Here are several examples of real-world cut-ins. TreeIRL anticipates the cut-in and brakes comfortably, while the other baselines either brake too late or brake uncomfortably (see inset history of vehicle kinematics).

Tree achieves 1-2 orders of magnitude improvement in safety, while also improving comfort and progress! On the road, it is by far the best planner.

We feed the MCTS trajectories into a deep scoring function trained with IRL to choose the most human-like among them.

The IRL network is trained on many hours of human export demonstrations to effectively reverse-engineer the intrinsic reward function of human driving.

MCTS uses search + ML to efficiently explore combinatorially large search spaces. In most applications (e.g. AlphaGo), MCTS outputs a single next best action.

The main innovation is to reporpose MCTS to ouput a *set of possible sequences* of actions (i.e., trajectories).

Excited to share a new preprint based on my work this past year:

**TreeIRL** is a novel planner that combines classical search with learning-based methods to achieve state-of-the-art performance in simulation and in **real-world autonomous driving**! 🚘 🤖 🚀