Navigating urban environments represents a complex task for automated vehicles. They
must reach their goal safely and efficiently while considering a multitude of traffic …

An open problem in autonomous vehicle safety validation is building reliable models of
human driving behavior in simulation. This work presents an approach to learn neural …

Safety and efficiency are two key elements for planning and control in autonomous driving.
Theoretically, model-based optimization methods, such as Model Predictive Control (MPC) …

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

Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …

Simulation is an appealing option for validating the safety of autonomous vehicles.
Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn …

In this paper, we consider autonomous driving of a vehicle using imitation learning.
Generative adversarial imitation learning (GAIL) is a widely used algorithm for imitation …

Traditional planning and control methods could fail to find a feasible trajectory for an
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …

We demonstrate the first large-scale application of model-based generative adversarial
imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard …

This paper investigates the vision-based autonomous driving with deep learning and
reinforcement learning methods. Different from the end-to-end learning method, our method …