No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …

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 …

Autonomous driving is considered one of the revolutionary technologies shaping humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …

The field of trajectory forecasting has grown significantly in recent years, partially owing to
the release of numerous large-scale, real-world human trajectory datasets for autonomous …

Learning-based behavior prediction methods are increasingly being deployed in real-world
autonomous systems, eg, in fleets of self-driving vehicles, which are beginning to …

Multi-agent trajectory prediction is crucial for various practical applications, spurring the
construction of many large-scale trajectory datasets, including vehicles and pedestrians …

Predicting the future trajectories of robots' nearby objects plays a pivotal role in applications
such as autonomous driving. While learning-based trajectory prediction methods have …

Accurate trajectory prediction is essential for safe and efficient autonomous driving in
complex traffic environments. While artificial intelligence has shown great promise in …

Trajectory prediction is a challenging problem that requires considering interactions among
multiple actors and the surrounding environment. While data-driven approaches have been …

Vehicle trajectory prediction has increasingly relied on data-driven solutions, but their ability
to scale to different data domains and the impact of larger dataset sizes on their …