Efficient and effective exploration in continuous space is a central problem in applying
reinforcement learning (RL) to autonomous driving. Skills learned from expert …

Reinforcement learning (RL) has shown promising performance in autonomous driving
applications in recent years. The early end-to-end RL method is usually unexplainable and …

Combining data-driven applications with control systems plays a key role in recent
Autonomous Car research. This thesis offers a structured review of the latest literature on …

Applying reinforcement learning to autonomous driving entails particular challenges,
primarily due to dynamically changing traffic flows. To address such challenges, it is …

In human-aware navigation, the robot tacitly games with humans, balancing safety and
efficiency according to human intentions. Poor balance or bad intent recognition causes the …

Exploring topologically distinctive trajectories provides more options for robot motion
planning. Since computing time grows greatly with environment complexity, improving …

To handle dynamic and static situations in robots using deep learning, the behavior of
dynamic obstacles is manually modeled. This is limited in generating diverse situations and …

In the narrow lane scene of autonomous driving, it is critical for the ego car to recognize the
intentions of social vehicles and cooperate with them. However, cooperating with social …

This thesis proposes methods for performing autonomous navigation with a topological map
and a vision sensor in a parking lot. These methods are necessary to complete fully …

In the smart city concept, an important place is occupied by unmanned vehicles, which are
considered as agents of multi-agent systems. The main tasks in the management of …