In this paper, we tackle the problem of detecting objects in 3D and forecasting their future
motion in the context of self-driving. Towards this goal, we design a novel approach that …

Autonomous vehicles operating in complex real-world environments require accurate
predictions of interactive behaviors between traffic participants. This paper tackles the …

Anticipating possible behaviors of traffic participants is an essential capability of
autonomous vehicles. Many behavior detection and maneuver recognition methods only …

Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory
prediction is challenging because it requires reasoning about agents' past movements …

In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the
perception and motion forecasting performance of self-driving vehicles. By intelligently …

The ability to reliably perceive the environmental states, particularly the existence of objects
and their motion behavior, is crucial for autonomous driving. In this work, we propose an …

Predicting future trajectories of traffic agents in highly interactive environments is an
essential and challenging problem for the safe operation of autonomous driving systems. On …

Predicting the behaviors of other agents on the road is critical for autonomous driving to
ensure safety and efficiency. However, the challenging part is how to represent the social …

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 …

One of the major challenges for autonomous vehicles in urban environments is to
understand and predict other road users' actions, in particular, pedestrians at the point of …