Gene

Process (computing)

Engineering

Marine engineering

Computer security

Robustness (evolution)

Real-time computing

Probabilistic logic

Archaeology

Operating system

Biochemistry

Data mining

Computer science

Collision

Obstacle

Automatic Identification System

Simulation

Geography

Artificial intelligence

Chemistry

Ocean Engineering

Maritime accidents have been imposing various risks to individuals and societies in terms of human and property loss, and environmental consequences. For probabilistic risk analysis and management, collision candidate detection is the first step. Therefore, it is of great importance to further improve methods to detect possible collision scenarios. This paper proposes a Time Discrete Non-linear Velocity Obstacle (TD-NLVO) method for collision candidate detection, which is based on the Non-linear Velocity Obstacle algorithm and tested on historical AIS data (Automatic Identification System). Collision candidates are detected based on the perspective which considers a ship encounter as a process, rather than analysing traffic data at certain time slices. Case studies on single encounters of ship traffic in waterways environments are conducted and presented in this paper. The results indicate that the TD-NLVO method can effectively detect collision candidates which satisfy pre-set criteria. A comparison between seven other popular AIS data-based collision candidate methods is performed, and the results indicate that the proposed method outperforms the other methods regarding its robustness towards the choice of parameter settings.

Ship collision candidate detection method: A velocity obstacle approach