Intelligent vehicles and social robots need to navigate in crowded environments while
avoiding collisions with pedestrians. To achieve this, pedestrian trajectory prediction is …

While many works involving human-agent interactions have focused on individuals or
crowds, modelling interactions on the group scale has not been considered in depth …

Pedestrian trajectory prediction is an active research area with recent works undertaken to
embed accurate models of pedestrians social interactions and their contextual compliance …

While mobile edge computing (MEC) potentially supports the stringent latency requirements
for Virtual Reality (VR), previous research only considers minimizing the latency of …

Pedestrian trajectory prediction is a prominent research track that has advanced towards
modelling of crowd social and contextual interactions, with extensive usage of Long Short …

We provide an initial model and preliminary findings of a lookahead based local
optimization scheme for collision resolution between agents in large goal-directed crowd …

Goal directed agent navigation in crowd simulations involves a complex decision making
process. An agent must avoid all collisions with static or dynamic obstacles (such as other …

Abstract Human-Robot/Agent Interaction is well researched in many areas, but approaches
commonly either focus on dyadic interactions or crowd simulations. However, the …

We present a novel trajectory prediction algorithm for pedestrians based on a personality-
aware probabilistic feature map. This map is computed using a spatial query structure and …