  The ability to accurately predict feasible multimodal future trajectories of
surrounding traffic participants is crucial for behavior planning in autonomous
vehicles. The Motion Transformer (MTR), a state-of-the-art motion prediction
method, alleviated mode collapse and instability during training and enhanced
overall prediction performance by replacing conventional dense future endpoints
with a small set of fixed prior motion intention points. However, the fixed
prior intention points make the MTR multi-modal prediction distribution
over-scattered and infeasible in many scenarios. In this paper, we propose the
ControlMTR framework to tackle the aforementioned issues by generating
scene-compliant intention points and additionally predicting driving control
commands, which are then converted into trajectories by a simple kinematic
model with soft constraints. These control-generated trajectories will guide
the directly predicted trajectories by an auxiliary loss function. Together
with our proposed scene-compliant intention points, they can effectively
restrict the prediction distribution within the road boundaries and suppress
infeasible off-road predictions while enhancing prediction performance.
Remarkably, without resorting to additional model ensemble techniques, our
method surpasses the baseline MTR model across all performance metrics,
achieving notable improvements of 5.22% in SoftmAP and a 4.15% reduction in
MissRate. Our approach notably results in a 41.85% reduction in the
cross-boundary rate of the MTR, effectively ensuring that the prediction
distribution is confined within the drivable area.
