Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

273
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...
273
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

150
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...
150
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

590
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
590
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

436
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...
436
Observational Learning01:12

Observational Learning

314
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
314
Associative Learning01:27

Associative Learning

579
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
579

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

H<sub>2</sub>S-mediated protein S-sulfhydration: a novel regulatory module in lipid metabolism.

Frontiers in cell and developmental biology·2026
Same author

Tension-band high-strength suture combined with absorbable screws with cortical penetration for treating Mayo type IIA olecranon fractures: finite element analysis, biomechanical testing, and clinical study.

Frontiers in bioengineering and biotechnology·2026
Same author

Targeting P2X receptor signaling for chronic visceral pain and beyond.

Neuropharmacology·2026
Same author

Therapeutic Effects of Shengdu Pingmu Formula on Loperamide-Induced Constipation in Rats via PI3K/AKT Signaling and Gut Microbiota Regulation.

Journal of visualized experiments : JoVE·2026
Same author

Diff-MomentFormer: Generative Diffusion-Augmented Transformer for End-to-End Joint Moment Estimation.

Sensors (Basel, Switzerland)·2026
Same author

Low-temperature topotactic conversion growth of porous CoFe<sub>2</sub>O<sub>4</sub> nanocubes for an advanced oxygen evolution reaction.

Chemical communications (Cambridge, England)·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Sep 13, 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

多任务轨迹预测使用车道解的有条件变量自编码器.

Haoyang Chen1, Na Li1, Hangguan Shan1

  • 1The College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.

Sensors (Basel, Switzerland)
|July 30, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了MS-SLV,这是一个用于自动驾驶轨迹预测的新生成框架. 它通过整合时间意识的场景编码和结构化的潜伏模型来增强多式预测,以获得更准确和更具上下文连贯性的预测.

关键词:
自动驾驶自动驾驶的自动驾驶.深度学习是一种深度学习.生成型模型的生成型模型.轨迹预测 轨迹预测

更多相关视频

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
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

5.0K

相关实验视频

Last Updated: Sep 13, 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
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
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

5.0K

科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能

背景情况:

  • 自动驾驶需要精确的轨迹预测,整合车辆动态和高清地图.
  • 目前的方法与动态的代理-场景交互作斗争,并由于静态编码和非结构化的潜空间产生多样化,连贯的预测.

研究的目的:

  • 开发MS-SLV,一个新的生成框架,解决当前轨迹预测方法的局限性.
  • 改进对不断变化的空间环境的建模,并为自动驾驶汽车制作多样化,背景一致的预测.

主要方法:

  • 引入了一个时刻感知场景编码器,将高清地图特征与车辆运动对齐.
  • 开发了一个结构化的潜伏模型来解开代理意图和场景约束.
  • 整合了一个辅助车道预测任务,以增强场景理解和潜在变量学习.

主要成果:

  • 通过MS-SLV实现了预测错误的显著降低:12.37%的平均位移错误 (ADE) 和7.67%的最终位移错误 (FDE).
  • 在多式联运预测方面取得了实质性的改进:失踪率@5 (MR5) 减少了26%,失踪率@10 (MR10) 减少了33%.
  • 与最强的基线相比,降低了3%的越野车速率 (ORR).

结论:

  • 该MS-SLV框架有效地模拟复杂的代理-场景相互作用,以改进轨迹预测.
  • 共同预测轨迹和车道序列可以提高可解释性和场景一致性.
  • 提出的方法显著提升了自动驾驶多式联运轨迹预测的最新技术.