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 …
Y Jaafra, A Deruyver, JL Laurent… - 2019 18th IEEE …, 2019 - ieeexplore.ieee.org
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 …
J Chen, T Wu, M Shi, W Jiang - Sensors, 2020 - mdpi.com
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 …