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Multi-input and Multi-variable systems

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.
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双雷达:一个多模式数据集,带有双4D雷达,用于自动驾驶.

Xinyu Zhang^1,2,3, Li Wang⁴, Jian Chen⁵

¹School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.

Scientific data

|March 14, 2025

概括

这项研究引入了一套新数据集,其中包括两种类型的4D雷达用于自动驾驶感知. 它可以在不同的环境条件下对4D雷达性能进行比较分析.

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科学领域:

自主驾驶系统自主驾驶系统
环境感知技术环境感知技术
传感器融合和数据分析

背景情况:

4D雷达为自动驾驶提供优越的点云密度和垂直分辨率,在恶劣条件下优于3D雷达.
然而,4D雷达的较高噪声水平需要不同的过策略,影响点云密度和噪声特征.
现有的数据集缺乏对不同4D雷达的比较分析,因为在相同的场景中捕获不同类型传感器的局限性.

研究的目的:

为自动驾驶领域的4D雷达研究引入一个新的,大规模的,多模式数据集.
为了促进在一致的环境条件下不同4D雷达系统的比较分析.
支持3D物体检测,跟踪和多式传感器融合任务的研究.

主要方法:

开发一个大规模的多模式数据集,包括151个序列 (主要是每一个20秒).
包括10,007个同步和注释的,捕捉不同的驾驶场景.
同时记录两种不同类型的4D雷达传感器的数据.

主要成果:

该数据集捕捉了各种具有挑战性的驾驶条件,包括不同的道路和天气条件,以及照明强度.
实验验证证明了数据集对于分析4D雷达性能和噪声特征的有用性.
该数据集提供了一个独特的资源,用于在相同的场景中比较不同的4D雷达传感器.

结论:

这一新型数据集能够对自动驾驶感知4D雷达技术进行重要的比较研究.
它通过包括多种4D雷达类型来解决现有数据集的局限性.
该资源将促进4D雷达的开发和理解,以实现强大的环境感知.