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.

自动驾驶任务中，在复杂交通环境中进行轨迹预测需要遵循现实世界的环境条件和行为多模态。我们介绍了一种名为 Controllable Diffusion Trajectory (CDT) 的新型轨迹生成器，它将地图信息和社交交互融入到基于 Transformer 的条件去噪扩散模型中，以指导未来轨迹的预测。通过引入行为标记，如直行、向右转或向左转，确保轨迹呈现多模态，并且使用预测的终点作为替代行为标记，以促进准确轨迹的预测。在 Argoverse 2 基准测试中的大量实验表明，CDT 在复杂城市环境中生成多样且符合场景要求的轨迹。