The Experts below are selected from a list of 4179 Experts worldwide ranked by ideXlab platform
Hassan A Karimi - One of the best experts on this subject based on the ideXlab platform.
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a global path planner for safe navigation of autonomous vehicles in uncertain environments
Sensors, 2020Co-Authors: Mohammed Alharbi, Hassan A KarimiAbstract:Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under sensor uncertainty, with the understanding that there is no perfect sensing solution for all driving environments. In this paper, we propose a global safe path planner that analyzes sensor uncertainty and determines optimal paths. The path planner has two Components: sensor Analytics and path finder. The sensor Analytics Component combines the uncertainties of all sensors to evaluate the positioning and navigation performance of an AV at given locations and times. The path finder Component then utilizes the acquired sensor performance and creates a weight based on safety for each road segment. The operation and quality of the proposed path finder are demonstrated through simulations. The simulation results reveal that the proposed safe path planner generates paths that significantly improve the navigation safety in complex dynamic environments when compared to the paths generated by conventional approaches.
Mohammed Alharbi - One of the best experts on this subject based on the ideXlab platform.
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a global path planner for safe navigation of autonomous vehicles in uncertain environments
Sensors, 2020Co-Authors: Mohammed Alharbi, Hassan A KarimiAbstract:Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under sensor uncertainty, with the understanding that there is no perfect sensing solution for all driving environments. In this paper, we propose a global safe path planner that analyzes sensor uncertainty and determines optimal paths. The path planner has two Components: sensor Analytics and path finder. The sensor Analytics Component combines the uncertainties of all sensors to evaluate the positioning and navigation performance of an AV at given locations and times. The path finder Component then utilizes the acquired sensor performance and creates a weight based on safety for each road segment. The operation and quality of the proposed path finder are demonstrated through simulations. The simulation results reveal that the proposed safe path planner generates paths that significantly improve the navigation safety in complex dynamic environments when compared to the paths generated by conventional approaches.
Ha Karimi - One of the best experts on this subject based on the ideXlab platform.
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A global path planner for safe navigation of autonomous vehicles in uncertain environments
'MDPI AG', 2020Co-Authors: Alharbi M, Ha KarimiAbstract:© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under sensor uncertainty, with the understanding that there is no perfect sensing solution for all driving environments. In this paper, we propose a global safe path planner that analyzes sensor uncertainty and determines optimal paths. The path planner has two Components: Sensor Analytics and path finder. The sensor Analytics Component combines the uncertainties of all sensors to evaluate the positioning and navigation performance of an AV at given locations and times. The path finder Component then utilizes the acquired sensor performance and creates a weight based on safety for each road segment. The operation and quality of the proposed path finder are demonstrated through simulations. The simulation results reveal that the proposed safe path planner generates paths that significantly improve the navigation safety in complex dynamic environments when compared to the paths generated by conventional approaches
Alharbi M - One of the best experts on this subject based on the ideXlab platform.
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A global path planner for safe navigation of autonomous vehicles in uncertain environments
'MDPI AG', 2020Co-Authors: Alharbi M, Ha KarimiAbstract:© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under sensor uncertainty, with the understanding that there is no perfect sensing solution for all driving environments. In this paper, we propose a global safe path planner that analyzes sensor uncertainty and determines optimal paths. The path planner has two Components: Sensor Analytics and path finder. The sensor Analytics Component combines the uncertainties of all sensors to evaluate the positioning and navigation performance of an AV at given locations and times. The path finder Component then utilizes the acquired sensor performance and creates a weight based on safety for each road segment. The operation and quality of the proposed path finder are demonstrated through simulations. The simulation results reveal that the proposed safe path planner generates paths that significantly improve the navigation safety in complex dynamic environments when compared to the paths generated by conventional approaches
Krishna Chandramouli - One of the best experts on this subject based on the ideXlab platform.
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Critical Infrastructure Security Against Drone Attacks Using Visual Analytics
Computer Vision Systems, 2019Co-Authors: Xindi Zhang, Krishna ChandramouliAbstract:The recent developments in the field of unmanned aerial vehicles (UAV or drones) technology has generated a lot of interdisciplinary applications, ranging from remote surveillance of energy infrastructure, to agriculture. However, in the context of national security, low-cost drone equipment has also been viewed as an easy means to cause destructive effects against national critical infrastructures and civilian population. Addressing the challenge of real-time detection and continuous tracking, this paper proposed presents a holistic architecture consisting of both software and hardware design. The software-based video Analytics Component leverages upon the advancement of Region based Fully Convolutional Network model for drone detection. The hardware Component includes a low-cost sensing equipment powered by Raspberry Pi for controlling the camera platform for continuously tracking the orientation of the drone by streaming the video footage captured from the long-range surveillance camera. The novelty of the proposed framework is twofold namely the detection of the drone in real-time and continuous tracking of the detected drone through controlling the camera platform. The framework relies on the capability of the long-range camera to lock into the drone and subsequently track the drone through space. The Analytics processing Component utilises the NVIDIA $$\circledR $$ GeForce $$\circledR $$ GTX 1080 with 8 GB GDDR5X GPU. The experimental results of the proposed framework have been validated against real-world threat scenarios simulated for the protection of the national critical infrastructure.