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

167
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,...
167
PI Controller: Design01:24

PI Controller: Design

173
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
173
Controller Configurations01:22

Controller Configurations

81
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
81
Hybrid Zones02:29

Hybrid Zones

16.7K
Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
16.7K
PID Controller01:19

PID Controller

89
Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
89
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
93

You might also read

Related Articles

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

Sort by
Same author

Optimization of stimulus color for peripheral SSVEP-based brain-computer interfaces.

Frontiers in human neuroscience·2026
Same author

Cutting-edge cross-linking biomaterials advancing ophthalmic therapeutics.

Progress in retinal and eye research·2026
Same author

Interfacial Water Coordination on Ruthenium Oxide Nanoparticles Confined Within Covalent Organic Framework and Its Effect on Electrochemical Nitrate Reduction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Ultrasound-programmable tumor extracellular vesicles delivery system for dual immune-epigenetic therapy against colorectal cancer metastasis.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

Multi-omics profiling reveals EMT-driven fibroblast activation in the renal injury niche.

Cellular and molecular life sciences : CMLS·2026
Same author

Two-step distance tuning of T<sub>1</sub>-T<sub>2</sub> dual-activated magnetic resonance nanoprobe for enhanced tumor diagnosis.

Materials today. Bio·2026

Related Experiment Video

Updated: May 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

4.4K

Hybrid-Prediction Integrated Planning for Autonomous Driving.

Haochen Liu, Zhiyu Huang, Wenhui Huang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 3, 2025
    PubMed
    Summary

    Autonomous driving systems need better prediction and planning integration. The Hybrid-Prediction integrated Planning (HPP) framework improves decision-making by aligning motion forecasting and interactive dynamics for enhanced safety and accuracy.

    More Related Videos

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    11.5K
    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

    Related Experiment Videos

    Last Updated: May 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

    4.4K
    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    11.5K
    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

    Area of Science:

    • Autonomous Driving Systems
    • Artificial Intelligence
    • Robotics

    Background:

    • Autonomous driving requires accurate environmental understanding and prediction for safe decision-making.
    • Integrating prediction and planning in learning-based systems presents alignment challenges.
    • Existing methods struggle with consistency between prediction patterns and planning interactions.

    Purpose of the Study:

    • To introduce a novel framework, Hybrid-Prediction integrated Planning (HPP), addressing prediction-planning alignment challenges.
    • To enhance the consistency and interaction between prediction and planning modules in autonomous systems.
    • To improve the overall accuracy and safety of autonomous driving decision-making.

    Main Methods:

    • Developed a Hybrid-Prediction integrated Planning (HPP) framework with three collaborative modules.
    • Introduced marginal-conditioned occupancy prediction using the MS-OccFormer module for spatial-temporal alignment.
    • Proposed a game-theoretic motion predictor, GTFormer, to model agent interactions based on predictive awareness.
    • Integrated hybrid prediction patterns into the Ego Planner, optimized by prediction guidance.

    Main Results:

    • HPP achieved state-of-the-art performance on the nuScenes dataset in end-to-end configurations.
    • Demonstrated superior accuracy and safety compared to existing methods.
    • Showcased enhanced interactive open-loop and closed-loop planning on Waymo Open Motion Dataset (WOMD) and CARLA benchmarks.
    • Achieved improved consistency between prediction and planning.

    Conclusions:

    • The HPP framework effectively addresses the challenges of integrating prediction and planning in autonomous driving.
    • HPP demonstrates significant improvements in accuracy, safety, and consistency for autonomous decision-making.
    • The proposed modules and integration strategy set a new benchmark for autonomous driving systems.