In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …

Pedestrian action prediction has been a topic of active research in recent years resulting in
many new algorithmic solutions. However, measuring the overall progress towards solving …

Road traffic accidents involving collisions between vehicles and pedestrians are a major
cause of death and injury globally. With recent technological progress in the field of …

We present a novel method for vehicle-pedestrian path prediction that takes into account the
awareness of the driver and the pedestrian towards each other. The method jointly models …

Pedestrians are the most vulnerable road users and are at a high risk of fatal accidents.
Accurate pedestrian detection and effectively analyzing their intentions to cross the road are …

Passenger flow anomaly detection in urban rail transit networks (URTNs) is critical in
managing surging demand and informing effective operations planning and controls in the …

Accurate pedestrian trajectory prediction is significant and challenging for traffic-actor
protection and intelligent driving. However, most of the existing methods focus on pedestrian …

Anticipating human motion is an essential requirement for autonomous vehicles and robots
in order to primary guarantee people's safety. In urban scenarios, they interact with humans …

Human mobility prediction is of great significance for analyzing the check-in data generated
by location-based applications. Compared with classical prediction methods, recently …

This paper compares two models for context-based path prediction of objects with switching
dynamics: a Dynamic Bayesian Network (DBN) and a Recurrent Neural Network (RNN) …