Abstract
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 results compared to that of the original algorithms in simulated driving scenarios while retaining its spike-based nature.
Highlights
Autonomous driving is a billion-dollar business with high demand for efficient computing systems
We present a novel spiking neural networks (SNNs) that is able to effectively replace the discrete Fourier transform (FT) (DFT) and constant false-alarm rate (CFAR) algorithms (Rohling, 1983)
The first two objects represent two pedestrians, whereas the last target represents a vehicle. These values are based on the typical velocities of such targets, and the radar cross section (RCS) values are obtained from previous studies (Kamel et al, 2017; Deep et al, 2020)
Summary
Autonomous driving is a billion-dollar business with high demand for efficient computing systems. The algorithms in this field are typically implemented on graphical processing units (GPUs), field-programmable gate arrays (FPGAs), or application-specific integrated circuits (ASICs) (Lin et al, 2018), neuromorphic hardware (NHW) offers an efficient alternative environment (Furber et al, 2014; Davies et al, 2018; Sangwan and Hersam, 2020) It provides a low-energy-footprint platform for a new generation of neural networks called spiking neural networks (SNNs), which reduce the high energy consumption of the popular artificial neural networks (ANNs) (Maass and Schmitt, 1999; Bouvier et al, 2019; Strubell et al, 2019). A few recent works have explored the application of SNNs to decomposing a time signal into a frequency spectrum, e.g., by applying sequential spiking band-pass filters to audio signals (Jiménez-Fernández et al, 2016) or using neurons that spike at specific input frequencies (Auge and Mueller, 2020) The former offers an efficient bio-inspired solution, but its applications are limited to extracting a small set of frequency components. Finding SNN equivalents for all radar-processing stages is paramount, as hybrid pipelines introduce additional complexity through communication and spike conversion when data flows between the NHW and traditional hardware
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