Multi-agent reinforcement learning (MARL) methods have emerged as a promising solution
for multi-agent collaborative driving in the intersection and roundabout scenarios. However …

Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road
participants, AVs have to accurately predict the behavior of surrounding vehicles and plan …

Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …

In order to generate favorable trajectories, road users need to cope with interaction among
them, especially in dense traffic. Thus, for autonomous cars, the intention of involved …

Predicting future trajectories of pairwise traffic agents in highly interactive scenarios, such as
cut-in, yielding, and merging, is challenging for autonomous driving. The existing works …

Driving is a social activity: drivers often indicate their intent to change lanes via motion cues.
We consider mixed-autonomy traffic where a Human-driven Vehicle (HV) and an …

Autonomous vehicles should be able to predict the future states of its environment and
respond appropriately. Specifically, predicting the behavior of surrounding human drivers is …

Highly automated driving requires effective handling of many complex scenarios. Here we
study a specific important task of highly automated driving: merging into traffic from a …

To achieve safe cooperative driving in mixed traffic of manned and unmanned vehicles, it is
necessary to understand and model human drivers' driving behaviors. This paper proposed …

Modeling other drivers' behavior in highly interactive traffic situations, such as roundabouts,
is a challenging task. We address this task using a Multi-Agent Reinforcement Learning …