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 …

We present a novel learning algorithm for action prediction and local navigation for
autonomous driving. Our approach classifies the driver behavior of other vehicles or road …

Traffic simulators are important tools in autonomous driving development. While continuous
progress has been made to provide developers more options for modeling various traffic …

Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …

Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can
be used for validation and verification of self-driving system performance without relying on …

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 …

Evaluating the performance of autonomous vehicle planning algorithms necessitates
simulating long-tail traffic scenarios. Traditional methods for generating safety-critical …

Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging
for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of …

In this work, we present a data-driven simulation and training engine capable of learning
end-to-end autonomous vehicle control policies using only sparse rewards. By leveraging …

Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …