scholarly journals Combined road prediction and target tracking in collision avoidance

Author(s):  
A. Eidehall ◽  
F. Gustafsson
2018 ◽  
Vol 103 (3) ◽  
pp. 2515-2528
Author(s):  
Santosh Kumar ◽  
Sudhir ◽  
Umesh Kumar Tiwari

2008 ◽  
Vol 41 (2) ◽  
pp. 5724-5729 ◽  
Author(s):  
Carole G. Prévost ◽  
André Desbiens ◽  
Eric Gagnon ◽  
Daniel Hodouin

Author(s):  
Zhihong Peng ◽  
◽  
Zhimin Chen

This paper focuses on ground-moving target tracking of an unmanned aerial vehicle (UAV) in the presence of static obstacles and moving threat sources. Due to a UAV is restricted by airspace restrictions and measurement limitations during flight, we derive a dynamic path planning strategy by generating guidance vector filed combined Lyapunov vector field with collision avoidance potential function to track target in standoff distance loitering pattern, and resolved collision avoidance, simultaneously. This method relies only on the current information of the UAV and target, and generates a single-step route plan in realtime. Its performance is simple, efficient, and fast and have low computational complexity. The results of numerical simulation verify the effectiveness of the tracking and collision avoidance process of the UAV.


2021 ◽  
Vol 93 (7s) ◽  
pp. 159-166
Author(s):  
Miro Petković ◽  
◽  
Danko Kezić ◽  
Igor Vujović ◽  
Ivan Pavić ◽  
...  

Automatic Identification Systems (AIS) and Automatic Radar Plotting Aids (ARPA) are commonly used to detect targets for collision avoidance. However, AIS cannot detect targets without AIS transmitters and ARPA has limitations due to blind sector and small targets may not be detected. Advances in computer performance and video-based detection generated much interest in developing intelligent video surveillance systems to achieve autonomous navigation. To develop a reliable collision avoidance system, we propose the use of a visual camera for real-time object detection and target tracking. Moreover, the system should follow the International Regulations for Preventing Collisions at Sea (COLREGs) to avoid catastrophic accidents. In this paper only a part of the system is presented. For real-time object detection, the You Only Look Once (YOLO) ver. 3 convolutional neural network is used, and the target tracking filter based on a Kalman filter with built-in estimated relative position and velocity.


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