Featured Application The presented method can be used as a real-time trajectory following
algorithm for autonomous vehicles using prediction based on lookahead information. …

The rapid growth of urbanization and the constant demand for mobility have put a great
strain on transportation systems in cities. One of the major challenges in these areas is traffic …

Malfunctions, congestions, and accidents occur in every railway system from time to time,
which influences the railway traffic on a given section of the system. The disturbance may …

Value-based Reinforcement Learning algorithms achieve superior performance by utilizing
experiences gathered in the past to update their so-called value-function. In most cases, it is …

The traffic signal control problem is an extensively researched area providing different
approaches, from classic methods to machine learning based ones. Different aspects can be …

The transportation industry is one of the main contributors to global warming since it is
responsible for a quarter of greenhouse gas emissions. Due to society’s crucial dependence on …

The paper introduces a novel approach to the classical adaptive traffic signal control (TSC)
problem. Instead of the traditional optimization or simple rule-based approach, Artificial …

With increasing realised traffic on transport networks, greenhouse gas emissions show a
similar trend. Reducing them is a modern aspiration, creating a better place to live and moving …

In recent years, Reinforcement Learning (RL) has excelled in the realm of autonomous vehicle
control, which is distinguished by the absence of limitations, such as specific training data …

Reinforcement Learning has gained tremendous attention recently, thanks to its excellent
solutions in several challenging domains. However, the formulation of the reward signal is …