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

Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

305
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...
305
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

107
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
107
Machines: Problem Solving II01:30

Machines: Problem Solving II

303
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
303
Stereotype Content Model02:16

Stereotype Content Model

14.0K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.0K
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

552
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
552
Machines: Problem Solving I01:22

Machines: Problem Solving I

310
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
310

You might also read

Related Articles

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

Sort by
Same author

Effortless creation of safe robots from modules through self-programming and self-verification.

Science robotics·2020
Same author

The regional oxygen saturation of pituitary adenomas is lower than that of the pituitary gland: microspectrophotometric study with potential clinical implications.

Neurosurgery·2003
Same journal

Cascaded Safety Analysis and Test Scenario Generation Techniques for Autonomous Driving: A Case Study with WATonoBus.

Automotive innovation·2025
Same journal

Primary and Secondary Emissions Reduction Using Cylinder Deactivation Strategies for Gasoline Direct Injection Engines in Hybrid Vehicles.

Automotive innovation·2025
Same journal

Proton Exchange Membrane (PEM) Fuel Cells with Platinum Group Metal (PGM)-Free Cathode.

Automotive innovation·2021
Same journal

Special Issue on Automotive Lightweight.

Automotive innovation·2021
See all related articles

Related Experiment Video

Updated: Jun 15, 2025

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
06:38

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

4.8K

Scenario Factory 2.0: Scenario-Based Testing of Automated Vehicles with CommonRoad.

Florian Finkeldei1, Christoph Thees1, Jan-Niklas Weghorn1

  • 1TUM School of Computation, Information and Technology (CIT), Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany.

Automotive Innovation
|June 13, 2025
PubMed
Summary
This summary is machine-generated.

Scenario Factory 2.0 enhances automated vehicle testing by unifying scenario generation techniques and introducing novel simulation modes. This tool efficiently creates realistic test scenarios to improve automated driving system safety and performance.

Keywords:
Automated vehiclesAutonomous driving safetyCommonRoadMicroscopic traffic simulationOpenTrafficSimSUMOScenario Factory 2.0Scenario-based testing

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

17.4K
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

4.4K

Related Experiment Videos

Last Updated: Jun 15, 2025

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
06:38

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

4.8K
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

17.4K
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

4.4K

Area of Science:

  • Robotics and Autonomous Systems
  • Computer Science
  • Transportation Engineering

Background:

  • Scenario-based testing is crucial for validating automated driving systems.
  • Generating realistic and relevant test scenarios remains a significant challenge.
  • Existing methods often lack flexibility and efficient integration with traffic simulators.

Purpose of the Study:

  • To introduce Scenario Factory 2.0, an advanced tool for automated driving system scenario generation.
  • To unify diverse scenario generation techniques within a modular framework.
  • To enable tunable similarity scenario generation through novel simulation modes.

Main Methods:

  • Integration of multiple scenario generation techniques from the CommonRoad framework.
  • Development of simulation modes for coupling with OpenTrafficSim and SUMO.
  • Implementation of a modular architecture for flexible scenario creation.
  • Scenario generation from formal specifications and falsification techniques.

Main Results:

  • Scenario Factory 2.0 successfully unifies existing techniques and introduces new simulation modes.
  • The tool enables generation of scenarios with tunable similarity to existing ones.
  • Demonstrated effectiveness in generating diverse traffic scenarios and a practical use case.

Conclusions:

  • Scenario Factory 2.0 provides an effective and flexible solution for generating realistic test scenarios for automated vehicles.
  • The novel simulation modes enhance the ability to create targeted and relevant test cases.
  • The open-source availability promotes further research and development in automated driving validation.