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相关概念视频

Perception01:28

Perception

442
Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...
442
Parallel Processing01:20

Parallel Processing

146
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
146
High-Level and Low-Level Awareness01:19

High-Level and Low-Level Awareness

254
Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
254
Factors Affecting Perception01:25

Factors Affecting Perception

1.5K
Perception is influenced by perceptual set, context, motivation, and emotion. Perceptual set, or perceptual expectancy, refers to the tendency to perceive things in a particular way, influenced by previous experiences and expectations. This phenomenon affects the interpretation of stimuli, creating a set of mental tendencies and assumptions that impact sensory perceptions of sound, taste, touch, and sight.
An illustrative example of a perceptual set is the scenario where an airline pilot told...
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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
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对于自动驾驶的多任务环境感知方法

Ri Liu1, Shubin Yang1, Wansha Tang1

  • 1School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了YOLO-Mg,这是自动驾驶的先进环境感知模型. 它提高了对象,车道和可驾驶区域的检测精度,提高了整体系统的安全性和可靠性.

关键词:
自动驾驶自动驾驶的自动驾驶.从端到端,从一个到另一个端.环境 感知 环境 感知多任务网络网络多任务网络

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科学领域:

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

背景情况:

  • 自动驾驶系统在环境感知方面面临挑战,包括对小物体和复杂场景的不准确检测.
  • 目前的算法与特征冗余,有限的上下文交互和糟糕的信息融合扎,阻碍了多任务效率.

研究的目的:

  • 为自动驾驶开发一个端到端的多任务环境感知模型.
  • 为了同时执行交通物体检测,车道线路检测和可驾驶区域细分.

主要方法:

  • 实施了多阶段封闭聚合网络 (MogaNet),以加强道智能的上下文交互.
  • 引入了一个重组的加权双向特征金字塔网络 (BiFPN) 以优化多尺度对象检测.
  • 使用了一个统一的模型,其中有一个检测头和两个细分头,以实现高效的多任务处理.

主要成果:

  • 在BDD100K数据集上实现了对象检测的81.4%mAP50.
  • 在车道检测方面获得了28.9%的IOU,在可驾驶区域细分方面获得了92.6%的IOU.
  • 证明了有效的现实世界性能,显著改善了环境感知.

结论:

  • YOLO-Mg有效地解决了当前自动驾驶感知模型的局限性.
  • 该模型为更安全,更可靠的自动驾驶系统提供了坚实的基础.
  • 同时的多任务学习提高了整体环境感知能力.