Automated driving has the potential to revolutionize personal, public, and freight mobility.
Besides the enormous challenge of perception, ie accurately perceiving the environment …

The release of nuPlan marks a new era in vehicle motion planning research, offering the first
large-scale real-world dataset and evaluation schemes requiring both precise short-term …

In this paper, we present LookOut, a novel autonomy system that perceives the environment,
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …

Automated driving has the potential to revolutionize personal, public, and freight mobility.
Beside accurately perceiving the environment, automated vehicles must plan a safe …

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 …

Autonomous vehicles (AVs) need to reason about the multimodal behavior of neighboring
agents while planning their own motion. Many existing trajectory planners seek a single …

Contingency planning, wherein an agent generates a set of possible plans conditioned on
the outcome of an uncertain event, is an increasingly popular way for robots to act under …

Offline reinforcement learning (RL) provides a framework for learning decision-making from
offline data and therefore constitutes a promising approach for real-world applications as …

Robots in complex multi-agent environments should reason about the intentions of observed
and currently unobserved agents. In this paper, we present a new learning-based method …

There has been tremendous progress in the development of individual modules of the
standard perception-prediction-planning robot autonomy stack. However, the principled …