LAUSR

Abstract Collision Avoidance (COLAV) for autonomous ships is challenging since it relies on track estimates of nearby obstacles which are inherently uncertain in both state and intent. This uncertainty must be accounted for in the COLAV system in order to ensure both safe and efficient operation of the vessel in accordance with the traffic rules. Here, a COLAV system built on the Scenario-based Model Predictive Control (SB-MPC) with dynamic probabilistic risk treatment is presented. The system estimates the probability of collision with all nearby obstacles using a combination of Monte Carlo simulation (MCS) and a Kalman Filter (KF), taking the uncertainty in both position and velocity into account. A probabilistic collision cost is then used in the MPC to penalize risk-taking maneuvers. Simulation results show that the proposed method may provide increased robustness due to increased situational awareness, while also being able to efficiently follow the nominal path and adhere to the traffic rules.