2,576 research outputs found
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
Machine Learning (ML) techniques have gained significant traction as a means
of improving the autonomy of marine vehicles over the last few years. This
article surveys the recent ML approaches utilised for ship collision avoidance
(COLAV) and mission planning. Following an overview of the ever-expanding ML
exploitation for maritime vehicles, key topics in the mission planning of ships
are outlined. Notable papers with direct and indirect applications to the COLAV
subject are technically reviewed and compared. Critiques, challenges, and
future directions are also identified. The outcome clearly demonstrates the
thriving research in this field, even though commercial marine ships
incorporating machine intelligence able to perform autonomously under all
operating conditions are still a long way off
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.Peer reviewe
Multi-level decision framework collision avoidance algorithm in emergency scenarios
With the rapid development of autonomous driving, the attention of academia
has increasingly focused on the development of anti-collision systems in
emergency scenarios, which have a crucial impact on driving safety. While
numerous anti-collision strategies have emerged in recent years, most of them
only consider steering or braking. The dynamic and complex nature of the
driving environment presents a challenge to developing robust collision
avoidance algorithms in emergency scenarios. To address the complex, dynamic
obstacle scene and improve lateral maneuverability, this paper establishes a
multi-level decision-making obstacle avoidance framework that employs the safe
distance model and integrates emergency steering and emergency braking to
complete the obstacle avoidance process. This approach helps avoid the
high-risk situation of vehicle instability that can result from the separation
of steering and braking actions. In the emergency steering algorithm, we define
the collision hazard moment and propose a multi-constraint dynamic collision
avoidance planning method that considers the driving area. Simulation results
demonstrate that the decision-making collision avoidance logic can be applied
to dynamic collision avoidance scenarios in complex traffic situations,
effectively completing the obstacle avoidance task in emergency scenarios and
improving the safety of autonomous driving
- …