The development of advanced autonomous driving applications is hindered by the complex temporal structure of sensory data, as well as by the limited computational and energy resources of their on-board systems. Currently, neuromorphic engineering is a rapidly growing field that aims to design information processing systems similar to the human brain by leveraging novel algorithms based on spiking neural networks (SNNs). These systems are well suited to recognize temporal patterns in data while maintaining a low energy consumption and offering highly parallel architectures for fast computation. However, the lack of effective algorithms for SNNs impedes their wide usage in mobile robot applications. This paper addresses the problem of radar signal processing by introducing a novel SNN that substitutes the discrete Fourier transform and constant false-alarm rate algorithm for raw radar data, where the weights and architecture of the SNN are derived from the original algorithms. We demonstrate that our proposed SNN can achieve competitive performance compared to that of the original algorithms in simulated driving scenarios while retaining its spike-based nature.

中文翻译：

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尖刺神经网络用于汽车雷达的傅立叶变换和目标检测

先进的自动驾驶应用的发展受到感官数据的复杂时间结构以及其车载系统有限的计算和能源资源的阻碍。当前，神经形态工程是一个快速发展的领域，旨在通过利用基于尖峰神经网络（SNN）的新颖算法来设计类似于人脑的信息处理系统。这些系统非常适合识别数据中的时间模式，同时保持较低的能耗，并提供高度并行的体系结构以进行快速计算。但是，缺乏有效的SNN算法，阻碍了它们在移动机器人应用中的广泛使用。本文通过引入一种新颖的SNN来解决雷达信号处理的问题，该SNN将离散傅里叶变换和恒定的虚警率算法替换为原始雷达数据，而SNN的权重和体系结构是从原始算法中得出的。我们证明，在模拟驾驶场景中，与原始算法相比，我们提出的SNN可以实现竞争性能，同时保留其基于尖峰的特性。