Autonomous cars have to navigate in dynamic environment which can be full of
uncertainties. The uncertainties can come either from sensor limitations such as occlusions …

Accurately predicting future behaviors of surrounding vehicles is an essential capability for
autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others …

We present a novel autonomous driving system which uses the road contextual information
and intentions of other road users for urban driving. Unlike highways, urban environments …

As more and more autonomous vehicles (AVs) are being deployed on public roads,
designing socially compatible behaviors for them is becoming increasingly important. In …

In this paper, a novel approach is presented for modeling the interaction dynamics between
an ego car and a bicycle in a traffic scenario using a hybrid reinforcement learning …

Efficient intersection planning is one of the most challenging tasks for an autonomous
vehicle at present. Politeness to other traffic participants and reaction to surrounding …

Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …

Motion prediction modules are crucial for autonomous vehicles to forecast the future
behavior of surrounding road users. Failures in prediction modules can mislead a …

Autonomous vehicles (AVs) need to interact with other traffic participants who can be either
cooperative or aggressive, attentive or inattentive. Such different characteristics can lead to …

Behavioral decision making is an important aspect of autonomous vehicles (AV). In this
work, we propose a behavior planning structure based on hierarchical reinforcement …