Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to
make safe decisions. Existing works explore to directly predict future trajectories based on …

Predicting the future behavior of road users is one of the most challenging and important
problems in autonomous driving. Applying deep learning to this problem requires fusing …

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 …

Transformers, originally devised for natural language processing (NLP), have also produced
significant successes in computer vision (CV). Due to their strong expression power …

Predicting the motion of multiple agents is necessary for planning in dynamic environments.
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …

In this paper, we address the problem of predicting the future motion of a dynamic agent
(called a target agent) given its current and past states as well as the information on its …

In this paper, we present LookOut, a novel autonomy system that perceives the environment,
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …

Despite the advancements in the technologies of autonomous driving, it is still challenging to
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …

Predicting human trajectories is an important component of autonomous moving platforms,
such as social robots and self-driving cars. Human trajectories are affected by both the …

Forecasting future trajectories of agents in complex traffic scenes requires reliable and
efficient predictions for all agents in the scene. However, existing methods for trajectory …