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 …

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

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 …

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 …

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 …

Although planning is a crucial component of the autonomous driving stack, researchers
have yet to develop robust planning algorithms that are capable of safely handling the …

Real-world autonomous driving systems must make safe decisions in the face of rare and
diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world …

Motion prediction and cost evaluation are vital components in the decision-making system of
autonomous vehicles. However, existing methods often ignore the importance of cost …

In this paper, we propose a neural motion planner for learning to drive autonomously in
complex urban scenarios that include traffic-light handling, yielding, and interactions with …

The motion planners used in self-driving vehicles need to generate trajectories that are safe,
comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior …