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 …

Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires
reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

The indeterminate nature of human motion requires trajectory prediction systems to use a
probabilistic model to formulate the multi-modality phenomenon and infer a finite set of …

Pedestrian trajectory prediction is critical in many vision tasks but challenging due to the
multimodality of the future trajectory. Most existing methods predict multimodal trajectories …

Trajectory prediction is a crucial undertaking in understanding entity movement or human
behavior from observed sequences. However, current methods often assume that the …

Lately, Autonomous vehicles (AV) have been gaining traction globally owing to their huge
social, economic and environmental benefits. However, the rising safety apprehensions for …

We present RAFTformer, a real-time action forecasting transformer for latency aware real-
world action forecasting applications. RAFTformer is a two-stage fully transformer based …

Understanding and forecasting future trajectories of agents are critical for behavior analysis,
robot navigation, autonomous cars, and other related applications. Previous methods mostly …

Motion forecasting plays a significant role in various domains (eg, autonomous driving,
human-robot interaction), which aims to predict future motion sequences given a set of …