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Deep Occupancy-Predictive Representations for Autonomous Driving
Manually specifying features that capture the diversity in traffic environments is impractical.
Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric
Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants and the underlying road infrastructure.
Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning
Autonomous systems are becoming ubiquitous and gaining momentum within the marine sector.
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning
Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics.
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions with non-moving obstacles along the way.
