Autonomous decision making and motion planning in complex dynamic traffic environments,
such as left-turn without traffic signals and multi-lane merging from side-ways, are still …

Motion prediction is crucial in enabling safe motion planning for autonomous vehicles in
interactive scenarios. It allows the planner to identify potential conflicts with other traffic …

Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …

Accurate prediction of the driving intentions and trajectories of other vehicles is critical to the
planning and control subsystem of the autonomous driving system. In addition to the driver's …

Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …

Accurate trajectory prediction is an essential task in automated driving, which is achieved by
sensing and analyzing the behavior of surrounding vehicles. Although plenty of research …

Reinforcement learning (RL) methods have gained popularity in the field of motion planning
for autonomous vehicles due to their success in robotics and computer games. However, no …

In the realm of autonomous driving, motion planning for the ego vehicle necessitates the
prediction of surrounding vehicles' motions. This prediction traditionally relies on object …

Behavior and motion planning play an important role in automated driving. Traditionally,
behavior planners instruct local motion planners with predefined behaviors. Due to the high …

Safe and human‐like trajectory planning is crucial for self‐driving cars. While model‐based
planning has demonstrated reliability, it is beneficial to incorporate human demonstrations …