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 …

We address one of the crucial aspects necessary for safe and efficient operations of
autonomous vehicles, namely predicting future state of traffic actors in the autonomous …

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 …

We present RobustTP, an end-to-end algorithm for predicting future trajectories of road-
agents in dense traffic with noisy sensor input trajectories obtained from RGB cameras …

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

We introduce a motion forecasting (behavior prediction) method that meets the latency
requirements for autonomous driving in dense urban environments without sacrificing …

This letter presents GAMMA, a general motion prediction model that enables large-scale
real-time simulation and planning for autonomous driving. GAMMA models heterogeneous …

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

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 …

We address the problem of ego-vehicle navigation in dense simulated traffic environments
populated by road agents with varying driver behaviors. Navigation in such environments is …