Deep RNN Based Prediction of Driver’s Intended Movements at Intersection Using Cooperative Awareness Messages

Komol, Md. Mostafizur Rahman; Elhenawy, Mohammed; Masoud, Mahmoud; Rakotonirainy, Andry; Glaser, Sébastien; Wood, Merle; Alderson, David

doi:10.1109/tits.2023.3254905

Cited by 13 publications

(2 citation statements)

References 33 publications

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“…The complexity, variety, and technological advancements of modern UAV missions drive their pursuit of greater and flying stability [136]. As a result, autonomous UAVs with pre-programmed routes, identification, and avoiding obstacles algorithms on computers have been developed [135], [145], [146]. In the bulk of the reviewed algorithms, supervised learning methods are used to identify and avoid obstacles.…”

Section: Automation and Artificial Intelligencementioning

confidence: 99%

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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In the contemporary landscape, the escalating deployment of drones across diverse industries has ushered in a consequential concern, including ensuring the security of drone operations. This concern extends to a spectrum of challenges, encompassing collisions with stationary and mobile obstacles and encounters with other drones. Moreover, the inherent limitations of drones, namely constraints on energy consumption, data storage capacity, and processing power, present formidable obstacles in developing collision avoidance algorithms. This review paper explores the challenges of ensuring safe drone operations, focusing on collision avoidance. We explore collision avoidance methods for UAVs from various perspectives, categorizing them into four main groups: obstacle detection and avoidance, collision avoidance algorithms, drone swarm, and path optimization. Additionally, our analysis delves into machine learning techniques, discusses metrics and simulation tools to validate collision avoidance systems, and delineates local and global algorithmic perspectives. Our evaluation reveals significant challenges in current drone collision prevention algorithms. Despite advancements, critical UAV network and communication challenges are often overlooked, prompting a reliance on simulation-based research due to cost and safety concerns. Challenges encompass precise detection of small and moving obstacles, minimizing path deviations at minimal cost, high machine learning and automation expenses, prohibitive costs of real testbeds, limited environmental comprehension, and security apprehensions. By addressing these key areas, future research can advance the field of drone collision avoidance and pave the way for safer and more efficient UAV operations.

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Section: Automation and Artificial Intelligencementioning

confidence: 99%

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Modern UAVs aim for increased autonomy and flight stability due to the technical developments, diversity, and complexity of missions [10]. This has led to the development of autonomous UAVs that can be controlled by computers loaded with pre-determined path, detection, and obstacle avoidance algorithms [11,12]. An onboard mission plan consists of path planning algorithms to alter the flight plan when the obstacles have been detected.…”

Section: Introductionmentioning

confidence: 99%

QuickNav: An Effective Collision Avoidance and Path-Planning Algorithm for UAS

Debnath,

Hawary,

Ramdan

et al. 2023

Drones

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Obstacle avoidance is a desirable capability for Unmanned Aerial Systems (UASs)/drones which prevents crashes and reduces pilot fatigue, particularly when operating in the Beyond Visual Line of Sight (BVLOS). In this paper, we present QuickNav, a solution for obstacle detection and avoidance designed to function as a pre-planned onboard navigation system for UAS flying in a known obstacle-cluttered environment. Our method uses a geometrical approach and a predefined safe perimeter (square area) based on Euclidean Geometry for the estimation of intercepting points, as a simple and efficient way to detect obstacles. The square region is treated as the restricted zone that the UAS must avoid entering, therefore providing a perimeter for manoeuvring and arriving at the next waypoints. The proposed algorithm is developed in a MATLAB environment and can be easily translated into other programming languages. The proposed algorithm is tested in scenarios with increasing levels of complexity, demonstrating that the QuickNav algorithm is able to successfully and efficiently generate a series of avoiding waypoints. Furthermore, QuickNav produces shorter distances as compared to those of the brute force method and is able to solve difficult obstacle avoidance problems in fractions of the time and distance required by the other methods. QuickNav can be used to improve the safety and efficiency of UAV missions and can be applied to the deployment of UAVs for surveillance, search and rescue, and delivery operations.

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2024

Urban Transportation Systems

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Deep RNN Based Prediction of Driver’s Intended Movements at Intersection Using Cooperative Awareness Messages

Cited by 13 publications

References 33 publications

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

QuickNav: An Effective Collision Avoidance and Path-Planning Algorithm for UAS

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