  Motion planning is crucial for safe navigation in complex urban environments.
Historically, motion planners (MPs) have been evaluated with
procedurally-generated simulators like CARLA. However, such synthetic
benchmarks do not capture real-world multi-agent interactions. nuPlan, a
recently released MP benchmark, addresses this limitation by augmenting
real-world driving logs with closed-loop simulation logic, effectively turning
the fixed dataset into a reactive simulator. We analyze the characteristics of
nuPlan's recorded logs and find that each city has its own unique driving
behaviors, suggesting that robust planners must adapt to different
environments. We learn to model such unique behaviors with BehaviorNet, a graph
convolutional neural network (GCNN) that predicts reactive agent behaviors
using features derived from recently-observed agent histories; intuitively,
some aggressive agents may tailgate lead vehicles, while others may not. To
model such phenomena, BehaviorNet predicts parameters of an agent's motion
controller rather than predicting its spacetime trajectory (as most forecasters
do). Finally, we present AdaptiveDriver, a model-predictive control (MPC) based
planner that unrolls different world models conditioned on BehaviorNet's
predictions. Our extensive experiments demonstrate that AdaptiveDriver achieves
state-of-the-art results on the nuPlan closed-loop planning benchmark, reducing
test error from 6.4% to 4.6%, even when applied to never-before-seen cities.
