Although numerous road segmentation studies have utilized vision data, obtaining robust classification is still challenging due to vision sensor noise and target object deformation. Long-distance images are still problematic because of blur and low resolution, and these features make distinguishing roads from objects difficult. This study utilizes light detection and ranging (LiDAR), which generates information that camera images lack, such as distance, height, and intensity, as a reliable supplement to address this problem. In contrast to conventional approaches, additional domain transformation to a bird’s eye view space is executed to obtain long-range data with resolutions comparable to those of short-range data. This study proposes a convolutional neural network architecture that processes data transformed to a bird’s eye view plane. The network’s pathways are split into two parts to resolve calibration errors in the transformed image and point cloud. The network, which has modules that operate sequentially at various scaled dilated convolution rates, is designed to quickly and accurately handle a wide range of data. Comprehensive empirical studies using the Karlsruhe Institute of Technology and Toyota Technological Institute’s (KITTI’s) road detection benchmarks demonstrate that this study’s approach takes advantage of camera and LiDAR information, achieving robust road detection with short runtimes. Our result ranks 22nd in the KITTI’s leaderboard and shows real-time performance.

In autonomous driving, the camera and LiDAR are complementary to each other through their strengths, and many autonomous vehicles and robot recognition systems utilize cameras and LiDAR. In order to effectively fuse the data of the camera and LiDAR attached to different positions and angles, we must perform camera–LiDAR extrinsic calibration. Most existing camera–LiDAR calibration methods infer the results by constructing a camera–LiDAR feature pair using features acquired from a single frame. It has the disadvantage that it is challenging to draw results. In this paper, we used sequential LiDAR data to extract features by accumulating LiDAR frames effectively. By using the location information between the accumulated frame (global) and the single frame (local) to convert the features detected from the global to the local, the shortcomings of LiDAR’s single frame with few features were supplemented. Methods for detecting feature points in LiDAR are described step-by-step, and the advantages and excellence of this camera–LiDAR calibration system are shown through quantitative/qualitative evaluation methods. As a result, the proposed system outputs superior performance compared to other systems.

In this paper, I propose a bird eye view image detection method for parking areas and collision risk areas at the same time in parking situations. Deep learning algorithms using area detection and semantic segmentation were used. The main architecture of the method described in this paper is based on a harmonic densely connected network and a cross-stage partial network. The dataset used for training was calibrated to four 190° wide-angle cameras to generate around view monitor (AVM) images based on the Chungbuk National University parking lot, and an experiment was performed based on this dataset. In the experimental results, the available parking area was visualized by detecting the parking line, parking area, and available driving area in the AVM images. Furthermore, the undetected area in the semantic segmentation as a collision risk area was visualized in order to obtain the results. According to the proposed attention CSPHarDNet model, the experimental results were 81.89% mIoU and 18.36 FPS in a NVIDIA Xavier environment. The results of this experiment demonstrated that algorithms can be used in real time in a parking situation and have better performance results compared to the conventional HarDNet.

This paper addresses a novel camera-LiDAR online targetless calibration method. As the installation of heterogeneous sensors in autonomous vehicles is increasing, the importance of sensor calibration is also growing. An automatic sensor calibration function is essential for safe autonomous driving, responding to the sensor's geometric change. In this paper, background knowledge of sensor calibration is explained, and related papers are referenced. Depth estimation and semantic segmentation models are learned to detect significant regions from the camera using open and custom datasets. This paper describes the online calibration procedures based on motion-based calibration between the camera and LiDAR. Motion-based calibration faces a challenge for lack of roll and pitch variation, and our method overcomes this challenge by estimating a camera 3D ground plane from semantic segmentation and depth estimation and simultaneously detecting a 3D LiDAR ground plane. LiDAR-to-camera roll, pitch, and z values are then extracted using the camera and LiDAR ground plane, and the motion-based calibration problem is solved and optimized using those values as constraints. Experimental results conducted on urban roads and the proving ground C-track using our autonomous vehicles showed that the proposed method is quantitatively and qualitatively improving the existing method