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

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

3.7K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
3.7K
Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

41
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
41
Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

13.5K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
13.5K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

4.8K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
4.8K
Vertical Curve: Problem Solving01:23

Vertical Curve: Problem Solving

37
Vertical curves provide the transition between two roadway grades, ensuring safety, comfort, and functionality. Calculating elevations at specific stations along the curve involves several systematic steps based on the curve's geometry and provided design parameters.The vertical curve is defined by its length, grades, Point of Vertical Intersection (P.V.I.) location, and P.V.I. elevation. The stations of the Point of Vertical Curvature (P.V.C.), where the curve begins, and the Point of Vertical...
37
Horizontal Curve: Problem Solving01:03

Horizontal Curve: Problem Solving

44
A horizontal curve is characterized by its radius, intersection angle, and stationing of key points. In this case, the radius is 400 meters, and the angle of intersection is 30 degrees, with the station of the point of curvature (P.C.) at 0 + 150 meters. The goal is to determine the station values at the point of intersection (P.I.), point of tangency (P.T.), and midpoint of the curve, as well as the length of the long chord.The process begins with calculating the tangent distance (T) and the...
44

You might also read

Related Articles

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

Sort by
Same author

Low-Temperature Deposition of Polycrystalline ε-Ga<sub>2</sub>O<sub>3</sub> for Deep Ultraviolet Perceptual Photodetection.

The journal of physical chemistry letters·2026
Same author

Roles and therapeutic prospects of the laminin family in disorders of the nervous system.

Journal of translational medicine·2026
Same author

Targeting melanocortin-4 receptor (MC4R) family in goldfish (Carassius auratus) for enhanced growth and optimized lipid distribution.

Molecular and cellular endocrinology·2026
Same author

Heat-assisted hot-hole transfer increases the surface-enhanced Raman activity of Au-TiO<sub>2</sub> nanoarrays.

Nature communications·2026
Same author

High-power LG laser generation based on a side-pumped Nd:YAG module cavity.

Applied optics·2026
Same author

S-scheme polyphenylguanidine / 2D-Bi<sub>2</sub>WO<sub>6</sub> heterojunction photocatalytic composite membrane: H<sub>2</sub> production and Congo red degradation.

Journal of environmental management·2026

Related Experiment Video

Updated: Jun 7, 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

4.4K

V-FCW: Vector-based forward collision warning algorithm for curved road conflicts using V2X networks.

Xiangpeng Cai1, Bowen Lv2, Hanchen Yao3

  • 1College of Transportation and Navigation, Quanzhou Normal University, Donghai Street 398, Quanzhou 362046, Fujian, China.

Accident; Analysis and Prevention
|November 21, 2024
PubMed
Summary
This summary is machine-generated.

A new vector-based forward collision warning (V-FCW) algorithm improves traffic safety by accurately detecting vehicles on curved roads. This advanced driver assistance system (ADAS) reduces false warnings, enhancing intelligent driving systems.

Keywords:
Cellular vehicle-to-everything (C-V2X)Curved road conflictForward collision warning

More Related Videos

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

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

Published on: February 1, 2020

20.3K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.2K

Related Experiment Videos

Last Updated: Jun 7, 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

4.4K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

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

Published on: February 1, 2020

20.3K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.2K

Area of Science:

  • Intelligent Transportation Systems
  • Automotive Engineering
  • Computer Science

Background:

  • Advanced driver assistance systems (ADAS), particularly forward collision warning (FCW) algorithms, are crucial for traffic accident prevention.
  • Existing FCW algorithms struggle with accurately determining vehicle distances on curved roads, leading to persistent traffic conflicts.
  • The need for enhanced algorithms that can handle complex road geometries is critical for improving road safety.

Purpose of the Study:

  • To propose and evaluate a novel vector-based forward collision warning (V-FCW) algorithm designed to overcome the limitations of current FCW systems on unconventional road sections.
  • To enhance the accuracy of distance and relative angle estimation between host vehicles (HV) and remote vehicles (RV) on both straight and curved roads.
  • To contribute to the advancement of vehicle-road cooperation within cellular vehicle-to-everything (C-V2X) enabled intelligent driving environments.

Main Methods:

  • The V-FCW algorithm utilizes vector relationships to estimate the poses (position, velocity, heading angle) of HV and RV via vehicle-to-vehicle (V2V) communication.
  • Lane localization is performed using vehicle-to-infrastructure (V2I) communication, supported by roadside unit (RSU)-based local maps.
  • The algorithm was implemented and tested on the Simcenter Prescan simulation platform and a cellular vehicle-to-everything (C-V2X) communication platform, including hardware-in-the-loop experiments.

Main Results:

  • Simulation results confirmed the V-FCW algorithm's capability to accurately identify and warn about dangerous vehicles on both straight and curved road segments.
  • Hardware-in-the-loop experiments demonstrated the algorithm's effectiveness in accurately forecasting four distinct warning levels under various road conditions.
  • The V-FCW algorithm significantly reduces false warnings, improving the reliability of collision avoidance systems.

Conclusions:

  • The proposed V-FCW algorithm effectively addresses the challenge of accurate vehicle distance estimation on curved roads, a key limitation in current ADAS.
  • The integration of V2V and V2I communication within the C-V2X framework enables robust performance of the V-FCW system.
  • This study represents a significant advancement in vehicle-road cooperation, paving the way for safer and more intelligent autonomous driving.