To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make
responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles …

We present a new algorithm for predicting the near-term trajectories of road agents in dense
traffic videos. Our approach is designed for heterogeneous traffic, where the road agents …

Predicting trajectories of multiple agents in interactive driving scenarios such as
intersections, and roundabouts are challenging due to the high density of agents, varying …

Data-driven simulation has become a favorable way to train and test autonomous driving
algorithms. The idea of replacing the actual environment with a learned simulator has also …

To safely and rationally participate in dense and heterogeneous traffic, autonomous vehicles
require to sufficiently analyze the motion patterns of surrounding traffic-agents and …

Despite the advancement in the technology of autonomous driving cars, the safety of a self-
driving car is still a challenging problem that has not been well studied. Motion prediction is …

To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles
need to have the ability to predict the future motion of surrounding vehicles. Multiple …

It is of great interest for autonomous vehicles to predict the trajectory of other vehicles when
planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the …

There is a dilemma regarding the accuracy and reality of vehicle trajectory prediction.
Balancing and predicting the effective trajectory is a topic of debate in autonomous driving …

Predicting the future motion of traffic agents is crucial for safe and efficient autonomous
driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts …