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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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 of...
Observational Learning01:12

Observational Learning

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 because...

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相关实验视频

Updated: Jul 15, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.6K

旅行追踪器:带有深度学习方法的驾驶员警报系统.

N L Yashaswini1, Vanishri Arun1, B M Shashikala2

  • 1Department of Information Science and Engineering, JSS Science and Technology University, Mysuru, Karnataka, India.

Frontiers in robotics and AI
|October 21, 2024
PubMed
概括

一个新的行程追踪系统使用EfficientNet来检测驾驶员的昏昏欲睡,提高公共交通安全. 这个人工智能系统实现了95%的准确性,提醒司机,并允许对提高专业性的处罚.

关键词:
基线行为基本情况.定制的 EfficientNet 效率网络媒体研究实验室 媒体研究实验室学生检测 学生检测动激活功能的功能

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Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
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相关实验视频

Last Updated: Jul 15, 2026

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 运输安全运输安全

背景情况:

  • 司机昏昏欲睡是公共交通中的重大安全风险.
  • 现有的系统可能缺乏准确性或实时监控能力.

研究的目的:

  • 开发和评估一个人工智能驱动的行程追踪系统,用于检测司机的昏昏欲睡.
  • 加强公共交通安全和司机问责制.

主要方法:

  • 在媒体研究实验室 (MRL) 的眼睛数据集上训练了一种定制的EfficientNet模型.
  • 眼部区域被隔离,反射被过,并建立了基线行为.
  • 使用了"swish"激活功能和从头开始训练的模型.

主要成果:

  • 该系统在检测驾驶员昏昏欲睡时达到95%的准确率.
  • "切换"激活功能表现优于其他测试的功能.
  • 从头开始训练的模型比预先训练的模型表现更好.

结论:

  • 开发的系统有效监测驾驶员的警觉性,并检测昏昏欲睡.
  • 这项技术促进了更安全的公共交通和专业的驾驶员行为.
  • 个性化数据和学生检测增强了警报和惩罚触发器.