DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification

TL;DR

DeepHybrid combines classical radar processing with deep learning and neural architecture search to improve object classification accuracy and efficiency in automotive radar systems, aiding autonomous vehicle safety.

Contribution

It introduces a hybrid radar data model and employs NAS to create a compact, high-performing neural network for automotive object classification.

Findings

Hybrid radar spectra and reflection attributes improve classification accuracy.

Neural architecture search produces a significantly smaller network with comparable performance.

Method enhances safety features like emergency braking and collision avoidance.

Abstract

Automated vehicles need to detect and classify objects and traffic participants accurately. Reliable object classification using automotive radar sensors has proved to be challenging. We propose a method that combines classical radar signal processing and Deep Learning algorithms. The range-azimuth information on the radar reflection level is used to extract a sparse region of interest from the range-Doppler spectrum. This is used as input to a neural network (NN) that classifies different types of stationary and moving objects. We present a hybrid model (DeepHybrid) that receives both radar spectra and reflection attributes as inputs, e.g. radar cross-section. Experiments show that this improves the classification performance compared to models using only spectra. Moreover, a neural architecture search (NAS) algorithm is applied to find a resource-efficient and high-performing NN. NAS yields an almost one order of magnitude smaller NN than the manually-designed one while preserving the accuracy. The proposed method can be used for example to improve automatic emergency braking or collision avoidance systems.