Deep Neural Network-Based In-Vehicle People Localization Using Ultra-Wideband Radar

Sohee Lim,Seongwook Lee,Seong-Cheol Kim,Jaehoon Jung

doi:10.1109/access.2020.2997033

Abstract

In this paper, we present an in-vehicle people localization technique using a deep neural network (DNN) model that is trained by the experimental data. First, an impulse radio ultra-wide band (IR-UWB) radar is installed inside the vehicle, and received signals are acquired by changing the arrangement of people sitting. Then, on the acquired data, we apply the DNN to train a classifier, which can predict whether a person is sitting or not in each seat. To design a network suitable for our system, we evaluate the performance by changing the type of activation function, the number of layers, and the number of nodes in each hidden layer of the DNN. In addition, we compare the performance of the proposed method with conventional machine learning algorithms such as support vector machine (SVM) and decision tree-based methods. From our measured signals, the proposed DNN-based method can classify all possible cases according to the location and number of people with an accuracy of 99%. Moreover, the advantage of our proposed method is that there is no need to extract features from a given radar signal.

Highlights

In recent years, there has been a growing interest in enhancing transportation safety and providing convenience to the general public
Unlike the method of [15], because our proposed method is not based on feature extraction, we do not need a deep understanding of radar signals
Preprocessed radar signals were used as an input to the classifier

Summary

INTRODUCTION

There has been a growing interest in enhancing transportation safety and providing convenience to the general public. Hand-based gesture recognition technique inside a vehicle was suggested by [5]–[7] to help the driver perform various tasks while driving In these works, sensors such as radio-frequency identification (RFID), radar, and data-gloves were used to identify specific hand gestures. S. Lim et al.: DNN-Based In-Vehicle People Localization Using Ultra-Wideband Radar sensor, but the method required extracting features from the received data. We propose a deep learning-based method for estimating the location of people inside vehicle using a single IR-UWB radar sensor. Unlike the method of [15], because our proposed method is not based on feature extraction, we do not need a deep understanding of radar signals It has the advantage of being able to monitor people inside the vehicle without compromising people’s privacy. By combining the results from each sampler, it has an equivalent effect of sampling per Ts seconds and fast sampling can be achieved

PRE-PROCESSING OF IR-UWB RADAR SIGNALS

Findings

CONCLUSION