Autonomous navigation requires structured representation of the road network and instance-
wise identification of the other traffic agents. Since the traffic scene is defined on the ground …

Abstract In most classical Autonomous Vehicle (AV) stacks, the prediction and planning
layers are separated, limiting the planner to react to predictions that are not informed by the …

Abstract Knowledge of the road network topology is crucial for autonomous planning and
navigation. Yet, recovering such topology from a single image has only been explored in …

Autonomous driving requires accurate local scene understanding information. To this end,
autonomous agents deploy object detection and online BEV lane graph extraction methods …

The extraction of road network is essential for the generation of high-definition maps since it
enables the precise localization of road landmarks and their interconnections. However …

Vehicle Trajectory Prediction (VTP) is one of the key issues in the field of autonomous
driving. In recent years, more researchers have tried applying Deep Learning methods and …

In most classical Autonomous Vehicle (AV) stacks, the prediction and planning layers are
separated, limiting the planner to react to predictions that are not informed by the planned …

Autonomous driving requires a structured understanding of the surrounding road network to
navigate. One of the most common and useful representation of such an understanding is …

Restoring an accurate Bird's Eye View (BEV) map plays a crucial role in the perception of
autonomous driving. The existing works of lifting representations from frontal view to BEV …

Motion prediction (MP) of multiple agents is a crucial task in arbitrarily complex
environments, from social robots to self-driving cars. Current approaches tackle this problem …