The generation of digital maps with lane-level resolution is rapidly becoming a necessity, as semi- or fully-autonomous driving vehicles are now commercially available. In this paper, we present a practical real-time working prototype for road lane detection using LiDAR data, which can be further extended to automatic lane-level map generation. Conventional lane detection methods are limited to simple road conditions and are not suitable for complex urban roads with various road signs on the ground. Given a 3D point cloud scanned by a 3D LiDAR sensor, we categorized the points of the drivable region and distinguished the points of the road signs on the ground. Then, we developed an expectation-maximization method to detect parallel lines and update the 3D line parameters in real time, as the probe vehicle equipped with the LiDAR sensor moved forward. The detected and recorded line parameters were integrated to build a lane-level digital map with the help of a GPS/INS sensor. The proposed system was tested to generate accurate lane-level maps of two complex urban routes. The experimental results showed that the proposed system was fast and practical in terms of effectively detecting road lines and generating lane-level maps.

Deep neural networks (DNNs) have successfully classified EEG-based brain-computer interface (BCI) systems. However, recent studies have found that well-designed input samples, known as adversarial examples, can easily fool well-performed deep neural networks model with minor perturbations undetectable by a human. This paper proposes an efficient generative model named generative perturbation network (GPN), which can generate universal adversarial examples with the same architecture for non-targeted and targeted attacks. Furthermore, the proposed model can be efficiently extended to conditionally or simultaneously generate perturbations for various targets and victim models. Our experimental evaluation demonstrates that perturbations generated by the proposed model outperform previous approaches for crafting signal-agnostic perturbations. We demonstrate that the extended network for signal-specific methods also significantly reduces generation time while performing similarly. The transferability across classification networks of the proposed method is superior to the other methods, which shows our perturbations' high level of generality. Our code is available for download on https://github.com/AIRLABkhu/Generative-Perturbation-Networks .

Urban scene modeling is a challenging but essential task for various applications, such as 3D map generation, city digitization, and AR/VR/metaverse applications. To model man-made structures, such as roads and buildings, which are the major components in general urban scenes, we present a clustering-based plane segmentation neural network using 3D point clouds, called hybrid K-means plane segmentation (HKPS). The proposed method segments unorganized 3D point clouds into planes by training the neural network to estimate the appropriate number of planes in the point cloud based on hybrid K-means clustering. We consider both the Euclidean distance and cosine distance to cluster nearby points in the same direction for better plane segmentation results. Our network does not require any labeled information for training. We evaluated the proposed method using the Virtual KITTI dataset and showed that our method outperforms conventional methods in plane segmentation. Our code is publicly available.

Recent advancements in deep learning for predicting Arterial Blood Pressure (ABP) have prominently featured the use of photoplethysmography (PPG) signals. Notably, PPG signals exhibit significant variability due to differences in measurement environments, alongside stark disparities in the distribution of collected signal data among different labels. To address these challenges, this study introduces a Bi-Guided Diffusion Model designed to generate PPG signals with expected features of ABP within a few-shot setting for each label group. We propose a guided diffusion model architecture that can simultaneously consider both the determinant group condition and the continuous label condition for each group in a few-shot setting. To our knowledge, this is the first study to use a diffusion model for generating PPG signals with a limited dataset. Initially, we categorized them into four groups based on SBP and DBP values: Hypo, Normal, Prehyper, and Hyper2. In each group, we sample an equal number of data points according to the few-shot setting and then generate appropriate PPG signals for each group through guidance. Additionally, Our study proposes a post-processing technique to address the limitations of generative models in few-shot settings, consistently boosting performance across various methods such as training from scratch, transfer learning, and linear probing. When benchmarked, our methodology demonstrated performance improvements across all datasets, including BCG, PPGBP, and SENSORS. We confirmed data quality by comparing training, generated, and actual data and analyzed error cases, morphology features, and t-SNE distribution to highlight the role of synthetic data in enhancing performance.