybrid Machine learning and statistical modeling approach for analyzing the crash severity of mobility scooter users considering temporal instability.

Accident; analysis and prevention·2024

Related Experiment Video

Updated: Oct 24, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

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

Published on: December 18, 2020

4.6K

Developing personalised braking and steering thresholds for driver support systems from SHRP2 NDS data.

Evita Papazikou1, Pete Thomas1, Mohammed Quddus2

  • 1School of Design and Creative Arts, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK.

Accident; Analysis and Prevention
|August 15, 2021
PubMed
Summary

Researchers analyzed driver braking and steering data before crashes using Strategic Highway Research Program 2 Naturalistic Driving Studies (SHRP2 NDS). Some drivers did not react to impending events, highlighting the need for personalized Advanced Driver Assistance Systems (ADAS).

Keywords:
Braking and steering behaviourDriver support systemsPersonalised thresholdsSHRP2 NDS dataSafety critical events

More Related Videos

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects
11:12

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Published on: September 18, 2012

17.6K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

Related Experiment Videos

Last Updated: Oct 24, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

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

Published on: December 18, 2020

4.6K
Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects
11:12

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Published on: September 18, 2012

17.6K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

Area of Science:

  • Traffic Safety
  • Human Factors in Transportation
  • Automotive Engineering

Background:

  • Driver support systems aim to prevent crashes by understanding pre-crash behaviors.
  • Personalized pre-crash driver behavior analysis, specifically braking and steering, is crucial for developing effective crash prevention strategies.

Purpose of the Study:

  • To investigate personalized steering and braking thresholds in the final 30 seconds before safety-critical events.
  • To analyze driver behavior leading up to critical events using naturalistic driving data.
  • To recommend thresholds for detecting emerging safety-critical situations.

Main Methods:

  • Utilized Strategic Highway Research Program 2 Naturalistic Driving Studies (SHRP2 NDS) data.
  • Developed algorithms to extract braking and steering events from deceleration and yaw rate data.
  • Analyzed the sequence of maneuvers in the 30 seconds preceding safety-critical events.

Main Results:

  • Identified personalized steering and braking thresholds preceding safety-critical events.
  • Found that 20% of drivers did not react to impending events, indicating a lack of awareness.
  • Established insights into the transition from normal driving to safety-critical scenarios.

Conclusions:

  • Driver behavior analysis before safety-critical events offers valuable insights for crash prevention.
  • Future Advanced Driver Assistance Systems (ADAS) should incorporate tailored activation thresholds for individual drivers.
  • Developed algorithms and recommended thresholds can advance driver behavior analysis and support the design of new driver support systems.