This work introduces interactive traffic scenarios in the CARLA simulator, which are based
on real-world traffic. We concentrate on tactical tasks lasting several seconds, which are …

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 …

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 …

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 …