CNN‐based estimation of heading direction of vehicle using automotive radar sensor

Abstract

Modern autonomous vehicles are being equipped with various automotive sensors to perform special functions. Especially, it is important to predict the heading direction of the front vehicle to adjust the speed of the ego-vehicle and select appropriate actions. Here, we propose a method for estimating the instantaneous heading direction of a vehicle using automotive radar sensor data. First, using a frequency-modulated continuous wave (FMCW) radar in the 77 GHz band, we accumulate the automotive radar sensor data for different movements of the front vehicle (e.g., stop, going ahead, reversing, turning left, and turning right). To distinguish the different movements of the vehicle, we use the convolutional neural network (CNN) and train it using the acquired radar sensor data. Because the CNN algorithm usually uses image data as input, it is essential to convert radar sensor data into image data. Therefore, we apply a high-resolution angle estimation algorithm to the obtained radar data and convert it into a two-dimensional range map. After the CNN model is trained with the obtained radar sensor data, various movements of the front vehicle can be classified with over 94% of accuracy.