Several key scenarios, such as intersection navigation, lane changing, and ramp merging,
are active areas of research in autonomous driving. In order to properly navigate these …

Lane changing systems have consistently received attention in the fields of vehicular
communication and autonomous vehicles. In this paper, we propose a lane change system …

Learning-based driving solution, a new branch for autonomous driving, is expected to
simplify the modeling of driving by learning the underlying mechanisms from data. To …

Decision-making is still a significant challenge to realize fully autonomous driving. Using
deep reinforcement learning (DRL) to solve autonomous driving decision-making problems …

In this article, a motion planning for autonomous driving on highway is studied. A high-level
motion planning controller with discrete action space is designed based on deep Q network …

The common pipeline in autonomous driving systems is highly modular and includes a
perception component which extracts lists of surrounding objects and passes these lists to a …

Deep Reinforcement Learning (DRL) has been successfully applied to learn policies for
safety-critical systems with unknown model dynamics in simulation. DRL controllers though …

In this work we propose a planning and acting architecture endowed with a module which
learns to select subgoals with Deep Q-Learning. This allows us to decrease the load of a …

In this work, the problem of path planning for an autonomous vehicle that moves on a
freeway is considered. The most common approaches that are used to address this problem …

Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …