Dynamic collision risk modeling under uncertainty

Abstract

This paper introduces a probabilistic model for collision risk assessment between moving vehicles. The uncertainties in the states and the geometric variables obtained from the sensory system are characterized by probability density functions. Given the states and their uncertainties, the goal is to determine the probability of collision in a dynamic environment. Two approaches are discussed: (1) The virtual configuration space (VCS), and (2) the rates of change of the visibility angles. The VCS is a transformation of observer that reduces collision detection with a moving object to collision detection with a stationary object. This approach allows to create simple geometric collision cones. Error propagation models are used to solve the problem when going from the VCS to the configuration space. The second approach derives the collision conditions in terms of the rate of change of the limit visibility angles. The probability of collision is then calculated. A comparison between the two methods is carried out. Results are illustrated using simulation, including Monte Carlo simulation.

Time‐Varying Risk Measurement for Ship Collision Prevention

“…A collision is seen as a physical process that ships approach each other and their positions overlap at the end (Belkhouche & Bendjilali, 2012;Park & Kim, 2016;Simsir, Amasyali, Bal, Çelebi, & Ertugrul, 2014). The risk is the probability of overlap in the future, given initial states, ship models and uncertainties (Belkhouche & Bendjilali, 2012). Given ship motion model and initial states, trajectories of ships are deterministic and collision event is certain.…”

“…However, the uncertainties of the system are inevitable, e.g. uncertainties in modeling models (mainly environmental disturbance (Belkhouche & Bendjilali, 2012)), sensing data (Park & Kim, 2016), etc. Consequently, the trajectories of ships become probabilistic.…”

An Ordinal Model of Risk Based on Mariner's Judgement

“…(Belkhouche and Bendjilali, 2013, Lambert et al, 2008, Plamen Angelov and Michael Everett, 2008. Amongst this research, there is a broad consensus concerning the needs to acquire a precise representation of the environment surrounding the craft and, most importantly, of processing the acquired data for assessing risk of collision before any decision can be made.…”

“…There is an extensive existing body of research in the field of collision avoidance as an aid for navigators in manned craft and also for use in autonomous unmanned craft (Belkhouche and Bendjilali, 2013; Lambert et al, 2008, Plamen Angelov et al, 2008). Among this research there is a broad consensus concerning the need to acquire a precise representation of the environment surrounding the craft and, most importantly, of processing the acquired data for assessing risk of collision before any decision can be made.…”

“…In addition, Perera et al (2012) applied both fuzzy inference and Bayesian methods in order to assess the risk of collision and to take evasive action and Goerland et al (2015) presented a comprehensive rule-based expert system with fuzzy inference where the knowledge domain has been defined by consultation with professional mariners. Furthermore, a number of probabilistic approaches have been proposed that take account of unknown factors in order to predict risk and possible trajectories; see Belkhouche and Bendjilali (2013) or Lambert et al (2008) and Simsir et al (2014) for the application of Artificial Neural Networks for predicting positions of vessels in a collision alert system. These methods add a layer of sophistication to the geometric approach.…”