Automatic testing of maritime collision avoidance methods with sensor fusion

2018

The development of Autonomous Surface Vessels (ASVs) has during the recent years seen a great

progress. When being completely developed, these vessels may be used in a variety of scientific

and commercial operations, eventually leading human based operations redundant.

An ASV needs a well-functioning Collision Avoidance (COLAV) system in order to operate

at sea where other obstacles such as vessels and land are present. In addition of being

able to handle collision situations, the COLAV system needs to comply with the rules

for avoiding collision at sea (COLREGS). To retrieve the necessary information of the

surroundings, the COLAV system utilizes a number of information sources. This may include

exteroceptive sensors such as radar and cameras, or communication-based solutions

such as the automatic identification system (AIS). In order to enable COLAV, the sensor

information needs to be included into the state estimation of the surrounding obstacles.

This is done by the use of a tracking system.

In this thesis, a COLAV system including a multi-target tracking system based on the Joint

Integrated Probabilistic Data Association Filter (JIPDAF) and two COLAV algorithms,

one based on the Velocity Obstacle (VO) and another method called Scenario-Based Model

Predictive Control (SBMPC), has been tested in a wide variety of ASV scenarios. The scenarios

are generated with a new method which challenges the COLAV system to a high

number of succeeding vessel interactions. To evaluate the COLAV system s scenario performance,

a number of evaluation metrics used to determine COLREGS compliance are

applied.

The COLAV system have been implemented on a Platform Supply Vessel (PSV) and the

testing has taken place in a simulated environment. The results from the testing show that

when exposed to a small amount of noise, the tracking system delivers accurate obstacle

estimates to the COLAV algorithm, resulting in good evaluation scores. In more challenging

scenarios which includes considerably more noise, the tracking system delivers more

fluctuating obstacle estimates and a high number of false tracks, resulting in poor performance

of the SBMPC algorithm, while the VO algorithm performs remarkably well.

NTNU