Modeling the driving behavior of traffic partici-pants in highly interactive traffic situations,
such as roundabouts, poses a significant challenge due to the complex interactions and the …

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 …

Intersections are key nodes and also bottlenecks of urban road networks, so improving the
traffic efficiency at intersections is beneficial to improving overall traffic throughput and …

Behavior prediction of traffic actors is an essential component of any real-world self-driving
system. Actors' long-term behaviors tend to be governed by their interactions with other …

Predicting other drivers' trajectories is challenging. We address the issue by introducing a
method to derive a driving policy based on multi-agent reinforcement learning. For this, we …

Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging
for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of …

Autonomous driving involves complex decision-making in highly interactive environments,
requiring thoughtful negotiation with other traffic participants. While reinforcement learning …

In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …

With the advent of machine learning, several autonomous driving tasks have become easier
to accomplish. Nonetheless, the proliferation of autonomous vehicles in urban traffic …

Human driving involves an inherent discrete layer in decision-making corresponding to
specific maneuvers such as overtaking, lane changing, lane keeping, etc. This is sensible to …