Forecasting the scalable future states of surrounding traffic participants in complex traffic
scenarios is a critical capability for autonomous vehicles, as it enables safe and feasible …

Learning-based approaches to autonomous vehicle planners have the potential to scale to
many complicated real-world driving scenarios by leveraging huge amounts of driver …

Autonomous driving systems require the ability to fully understand and predict the
surrounding environment to make informed decisions in complex scenarios. Recent …

Our goal is to train a neural planner that can capture diverse driving behaviors in complex
urban scenarios. We observe that even state-of-the-art neural planners are struggling to …

Machine Learning (ML) has replaced traditional handcrafted methods for perception and
prediction in autonomous vehicles. Yet for the equally important planning task, the adoption …

Motion planning is crucial for safe navigation in complex urban environments. Historically,
motion planners (MPs) have been evaluated with procedurally-generated simulators like …

We present PLUTO, a powerful framework that pushes the limit of imitation learning-based
planning for autonomous driving. Our improvements stem from three pivotal aspects: a …

In autonomous driving, an accurate understanding of the environment, eg, the vehicle-to-
vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks, such as …

Recent advancements in motion planning for Autonomous Vehicles (AVs) show great
promise in using expert driver behaviors in non-stationary driving environments. However …

Manually specifying features that capture the diversity in traffic environments is impractical.
Consequently, learning-based agents cannot realize their full potential as neural motion …