Controllable Diverse Sampling for Diffusion Based Motion Behavior
  Forecasting
Yiming Xu Hao Cheng Monika Sester
In autonomous driving tasks, trajectory prediction in complex traffic
environments requires adherence to real-world context conditions and behavior
multimodalities. Existing methods predominantly rely on prior assumptions or
generative models trained on curated data to learn road agents' stochastic
behavior bounded by scene constraints. However, they often face mode averaging
issues due to data imbalance and simplistic priors, and could even suffer from
mode collapse due to unstable training and single ground truth supervision.
These issues lead the existing methods to a loss of predictive diversity and
adherence to the scene constraints. To address these challenges, we introduce a
novel trajectory generator named Controllable Diffusion Trajectory (CDT), which
integrates map information and social interactions into a Transformer-based
conditional denoising diffusion model to guide the prediction of future
trajectories. To ensure multimodality, we incorporate behavioral tokens to
direct the trajectory's modes, such as going straight, turning right or left.
Moreover, we incorporate the predicted endpoints as an alternative behavioral
token into the CDT model to facilitate the prediction of accurate trajectories.
Extensive experiments on the Argoverse 2 benchmark demonstrate that CDT excels
in generating diverse and scene-compliant trajectories in complex urban
settings.