Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

It is critical to predict the motion of surrounding vehicles for self-driving planning, especially
in a socially compliant and flexible way. However, future prediction is challenging due to the …

The computational effort of trajectory planning for automated vehicles often increases with
the complexity of the traffic situation. This is particularly problematic in safety-critical …

In this article, we present an efficient planning system for automated vehicles in highly
interactive environments (EPSILON). EPSILON is an efficient interaction-aware planning …

Decision making in dense traffic can be challenging for autonomous vehicles. An
autonomous system only relying on predefined road priorities and considering other drivers …

For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with
human-driven vehicles. Their planning and control systems need extensive testing …

Behavior planning in urban environments must consider the various existing uncertainties in
an explicit way. This work proposes a behavior planner, based on a POMDP formulation …

Car-following (CF) algorithms are crucial components of traffic simulations and have been
integrated into many production vehicles equipped with Advanced Driving Assistance …

Motion planning at urban intersections that accounts for the situation context, handles
occlusions, and deals with measurement and prediction uncertainty is a major challenge on …

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