U SMART ZONE – Creating highly realistic virtual environment for vehicle-in-the-loop simulations

Developing Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV) based on machine learning is generally an extensive and costly process. Due to the increasing complexity of autonomous systems, the need for extensive testing and validation arises. In recent years, computer simulation has been used for these purposes. Performing realistic simulations, especially for the purpose of computer vision-based systems, requires a high-quality, almost photorealistic virtual environment. This paper introduces U SMART ZONE, a high-fidelity virtual model of the Severní Terasa district in Ústí nad Labem, Czech Republic comprising of more than 7.5 km of drivable roads with a total area of approximately 1.4 km2 for human-in-the-loop and hardware-in-the-loop (HiL) simulations

U SMART ZONE – Creating highly realistic virtual environment for vehicle-in-the-loop simulations

Developing Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV) based on machine learning is generally an extensive and costly process. Due to the increasing complexity of autonomous systems, the need for extensive testing and validation arises. In recent years, computer simulation has been used for these purposes. Performing realistic simulations, especially for the purpose of computer vision-based systems, requires a high-quality, almost photorealistic virtual environment. This paper introduces U SMART ZONE, a high-fidelity virtual model of the Severní Terasa district in Ústí nad Labem, Czech Republic comprising of more than 7.5 km of drivable roads with a total area of approximately 1.4 km2 for human-in-the-loop and hardware-in-the-loop (HiL) simulations

Pedestrian Flow Simulation Validation and Verification Techniques

For the verification and validation of microscopic simulation models of pedestrian flow, we have performed experiments for different kind of facilities and sites where most conflicts and congestion happens e.g. corridors, narrow passages, and crosswalks. The validity of the model should compare the experimental conditions and simulation results with video recording carried out in the same condition like in real life e.g. pedestrian flux and density distributions. The strategy in this technique is to achieve a certain amount of accuracy required in the simulation model. This method is good at detecting the critical points in the pedestrians walking areas. For the calibration of suitable models we use the results obtained from analyzing the video recordings in Hajj 2009 and these results can be used to check the design sections of pedestrian facilities and exits. As practical examples, we present the simulation of pilgrim streams on the Jamarat bridge. The objectives of this study are twofold: first, to show through verification and validation that simulation tools can be used to reproduce realistic scenarios, and second, gather data for accurate predictions for designers and decision makers.Comment: 19 pages, 10 figure

ARTMAP-FTR: A Neural Network For Fusion Target Recognition, With Application To Sonar Classification

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657

Transfer Learning-Based Crack Detection by Autonomous UAVs

Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping in building inspection. Yet, the number of studies is limited considering the post processing of the data and its integration with autonomous UAVs. These will enable huge steps onward into full automation of building inspection. In this regard, this work presents a decision making tool for revisiting tasks in visual building inspection by autonomous UAVs. The tool is an implementation of fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack detection. It offers an optional mechanism for task planning of revisiting pinpoint locations during inspection. It is integrated to a quadrotor UAV system that can autonomously navigate in GPS-denied environments. The UAV is equipped with onboard sensors and computers for autonomous localization, mapping and motion planning. The integrated system is tested through simulations and real-world experiments. The results show that the system achieves crack detection and autonomous navigation in GPS-denied environments for building inspection