  Trajectory generation is a pivotal task in autonomous driving. Recent studies
have introduced the autoregressive paradigm, leveraging the state transition
model to approximate future trajectory distributions. This paradigm closely
mirrors the real-world trajectory generation process and has achieved notable
success. However, its potential is limited by the ineffective representation of
realistic trajectories within the redundant state space. To address this
limitation, we propose the Kinematic-Driven Generative Model for Realistic
Agent Simulation (KiGRAS). Instead of modeling in the state space, KiGRAS
factorizes the driving scene into action probability distributions at each time
step, providing a compact space to represent realistic driving patterns. By
establishing physical causality from actions (cause) to trajectories (effect)
through the kinematic model, KiGRAS eliminates massive redundant trajectories.
All states derived from actions in the cause space are constrained to be
physically feasible. Furthermore, redundant trajectories representing identical
action sequences are mapped to the same representation, reflecting their
underlying actions. This approach significantly reduces task complexity and
ensures physical feasibility. KiGRAS achieves state-of-the-art performance in
Waymo's SimAgents Challenge, ranking first on the WOMD leaderboard with
significantly fewer parameters than other models. The video documentation is
available at \url{https://kigras-mach.github.io/KiGRAS/}.
