Applications and Services Using Vehicular Exteroceptive Sensors: A Survey

Ortiz, Fernando Molano; Sammarco, Matteo; Costa, Luís Henrique M. K.; Detyniecki, Marcin

doi:10.1109/tiv.2022.3182218

Cited by 16 publications

(4 citation statements)

References 170 publications

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“…These files can vary in size and density of points and these depend mostly on the camera that is being used to generate such files, thousands of points can be found in a scene captured in a single shot, and, generally, the complexity of the processing of this information is increased proportionally with the quality of the camera and of the information it produces, the greater the detail, the greater point density. Novel methods can be found that use this type of information to solve problems in different fields, for example, in 3D reconstruction [6,7], simultaneous localization and mapping (SLAM) as in [8,9], navigation [10], object detection [11], mapping urban buildings [12], recovering building geometries [13,14], indoor scene reconstruction [7,15], computer vision as face recognition [16], segmentation with background removal [17], recognition tasks in robotics using scene modeling [18], navigation in agriculture [19], pedestrian detection [11], augmented reality (AR) [20], computerassisted surgery [21], 3D navigation for pedestrians and robots [22], ADAS (advanced driving assistance systems) [23], uncrewed aerial vehicles (UAVs) navigation [24], autonomous driving [25], body tracking [26], and RGB-D Multi-Camera Pose Estimation for 3D Reconstruction [27]. There are different sets of data or databases that are compiled and organized to facilitate the research paper using information from different scenarios represented in 3D point clouds [28].…”

Section: Introductionmentioning

confidence: 99%

A Robust Sphere Detection in a Realsense Point Cloud by USING Z-Score and RANSAC

et al. 2023

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Three-dimensional vision cameras, such as RGB-D, use 3D point cloud to represent scenes. File formats as XYZ and PLY are commonly used to store 3D point information as raw data, this information does not contain further details, such as metadata or segmentation, for the different objects in the scene. Moreover, objects in the scene can be recognized in a posterior process and can be used for other purposes, such as camera calibration or scene segmentation. We are proposing a method to recognize a basketball in the scene using its known dimensions to fit a sphere formula. In the proposed cost function we search for three different points in the scene using RANSAC (Random Sample Consensus). Furthermore, taking into account the fixed basketball size, our method differentiates the sphere geometry from other objects in the scene, making our method robust in complex scenes. In a posterior step, the sphere center is fitted using z-score values eliminating outliers from the sphere. Results show our methodology converges in finding the basketball in the scene and the center precision improves using z-score, the proposed method obtains a significant improvement by reducing outliers in scenes with noise from 1.75 to 8.3 times when using RANSAC alone. Experiments show our method has advantages when comparing with novel deep learning method.

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Section: Introductionmentioning

confidence: 99%

A Robust Sphere Detection in a Realsense Point Cloud by USING Z-Score and RANSAC

et al. 2023

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“…The study by Farah & Moreira-Matias (2017) developed an off-the-shelf machine learning framework that used inexpensive driver trip data for driver identification. The In-Vehicle Data Recorder (IVDR) technology helped in achieving more than 75% accuracy [2].…”

Section: Introductionmentioning

confidence: 99%

Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

Li¹,

Sun²,

Hu³

2023

Computers, Materials &Amp; Continua

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Driver identification in intelligent transport systems has immense demand, considering the safety and convenience of traveling in a vehicle. The rapid growth of driver assistance systems (DAS) and driver identification system propels the need for understanding the root causes of automobile accidents. Also, in the case of insurance, it is necessary to track the number of drivers who commonly drive a car in terms of insurance pricing. It is observed that drivers with frequent records of paying "fines" are compelled to pay higher insurance payments than drivers without any penalty records. Thus driver identification act as an important information source for the intelligent transport system. This study focuses on a similar objective to implement a machine learning-based approach for driver identification. Raw data is collected from in-vehicle sensors using the controller area network (CAN) and then converted to binary form using a one-hot encoding technique. Then, the transformed data is dimensionally reduced using the Principal Component Analysis (PCA) technique, and further optimal parameters from the dataset are selected using Whale Optimization Algorithm (WOA). The most relevant features are selected and then fed into a Convolutional Neural Network (CNN) model. The proposed model is evaluated against four different use cases of driver behavior. The results show that the best prediction accuracy is achieved in the case of drivers without glasses. The proposed model yielded optimal accuracy when evaluated against the K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) models with and without using dimensionality reduction approaches.

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“…Therefore, high-precision and high-reliability sensors are of great significance for unmanned vehicles. (4) Ortiz et al (5) discussed the advantages and disadvantages of various external sensors. The camera is the most common sensor used to simulate the image perceived by the human eye.…”

Section: Introductionmentioning

confidence: 99%

Detection and Navigation of Unmanned Vehicles in Wooded Environments Using Light Detection and Ranging Sensors

Zhang,

Jhang,

Lin

2023

Sensors and Materials

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With the advancement of automatic navigation, navigation control has become an indispensable core technology in the movement of unmanned vehicles. In particular, research on navigation control in outdoor wooded environments, which are more complex, less controlled, and more unpredictable than indoor environments, has received widespread attention. To realize the movement control and obstacle avoidance of unmanned vehicles in unknown environments, in this study, we use light detection and ranging (LiDAR) sensors to sense the surrounding environment. By plane meshing the point cloud reflected from LiDAR, we can instantly establish feasible regions. At the same time, using the artificial potential field algorithm, a stable obstacle avoidance and navigation path is planned for use in an unknown environment. In an actual woods navigation experiment to evaluate our proposed LiDAR detection method, we used an independently developed unmanned vehicle with Ackermann steering geometry. Experimental results indicate that the proposed method can effectively detect obstacles. The accuracy requirement is within 30 cm from the navigation target, and the experimental results show that the average navigation success rate of the proposed method is as high as 85%. The experimental results demonstrate that the system can stably and safely navigate in scenarios with different unknown environments.

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Applications and Services Using Vehicular Exteroceptive Sensors: A Survey

Cited by 16 publications

References 170 publications

A Robust Sphere Detection in a Realsense Point Cloud by USING Z-Score and RANSAC

A Robust Sphere Detection in a Realsense Point Cloud by USING Z-Score and RANSAC

Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

Detection and Navigation of Unmanned Vehicles in Wooded Environments Using Light Detection and Ranging Sensors

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