Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that
focuses on the development of autonomous vehicles (AVs) that are capable of interacting …

Currently, autonomous vehicles (AVs) have gained considerable research interest in motion
planning (MP) to control driving. Deep learning (DL) is a subset of machine learning …

Current research on lane-changing trajectory planning typically focuses on the benefits of
the Subject Vehicle (SV), including factors such as lane-changing efficiency, comfort, and …

The automation of agricultural vehicles represents a significant field of interest in the
application of intelligent driving technologies. Agricultural vehicles operate in complex …

This research is seeking for design the autonomous vehicle's perception system from the
perspective of cyber-physical system. The perception system is considered a cyber and …

In scenarios characterized by significant curvature, such as curves, the presence of steering
system hysteresis and linearization of the vehicle model may lead to understeer and steady …

In the joint research project PRORETA 5, building blocks for automated driving in urban
areas have been developed, implemented, and tested. The developed blocks involve an …

Decision-making is a crucial task for autonomous driving. Taking thewrong decision might
cause a cartographical accident. Decision-making impliesplanning the appropriate behavior …

Driving on bumpy roads with potholes or bulges may severely compromise vehicle ride
comfort, while in extreme cases safety may be affected. Existing approaches mainly focus on …

Trajectory planning is an essential module of autonomous vehicles, and its planning
efficiency and robustness directly affect driving safety and ride comfort. The sampling-based …