  Predicting pedestrian trajectories is crucial for improving the safety and
effectiveness of autonomous driving and mobile robots. However, this task is
nontrivial due to the inherent stochasticity of human motion, which naturally
requires the predictor to generate multi-model prediction. Previous works have
used various generative methods, such as GAN and VAE, for pedestrian trajectory
prediction. Nevertheless, these methods may suffer from problems, including
mode collapse and relatively low-quality results. The denoising diffusion
probabilistic model (DDPM) has recently been applied to trajectory prediction
due to its simple training process and powerful reconstruction ability.
However, current diffusion-based methods are straightforward without fully
leveraging input information and usually require many denoising iterations
leading to a long inference time or an additional network for initialization.
To address these challenges and promote the application of diffusion models in
trajectory prediction, we propose a novel scene-aware multi-modal pedestrian
trajectory prediction framework called GBD. GBD combines goal prediction with
the diffusion network. First, the goal predictor produces multiple goals, and
then the diffusion network generates multi-modal trajectories conditioned on
these goals. Furthermore, we introduce a new diffusion sampling algorithm named
tree sampling (TS), which leverages common feature to reduce the inference time
and improve accuracy for multi-modal prediction. Experimental results
demonstrate that our GBD-TS method achieves state-of-the-art performance with
real-time inference speed.
