Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges

Abstract

Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light conditions. However, the rising number of transmitters and receivers for obtaining a higher angular resolution increases the cost for digital signal processing. One promising approach for energy-efficient signal processing is the usage of brain-inspired spiking neural networks (SNNs) implemented on neuromorphic hardware. In this article we perform a step-by-step analysis of automotive radar processing and argue how spiking neural networks could replace or complement the conventional processing. We provide SNN examples for two processing steps and evaluate their accuracy and computational efficiency. For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead. Instead, our SNN for target classification achieves an accuracy close to a reference artificial neural network while requiring 200 times less operations. Finally, we discuss the specific requirements and challenges for SNN-based radar processing on neuromorphic hardware. This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles.

A review of non-cognitive applications for neuromorphic computing

“…Few works have also explored spike signal processing from the point of view of novel materials' research, e.g., Ganguly et al, have explored the use of a stochastic analog neuron based on spintronic materials in signal processing tasks such as channel equalization [95]. Neuromorphic approaches are also increasingly being used in assisted and autonomous driving applications to pre-process signals acquired from RADAR and LiDAR sensors [92,[96][97][98]. A great opportunity exists to take inspiration from the signal processing mechanisms in biology such as echolocation to design neuromorphic systems that are highly performant and accurate.…”

“…Shalumov et al, have employed different bio-inspired models of spiking neurons [98]. Vogginger et al, have successfully employed RF neurons with temporal coding to carry out the Fourier transform of the radar signal to discern the location and speed of the target object [96]. SNNs have also been applied in processing the LiDAR data in real-time for object detection and collision avoidance in autonomous driving applications [97,98].…”

Integrate-and-fire circuit for converting analog signals to spikes using phase encoding *

“…However, energy and time efficiency becomes more relevant for low-level neuromorphic applications that directly process sensor data. This is the case of embedded systems where the pool of energy is limited, such as automotive applications [20,21]. There are recent examples of neuromorphic computing algorithms that deal with low-end tasks and are applied to sensor data, such as LiDAR [20], event-based cameras [22], FMCW radar [21], electrocardiogram signals [23], or microphones [24].…”

On-Chip Adaptive Implementation of Neuromorphic Spiking Sensory Systems with Self-X Capabilities

“…As a result, the time resolution improves with CMOS scaling, leading to a growing interest in the time domain, as reported by [8,9]. This motivated the researchers to create electronic sensor systems that use spike or time-coded signals, which possess a technology-agnostic property that remains robust even as technology scales up, as demonstrated in [10][11][12][13][14][15]. A scalable ADC based on the neural engineering framework was proposed by the authors in [10].…”