Obstacles In Urban Environments Research Articles

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds.

The precise extraction of road boundaries is an essential task to obtain road infrastructure data that can support various applications, such as maintenance, autonomous driving, vehicle navigation, and the generation of high-definition maps (HD map). Despite promising outcomes in prior studies, challenges persist in road extraction, particularly in discerning diverse road types. The proposed methodology integrates state-of-the-art techniques like DBSCAN and RANSAC, aiming to establish a universally applicable approach for diverse mobile mapping systems. This effort represents a pioneering step in extracting road information from image-based point cloud data. To assess the efficacy of the proposed method, we conducted experiments using a large-scale dataset acquired by two mobile mapping systems on the Yıldız Technical University campus; one system was configured as a mobile LiDAR system (MLS), while the other was equipped with cameras to operate as a photogrammetry-based mobile mapping system (MMS). Using manually measured reference road boundary data, we evaluated the completeness, correctness, and quality parameters of the road extraction performance of our proposed method based on two datasets. The completeness rates were 93.2% and 84.5%, while the correctness rates were 98.6% and 93.6%, respectively. The overall quality of the road curb extraction was 93.9% and 84.5% for the two datasets. Our proposed algorithm is capable of accurately extracting straight or curved road boundaries and curbs from complex point cloud data that includes vehicles, pedestrians, and other obstacles in urban environment. Furthermore, our experiments demonstrate that the algorithm can be applied to point cloud data acquired from different systems, such as MLS and MMS, with varying spatial resolutions and accuracy levels.

Исследование детектирования ZF и MMSE в системах MIMO

The proliferation of obstacles in urban environments, such as buildings, wires, and trees, can cause signal re-reflection, resulting in difficulties in detecting the original one. Multipath propagation occurs when electromagnetic waves hit natural and man-made obstacles, leading to the reflection, scattering, and diffraction of waves. This can result in multiple waves arriving at the receiving antenna with different delays, attenuations, and phases. To address this problem, the widely used in modern wireless communication systems MIMO technology (Multiple Input Multiple Output) can be utilized. It uses multiple transmitting and receiving antennas to increase communication channel throughput and reduce errors that occur during data transmission. This paper examines a receiver with decorrelation (ZF receiver) and a receiver that minimizes the minimum mean square error (MMSE). Additionally, an algorithm for a MIMO transmission system model using ZF and MMSE receivers is presented. In the final section of the article, the results of Bit Error Rate (BER) modeling are presented for different numbers of transmitting and receiving antennas. The presented algorithm can significantly reduce errors that occur during data transmission in urban environments, where multipath propagation is common because of signal re-reflection. The use of MIMO technology with decorrelation and MMSE receivers can increase the efficiency of communication channels and make wireless communication more reliable in urban environments.

Receding-horizon RRT-Infotaxis for autonomous source search in urban environments

In emergency situations such as hazardous gas leak, search and estimation for identifying source information, known as source term estimation (STE), in a timely and accurate manner is of significant importance. In real world situations, obstacles such as buildings or barriers not only block the path for search but also interfere the flow of the gas source. For autonomous source search and estimation using a mobile sensor in such obstacle-rich environments, this paper proposes an information-theoretic STE approach by combining a widely-used Infotaxis with the rapidly-exploring random trees (RRT). In particular, the proposed strategy utilizes the receding-horizon RRT concept with a newly designed utility function for determining the next maneuver of a mobile agent to get the best information of the source while avoiding obstacles in urban environments. Numerical simulations in various environments show the superior performance of the proposed approach compared with the original Infotaxis method.

Application of three-dimensional electrical resistivity tomography in urban zones by arbitrary electrode distribution survey design

One of the most widely used geophysical surveying techniques in the urban environment is the electrical resistivity tomography (ERT) method. However, traditional ERT can only utilize regular grid and equidistant electrode layouts. Furthermore, all electrodes must be connected by long cables, and the existence of obstacles in urban environments makes it challenging to implement such layouts. We therefore present an optimized 3D ERT survey design consisting of arbitrarily distributed electrodes. We use the dipole-dipole array as the data acquisition unit. Each unit is independent and can be randomly distributed and adjusted in length. The unit can be used as a current or potential electrode as required. In this way, the limitations of grids and cables can be eliminated. Arbitrary distribution does not mean doing whatever you want. Must follow the basic principles of the geoelectric method. Based on this, we have formulated a set of reasonable procedures in the survey design, including the ‘main-sub unit’ and the ‘effective measurement circle’, which make the surveying system run more quickly and accurately. We select a complex urban area with partial prior information as the field sample to verify our survey design. This approach can effectively avoid the disadvantages of the traditional ERT method and obtain correct and effective results in a complex urban environment.

A Fast Obstacle Detection Method by Fusion of Double-Layer Region Growing Algorithm and Grid-SECOND Detector

Adjacent obstacles are difficult to be distinguished, and remote obstacles are detected easily to split. Besides, limited deep learning samples easily result in missed detection of obstacles in urban environment. In view of this, a fast and robust detection method is proposed by fusing Double-Layer Region Growth algorithm and Grid-SECOND detector. At first, SECOND detector is improved by replacing voxel grids with 2D grids and adopting multi-dimensional features to detect obstacles, which can reduce the time consumption and ensure the accurate detection of remote obstacles. Then, the first Region Growing algorithm is used to cluster the undetected and non-empty grids, which can detect obstacles outside the training set. At last, the second Region Growing algorithm is used to refine the detection results of obstacles with larger volume and multi-obstacles grids, and complete the obstacle detection. Through testing in our extracted urban dataset and KITTI dataset, it is verified that the proposed method outperforms state-of-the-art methods and can accurately achieve obstacle detection. The average duration of the entire process is about 50ms.

Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math> </inline-formula>-Nearest Neighbor Scheme

Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this paper, we propose a stereovision-based method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised $k$ -nearest neighbors (KNN) algorithm to accurately and reliably detect the presence of obstacles. We consider obstacle detection as an anomaly detection problem. We evaluated the proposed method by using practical data from three publicly available data sets, the Malaga stereovision urban data set, the Daimler urban segmentation data set, and the Bahnhof data set. Also, we compared the efficiency of DSA-KNN approach to the deep belief network-based clustering schemes. Results show that the DSA-KNN is suitable to visually monitor urban scenes.

Urban wind resource assessment in changing climate: Case study

Urban wind resource assessment in changing climate has not been studied so far. This study presents a methodology for microscale numerical modelling of urban wind resource assessment in changing climate. The methodology is applied for a specific urban development in the city of Toronto, ON, Canada. It is shown that the speed of the southwest winds, that is, the most frequent winds increased for .8 m s−1 in the period from 1948 to 2015. The generated wind energy maps are used to estimate the influences of climate change on the available wind energy. It is shown that the geometry of irregularly spaced and located obstacles in urban environments has to be taken into consideration when performing studies on urban wind resource assessment in changing climate. In the analysed urban environment, peak speeds are more affected by climate change than the mean speeds.

Track-based self-supervised classification of dynamic obstacles

This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical scheme that relies on the stability of a subset of features given by a sensor to perform an initial robust classification based on unsupervised techniques. The obtained results are used as labels to train a set of supervised classifiers. The outcomes obtained with the second sensor can be used for higher level tasks such as segmentation or to refine the within-clusters discrimination. The proposed architecture is evaluated for a particular realization based on range and visual information which produces track-based labeling that is then employed to train supervised modules that perform instantaneous classification. Experiments show that the system is able to achieve 95% classification accuracy and to maintain the performance through on-line retraining when working conditions change.

Unsupervised classification of dynamic obstacles in urban environments

AbstractThis paper presents a solution to the problem of unsupervised classification of dynamic obstacles in urban environments. A track‐based model is introduced for the integration of two‐dimensional laser and vision information that provides a robust spatiotemporal synthesis of the sensed moving obstacles and forms the basis for suitable algorithms to perform unsupervised classification by clustering. This work presents various contributions in order to achieve accurate and efficient performance, initially using laser tracks for classification and then incorporating visual tracks to the model. A procedure is proposed for accurate unsupervised classification of dynamic obstacles using a laser stamp representation of the tracks. Laser data are then integrated with visual information through a single‐instance visual stamp representation, which is finally extended using a multiple‐instance framework to robustly deal with challenges associated with perception in real‐world scenarios. The proposed algorithms are extensively validated with a simulated environment. Experiments with a research vehicle in an urban environment demonstrate the performance of the approach with real data. The experimental results reach an accuracy of more than 92% for obstacle classification, finding the clusters that correspond to the main obstacle classes in the data. © 2010 Wiley Periodicals, Inc.

A Stereovision Method for Obstacle Detection and Tracking in Non-Flat Urban Environments

Obstacle detection is an essential capability for the safe guidance of autonomous vehicles, especially in urban environments. This paper presents an efficient method to integrate spatial and temporal constraints for detecting and tracking obstacles in urban environments. In order to enhance the reliability of the obstacle detection task, we do not consider the urban roads as rigid planes, but as quasi-planes, whose normal vectors have orientation constraints. Under this flexible road model, we propose a fast, robust stereovision based obstacle detection method. A watershed transformation is employed for obstacle segmentation in dense traffic conditions, even with partial occlusions, in urban environments. Finally a UKF (Unscented Kalman filter) is applied to estimate the obstacles parameters under a nonlinear observation model. To avoid the difficulty of the computation in metric space, the whole detection process is performed in the disparity image. Various experimental results are presented, showing the advantages of this method.