Abstract
In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training.
Highlights
Estimation in Millimeter-Wave RadarOne of the essential functions required for autonomous vehicles is to recognize and identify various objects on the road
By processing data acquired from automotive sensors, such as cameras, lidars, and radars, the type and location information of an object can be estimated
There is a need for a method that can compensate for the degradation of the camera’s recognition performance using other automotive sensors
Summary
One of the essential functions required for autonomous vehicles is to recognize and identify various objects on the road. A method of estimating the moving direction of a vehicle by applying a convolutional neural network (CNN) to the range-angle detection result was proposed in [10]. More advanced versions of YOLO networks were applied to radar sensor data to perform effective object detection [16,17]. We obtained the point cloud-based object detection result using a high-resolution radar system, and proposed a method to convert it into an image format suitable for training the CNN-based classifier. Based on the high-resolution radar sensor data, we designed a deep learning-based classifier that can determine the type of detected object and estimate its moving direction as well.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
