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Found 15 papers, 5 papers with code
Towards Autonomous Driving with Small-Scale Cars: A Survey of Recent Development
While engaging with the unfolding revolution in autonomous driving, a challenge presents itself, how can we effectively raise awareness within society about this transformative trend?
Self-organized arrival system for urban air mobility
Urban air mobility is an innovative mode of transportation in which electric vertical takeoff and landing (eVTOL) vehicles operate between nodes called vertiports.
Two-step dynamic obstacle avoidance
Dynamic obstacle avoidance (DOA) is a fundamental challenge for any autonomous vehicle, independent of whether it operates in sea, air, or land.
2-Level Reinforcement Learning for Ships on Inland Waterways
This paper proposes a realistic modularized framework for controlling autonomous surface vehicles (ASVs) on inland waterways (IWs) based on deep reinforcement learning (DRL).
Vision-based DRL Autonomous Driving Agent with Sim2Real Transfer
To the best of our knowledge, our vision-based car following and lane keeping agent with Sim2Real transfer capability is the first of its kind.
A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer in Autonomous Driving
Deep Reinforcement Learning (DRL) has shown remarkable success in solving complex tasks across various research fields.
Adaptive local VAR for dynamic economic policy uncertainty spillover
The availability of data on economic uncertainty sparked a lot of interest in models that can timely quantify episodes of international spillovers of uncertainty.
Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk
This paper proposes a general training environment where we gain control over the difficulty of the obstacle avoidance task by using short training episodes and assessing the difficulty by two metrics: The number of obstacles and a collision risk metric.
Spatial-temporal recurrent reinforcement learning for autonomous ships
This paper proposes a spatial-temporal recurrent neural network architecture for deep $Q$-networks that can be used to steer an autonomous ship.
Vessel-following model for inland waterways based on deep reinforcement learning
While deep reinforcement learning (RL) has been increasingly applied in designing car-following models in the last years, this study aims at investigating the feasibility of RL-based vehicle-following for complex vehicle dynamics and strong environmental disturbances.
Vulnerability-CoVaR: Investigating the Crypto-market
This paper proposes an important extension to Conditional Value-at-Risk (CoVaR), the popular systemic risk measure, and investigates its properties on the cryptocurrency market.
Addressing Maximization Bias in Reinforcement Learning with Two-Sample Testing
Value-based reinforcement-learning algorithms have shown strong results in games, robotics, and other real-world applications.
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator
In the autonomous driving field, fusion of human knowledge into Deep Reinforcement Learning (DRL) is often based on the human demonstration recorded in a simulated environment.
Missing Velocity in Dynamic Obstacle Avoidance based on Deep Reinforcement Learning
Filling a gap in the current literature, we thoroughly investigate the effect of missing velocity information on an agent's performance in obstacle avoidance tasks.
Formulation and validation of a car-following model based on deep reinforcement learning
For various parameterizations of the reward functions, and for a wide variety of artificial and real leader data, the model turned out to be unconditionally string stable, comfortable, and crash-free.
