Autonomous vehicles (AVs) have the potential to significantly revolutionize society by
providing a secure and efficient mode of transportation. Recent years have witnessed …

Autonomous driving has attracted significant research interests in the past two decades as it
offers many potential benefits, including releasing drivers from exhausting driving and …

Human action anticipation aims at predicting what people will do in the future based on past
observations. In this paper we introduce Uncertainty-aware Action Decoupling Transformer …

We present a new method for multi-agent planning involving human drivers and
autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging …

The development of autonomous driving models through reinforcement learning has gained
significant traction. However, developing obstacle avoidance systems remains a challenge …

Early action prediction aims to successfully predict the class label of an action before it is
completely performed. This is a challenging task because the beginning stages of different …

We propose an extension to the MAPF formulation, called SocialMapf, to account for private
incentives of agents in constrained environments such as doorways, narrow hallways, and …

Vehicle trajectory anomaly detection plays an essential role in the fields of traffic video
surveillance, autonomous driving navigation, and taxi fraud detection. Deep generative …

Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic
environment composed of human drivers and do not adapt to local conditions and socio …

Despite the structure of road environments, imposed via geometry and rules, traffic flows
exhibit complex multiagent dynamics. Reasoning about such dynamics is challenging due to …