With growing numbers of intelligent autonomous systems in human environments, the ability
of such systems to perceive, understand, and anticipate human behavior becomes …

In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task
depending on many external factors. The topology of the scene and the interactions …

Human trajectory forecasting with multiple socially interacting agents is of critical importance
for autonomous navigation in human environments, eg, for self-driving cars and social …

This paper proposes a novel approach for predicting the motion of pedestrians interacting
with others. It uses a Generative Adversarial Network (GAN) to sample plausible predictions …

Driving Scene understanding is a key ingredient for intelligent transportation systems. To
achieve systems that can operate in a complex physical and social environment, they need …

The recent progress in autonomous vehicle research and development has led to
increasingly widespread testing of fully autonomous vehicles on public roads, where …

The ability to anticipate pedestrian actions on streets is a safety issue for intelligent cars and
has increasingly drawn the attention of the automotive industry. Estimating when …

We present a new task that predicts future locations of people observed in first-person
videos. Consider a first-person video stream continuously recorded by a wearable camera …

We present a novel Dynamic Bayesian Network for pedestrian path prediction in the
intelligent vehicle domain. The model incorporates the pedestrian situational awareness …

This paper highlights the role of humans in the next generation of driver assistance and
intelligent vehicles. Understanding, modeling, and predicting human agents are discussed …