Data-driven simulation has become a favorable way to train and test autonomous driving algorithms. The idea of replacing the actual environment with a learned simulator has also …
The paper presents a simple yet powerful and intelligent driver agent, designed to operate in a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. The …
Recent advances in deep reinforcement learning (RL) offer an opportunity to revisit complex traffic control problems at the level of vehicle dynamics, with the aim of learning locally …
This article investigates the feasibility of implementing a reinforcement learning agent able to plan the trajectory of a simple automated vehicle 2D model in a motorway simulation. The …
We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of …
L Forneris, A Pighetti… - International …, 2023 - journal.seriousgamessociety.org
We propose a new, hierarchical architecture for behavioral planning of vehicle models usable as realistic non-player vehicles in serious games related to traffic and driving. These …
Y Koeberle, S Sabatini, D Tsishkou… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
Traffic simulation has gained a lot of interest for quantitative evaluation of self driving vehicles performance. In order for a simulator to be a valuable test bench, it is required that …
The paper presents a microscopic highway simulation model, built as an environment for the development of different machine learning based autonomous vehicle controllers. The …
The use of reinforcement learning (RL) in real-world domains often requires extensive effort to ensure safe behavior. While this compromises the autonomy of the system, it might still be …