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 …

In the most of previous studies, social interactions between vehicles are not considered
explicitly when designing decision making and motion planning module. Nevertheless, the …

It is essential for autonomous driving cars to understand and predict other surrounding cars'
behaviors, especially in urban environments, due to the high traffic volumes and complex …

Autonomous agents that drive on roads shared with human drivers must reason about the
nuanced interactions among traffic participants. This poses a highly challenging decision …

The use of reinforcement learning (RL) in real-world domains often requires extensive effort
to ensure safe behavior. While this compromises the autonomy of the system, it might still be …

Reinforcement learning (RL) has recently been used for solving challenging decision-
making problems in the context of automated driving. However, one of the main drawbacks …

In this work, we use the communication of intent as a means to facilitate cooperation
between autonomous vehicle agents. Generally speaking, intents can be any reliable …

Highway merging is a challenging task for an autonomous vehicle, because the vehicle
must interact with other vehicles to identify a gap to merge into before the ending of current …

Ramp merging is a critical maneuver for road safety and traffic efficiency. Most of the current
automated driving systems developed by multiple automobile manufacturers and suppliers …

Despite the success of AI-enabled onboard perception, on-ramp merging has been one of
the main challenges for autonomous driving. Due to limited sensing range of onboard …