Diffusion With Reinforcement Learning for Pedestrian Trajectory Prediction

The trajectories of pedestrian movements involve uncertainty, requiring a predictive probability model capable of modeling the underlying multimodality. To predict the trajectory of pedestrian, most existing methods try to learn the probability distribution of real pedestrian trajectories and then independently sample multiple times from this distribution to obtain a set of possible future paths. However, naively learning the distribution of real-world trajectories leads to sub-optimal results. In this paper, we design a model-agnostic reinforcement learning-based framework for pedestrian trajectory prediction. This framework models pedestrian trajectory generation as a denoising process, which is further formulated as a multi-step decision-making process. In our framework, we subtly design a reward function, which is used to optimize the diffusion model with policy-based reinforcement learning. We make evaluation on multiple benchmark datasets, including ETH/UCY and SDD datasets, where our approach achieves promising results. Our source code will be released at: https://github.com/ustc-yaojinchen/DRL-for-PTP