Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially,
in the presence of many aggressive, high-speed traffic participants. This paper presents …

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

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 …

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 …

Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a
very challenging task. This is because human movement and interactions are very hard to …

We present a novel learning-based collision avoidance algorithm, CrowdSteer, for mobile
robots operating in dense and crowded environments. Our approach is end-to-end and uses …

Autonomous driving in a crowded environment, eg, a busy traffic intersection, is an unsolved
challenge for robotics. The robot vehicle must contend with a dynamic and partially …

Dense urban traffic environments can produce situations where accurate prediction and
dynamic models are insufficient for successful autonomous vehicle motion planning. We …

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

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