Active Learning-augmented Intention-aware Obstacle Avoidance of Autonomous Surface Vehicles in High-traffic Waters

TL;DR

This paper presents an active learning approach for intention-aware obstacle avoidance in autonomous surface vehicles, combining topological modeling, neural classification, and multi-objective optimization to improve safety in high-traffic waters.

Contribution

It introduces a novel topological model of obstacle intentions, integrates LSTM-based intention classification, and develops a multi-objective framework for real-time collision avoidance.

Findings

Successful collision avoidance in simulations and real-world tests.

Effective intention classification with LSTM neural networks.

Robust performance across various obstacle behaviors and environmental conditions.

Abstract

This paper enhances the obstacle avoidance of Autonomous Surface Vehicles (ASVs) for safe navigation in high-traffic waters with an active state estimation of obstacle's passing intention and reducing its uncertainty. We introduce a topological modeling of passing intention of obstacles, which can be applied to varying encounter situations based on the inherent embedding of topological concepts in COLREGs. With a Long Short-Term Memory (LSTM) neural network, we classify the passing intention of obstacles. Then, for determining the ASV maneuver, we propose a multi-objective optimization framework including information gain about the passing obstacle intention and safety. We validate the proposed approach under extensive Monte Carlo simulations (2,400 runs) with a varying number of obstacles, dynamic properties, encounter situations, and different behavioral patterns of obstacles (cooperative, non-cooperative). We also present the results from a real marine accident case study as well as real-world experiments of a real ASV with environmental disturbances, showing successful collision avoidance with our strategy in real-time.