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 …

Predicting vulnerableroad user behavior is an essential prerequisite for deploying
Automated Driving Systems (ADS) in the real-world. Pedestrian crossing intention should be …

Autonomous driving has rapidly developed and shown promising performance due to recent
advances in hardware and deep learning techniques. High-quality datasets are fundamental …

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 …

Anticipating pedestrian crossing behavior in urban scenarios is a challenging task for
autonomous vehicles. Early this year, a benchmark comprising JAAD and PIE datasets have …

Predicting future trajectories for other road agents is an essential task for autonomous
vehicles. Established trajectory prediction methods primarily use agent tracks generated by …

Pedestrian behavior prediction is one of the major challenges for intelligent driving systems
in urban environments. Pedestrians often exhibit a wide range of behaviors and adequate …

Automated vehicles require a comprehensive understanding of traffic situations to ensure
safe and anticipatory driving. In this context, the prediction of pedestrians is particularly …

In this paper, we delve into the pedestrian behavior understanding problem from the
perspective of three different tasks: intention estimation, action prediction, and event risk …

Autonomous robots that can perform common tasks like driving, surveillance, and chores
have the biggest potential for impact due to frequency of usage, and the biggest potential for …