Vision based obstacle detection and collision risk estimation of an unmanned surface vehicle

2016 ◽  
Author(s):  
Joohyun Woo ◽  
Nakwan Kim
Keyword(s):  
Risk Estimation ◽  
Obstacle Detection ◽  
Collision Risk ◽  
Unmanned Surface Vehicle

scholarly journals Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Anna Elena Tirri ◽  
Giancarmine Fasano ◽  
Domenico Accardo ◽  
Antonio Moccia
Keyword(s):  
Particle Filter ◽  
Particle Filtering ◽  
Risk Estimation ◽  
Tracking System ◽  
Nonlinear Problems ◽  
Obstacle Detection ◽  
Collision Risk ◽  
Sense And Avoid ◽  
Particle Filter Algorithm ◽  
The Impact

Obstacle detection and tracking is a key function for UAS sense and avoid applications. In fact, obstacles in the flight path must be detected and tracked in an accurate and timely manner in order to execute a collision avoidance maneuver in case of collision threat. The most important parameter for the assessment of a collision risk is the Distance at Closest Point of Approach, that is, the predicted minimum distance between own aircraft and intruder for assigned current position and speed. Since assessed methodologies can cause some loss of accuracy due to nonlinearities, advanced filtering methodologies, such as particle filters, can provide more accurate estimates of the target state in case of nonlinear problems, thus improving system performance in terms of collision risk estimation. The paper focuses on algorithm development and performance evaluation for an obstacle tracking system based on a particle filter. The particle filter algorithm was tested in off-line simulations based on data gathered during flight tests. In particular, radar-based tracking was considered in order to evaluate the impact of particle filtering in a single sensor framework. The analysis shows some accuracy improvements in the estimation of Distance at Closest Point of Approach, thus reducing the delay in collision detection.

scholarly journals An Estimation of Ship Collision Risk Based on Relevance Vector Machine

2021 ◽  
Vol 9 (5) ◽  
pp. 538
Author(s):  
Jinwan Park ◽  
Jung-Sik Jeong
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Risk Estimation ◽  
Good Method ◽  
Relevance Vector Machine ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Collision Risk ◽  
Estimation Model ◽  
Ship Collision

According to the statistics of maritime collision accidents over the last five years (2016–2020), 95% of the total maritime collision accidents are caused by human factors. Machine learning algorithms are an emerging approach in judging the risk of collision among vessels and supporting reliable decision-making prior to any behaviors for collision avoidance. As the result, it can be a good method to reduce errors caused by navigators’ carelessness. This article aims to propose an enhanced machine learning method to estimate ship collision risk and to support more reliable decision-making for ship collision risk. In order to estimate the ship collision risk, the conventional support vector machine (SVM) was applied. Regardless of the advantage of the SVM to resolve the uncertainty problem by using the collected ships’ parameters, it has inherent weak points. In this study, the relevance vector machine (RVM), which can present reliable probabilistic results based on Bayesian theory, was applied to estimate the collision risk. The proposed method was compared with the results of applying the SVM. It showed that the estimation model using RVM is more accurate and efficient than the model using SVM. We expect to support the reasonable decision-making of the navigator through more accurate risk estimation, thus allowing early evasive actions.

Real-time obstacle detection for Unmanned Surface Vehicle

2011 ◽  
Author(s):  
Han Wang ◽  
Zhuo Wei ◽  
Sisong Wang ◽  
Chek Seng Ow ◽  
Kah Tong Ho ◽  
...  
Keyword(s):  
Real Time ◽  
Obstacle Detection ◽  
Unmanned Surface Vehicle

scholarly journals Smart Sensor Based Obstacle Detection for High-Speed Unmanned Surface Vehicle

2015 ◽  
Vol 48 (16) ◽  
pp. 190-197 ◽  
Author(s):  
D. Hermann ◽  
R. Galeazzi ◽  
J.C. Andersen ◽  
M. Blanke
Keyword(s):  
High Speed ◽  
Obstacle Detection ◽  
Smart Sensor ◽  
Unmanned Surface Vehicle

European Studies to Investigate the Feasibility of using 1000 ft Vertical Separation Minima above FL 290. Part II. Precision Radar Data Analysis and Collision Risk Assessment

1992 ◽  
Vol 45 (1) ◽  
pp. 91-106 ◽  
Author(s):  
D. Harrison ◽  
G. Moek
Keyword(s):  
North Atlantic ◽  
Risk Estimation ◽  
Radar Data ◽  
Vertical Separation ◽  
Collision Risk ◽  
European Studies ◽  
North Atlantic Region ◽  
Atlantic Region ◽  
Technical Performance ◽  
The North

This paper is the second of a series of three papers, documenting the European studies into the feasibility of 1000 ft vertical separation minima above FL290. The paper discusses the vertical collision risk estimation methodology and an assessment of the collision risk against a target level of safety.The analysis indicates the technical feasibility of a reduced vertical separation minima in the North Atlantic Region. However, for current operations and technical performance within European continental airspace, the risk estimation indicates that a 1000 ft minima is not technically feasible.The contents of this paper reflect the views of the authors concerned; they do not necessarily reflect the official views or policy of the CAA or NLR.

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