Simulators offer the possibility of safe, low-cost development of self-driving systems.
However, current driving simulators exhibit naïve behavior models for background traffic …

In recent years self-driving vehicles have become more commonplace on public roads, with
the promise of bringing safety and efficiency to modern transportation systems. Increasing …

Imitation learning (IL) is a simple and powerful way to use high-quality human driving data,
which can be collected at scale, to produce human-like behavior. However, policies based …

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 …

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

Autonomous urban driving navigation with complex multi-agent dynamics is under-explored
due to the difficulty of learning an optimal driving policy. The traditional modular pipeline …

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 …

The ability to accurately predict and simulate human driving behavior is critical for the
development of intelligent transportation systems. Traditional modeling methods have …

Our goal is to train a policy for autonomous driving via imitation learning that is robust
enough to drive a real vehicle. We find that standard behavior cloning is insufficient for …

Simulation is the key to scaling up validation and verification for robotic systems such as
autonomous vehicles. Despite advances in high-fidelity physics and sensor simulation, a …