In this paper, we propose a method for performing the simultaneous localization and mapping (SLAM) in indoor environments using only multiple-input and multiple-output frequency-modulated continuous wave radar. The SLAM is a technology that maps the surrounding environment and position of a platform simultaneously. The following are the steps of the overall the SLAM implementation of the proposed method. First, the ego-velocity of the platform is estimated using the relative velocity with respect to stationary objects. In this case, a random sample consensus algorithm is used in the velocity-angle map generated from the detection results to identify stationary objects. Second, the rotation angle of the platform is estimated using the linear component extracted from the walls in the xy plane. Then, we determine the ego-motion of the platform using the estimated ego-velocity and rotation angle. Finally, we map the position of the platform and the indoor environment simultaneously in the absolute coordinate system. We validate the mapping result generated using our proposed method by comparing it with the ground truth-based mapping result.

In this paper, we propose methods to estimate the moving direction and size of a target vehicle based on point cloud data detected by high-resolution automotive radar sensor. Previous studies using automotive radar sensors have proposed methods to roughly estimate the moving direction of vehicles, such as left, straight, or right. This study proposes methods to estimate not only the specific moving direction but also the approximate size of the vehicle. First, we use the high-resolution frequency-modulated continuous wave radar to acquire point cloud data for vehicles moving at various angles. In the point cloud data, radar signals are strongly reflected from the side of the vehicle and detected as a line segment. The proposed moving direction and size estimation method is based on line segment extracted from the Hough transform (HT). To extract the line segment from the point cloud data, the Hough transform is used. Using the extracted line segment, methods for estimating the moving direction and size of the vehicle are proposed. And then, the quick hull algorithm is used to estimate the center point of the vehicle to match the position of the target in the coordinate system. Finally, the direction, width, and length of the vehicle estimated from the proposed methods show average errors of 1.94 $^\circ$ , 4.32%, and 6.32%, respectively. In addition, when compared to the conventional principal component analysis (PCA)-based method, our proposed method exhibits superior performance in terms of estimation accuracy. Moreover, we have validated the effectiveness of the proposed method even in scenarios with multiple vehicles and in noisy road environments.

In the autonomous driving environment, an obstructed view of the road can cause a delayed response time, making it challenging to avoid potential accidents. Thus, the method for detecting the blind spot is required, and the demand for sharing the detection results between vehicles through communication systems is increasing. In this letter, we propose a novel method of sharing the detection results between vehicles in automotive radar systems. We propose to use frequency-division multiplexing to reallocate the frequency band designated for automotive radar systems into two separate bands, one for the target detection and the other for the vehicle-to-vehicle (V2V) communications. In addition, the detection results are encoded by using amplitude modulation in the signals for V2V communications. The proposed method of sharing the detection results between vehicles is verified through several simulation scenarios. Through the simulations, the detection result of the preceding vehicle can be successfully shared to the following vehicle. Moreover, the average error between the actual and shared detection results is lower than 1%.

The reliability and safety of advanced driver assistance systems and autonomous vehicles are highly dependent on the accuracy of automotive sensors such as radar, lidar, and camera. However, these sensors can be misaligned compared to the initial installation state due to external shocks, and it can cause deterioration of their performance. In the case of the radar sensor, when the mounting angle is distorted and the sensor tilt toward the ground or sky, the sensing performance deteriorates significantly. Therefore, to guarantee stable detection performance of the sensors and driver safety, a method for determining the misalignment of these sensors is required. In this paper, we propose a method for estimating the vertical tilt angle of the radar sensor using a deep neural network (DNN) classifier. Using the proposed method, the mounting state of the radar can be easily estimated without physically removing the bumper. First, to identify the characteristics of the received signal according to the radar misalignment states, radar data are obtained at various tilt angles and distances. Then, we extract range profiles from the received signals and design a DNN-based estimator using the profiles as input. The proposed angle estimator determines the tilt angle of the radar sensor regardless of the measured distance. The average estimation accuracy of the proposed DNN-based classifier is over 99.08%. Therefore, through the proposed method of indirectly determining the radar misalignment, maintenance of the vehicle radar sensor can be easily performed.

In this paper, we propose a method for efficient target classification based on the spatial features of the point cloud generated by using a high-resolution radar sensor. The frequency-modulated continuous wave radar sensor can estimate the distance and velocity of a target. In addition, the azimuth and elevation angle of the target can be estimated by using a multiple-input and multiple-output antenna system. Using the estimated distance, velocity, and angle, the 3D point cloud of target can be generated. From the generated point cloud, we extract the point cloud for each individual target using the density-based spatial clustering of application with noise method and a camera mounted on the radar sensor. Then, we define the convex hull boundaries that enclose these point clouds in both 3D and 2D spaces obtained by orthogonally projecting onto the $xy$, $yz$, and $zx$ planes. Using the vertices of convex hull, we calculate the volume of the targets and the areas in 2D spaces. Several feature points, including the calculated spatial information, are numerized and configured into feature vectors. We design an uncomplicated deep neural network classifier based on minimal input information to achieve fast and efficient classification performance. As a result, the proposed method achieved an average accuracy of 97.1%, and the time required for training was reduced compared to the method using only point cloud data and the convolutional neural network-based method.