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

Curvilinear Motion: Normal and Tangential Components01:27

Curvilinear Motion: Normal and Tangential Components

462
When a car traverses a curved road, its motion can be elucidated by breaking it down into tangential and normal components. The car-centric coordinates attached to the vehicle move with it.
The positive direction of the t-axis aligns with the increasing position of the car along the curved path, denoted by the unit vector ut. Simultaneously, the n-axis, perpendicular to the t-axis, dissects the curved path into differential arc segments, each forming the arc of a circle with a radius of...
462
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

271
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
271
Velocity and Position by Integral Method01:13

Velocity and Position by Integral Method

6.3K
If acceleration as a function of time is known, then velocity and position functions can be derived using integral calculus. For constant acceleration, the integral equations refer to the first and second kinematic equations for velocity and position functions, respectively.
Consider an example to calculate the velocity and position from the acceleration function. A motorboat is traveling at a constant velocity of 5.0 m/s when it starts to decelerate to arrive at the dock. Its acceleration is...
6.3K
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

13.6K
When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
13.6K
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

623
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
623
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

429
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
429

You might also read

Related Articles

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

Sort by
Same author

Geospatial and econometric approaches or older driver safety: Analysis of crash injury severity of regional highways.

PloS one·2025
Same author

Investigating injury severity interaction between urban and urban-rural fringe areas: a grouped random parameters seemingly unrelated bivariate probit approach.

International journal of injury control and safety promotion·2023
Same author

Data-driven crash prediction by injury severity using a recurrent neural network model based on Keras framework.

International journal of injury control and safety promotion·2023
Same author

Acute liver failure associated with human adenovirus infection after allogeneic hematopoietic stem cell transplantation.

Annals of hematology·2023
Same author

Investigating interaction pattern between urban-rural integration and transport network: A dynamic evolution model.

PloS one·2022
Same author

An improved method to build lung cancer PDX models by surgical resection samples and its association with B7-H3 expression.

Translational cancer research·2022

Related Experiment Video

Updated: Sep 9, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K

A Survey on Vehicle Trajectory Prediction Procedures for Intelligent Driving.

Tingjing Wang1, Daiquan Xiao2, Xuecai Xu2

  • 1School of Architectural Engineering, Zhejiang College of Construction Technology, Hangzhou 311215, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary

This survey reviews intelligent driving trajectory prediction, covering perception, core prediction technologies, decision-making, and applications. It offers insights for advancing vehicle trajectory prediction methods and real-world implementation.

Keywords:
application scenariodecision-makingdeep learningintelligent drivingvehicle trajectory prediction

More Related Videos

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

1.1K
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

3.4K

Related Experiment Videos

Last Updated: Sep 9, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

1.1K
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

3.4K

Area of Science:

  • Intelligent Transportation Systems
  • Autonomous Driving Technology
  • Computer Vision

Background:

  • Vehicle trajectory prediction is crucial for intelligent driving safety and efficiency.
  • Existing reviews often lack a comprehensive theoretical and practical perspective.

Purpose of the Study:

  • To provide a thorough review of vehicle trajectory prediction procedures in intelligent driving.
  • To categorize and analyze current methods from perception to application.
  • To identify future research directions.

Main Methods:

  • Categorization of trajectory prediction methods into short-term (physics-based, machine learning) and long-term (deep learning, intention-based) domains.
  • Analysis of perception layer technologies (sensors, visual, multimodal fusion).
  • Review of decision-making layers (cooperation, closed-loop, real-time optimization) and scenario applications (open, closed).

Main Results:

  • Detailed enumeration of sensors and perception techniques used in leading intelligent vehicles.
  • Summary of core trajectory prediction algorithms and decision-making strategies.
  • Discussion of open and closed scenario applications and their practical considerations.

Conclusions:

  • The study synthesizes current knowledge on vehicle trajectory prediction for intelligent driving.
  • It highlights key technologies and challenges in perception, prediction, and decision-making.
  • Future research should focus on data collection, method generalization, and real-world validation.