Reinforcement learning (RL) is revolutionizing the artificial intelligence (AI) domain and
significantly aiding in building autonomous systems with a higher level comprehension of …

Autonomous driving is still considered as an “unsolved problem” given its inherent important
variability and that many processes associated with its development like vehicle control and …

Traditional autonomous vehicle pipelines are highly modularized with different subsystems
for localization, perception, actor prediction, planning, and control. Though this approach …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years,
with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed …

Reinforcement learning (RL) methods achieved major advances in multiple tasks
surpassing human performance. However, most of RL strategies show a certain degree of …

Deep Reinforcement Learning (DRL) enables agents to make decisions based on a well-
designed reward function that suites a particular environment without any prior knowledge …

Lane-free traffic is a novel research domain, in which vehicles no longer adhere to the
notion of lanes, and consider the whole lateral space within the road boundaries. This …

Autonomous driving with artificial intelligence technology has been viewed as promising for
autonomous vehicles hitting the road in the near future. In recent years, considerable …

Reinforcement learning (RL) shows promise for autonomous driving decision-making.
However, designing appropriate reward functions to guide RL agents towards complex …