  Trajectory prediction is an essential component in autonomous driving,
particularly for collision avoidance systems. Considering the inherent
uncertainty of the task, numerous studies have utilized generative models to
produce multiple plausible future trajectories for each agent. However, most of
them suffer from restricted representation ability or unstable training issues.
To overcome these limitations, we propose utilizing the diffusion model to
generate the distribution of future trajectories. Two cruxes are to be settled
to realize such an idea. First, the diversity of intention is intertwined with
the uncertain surroundings, making the true distribution hard to parameterize.
Second, the diffusion process is time-consuming during the inference phase,
rendering it unrealistic to implement in a real-time driving system. We propose
an Intention-aware denoising Diffusion Model (IDM), which tackles the above two
problems. We decouple the original uncertainty into intention uncertainty and
action uncertainty and model them with two dependent diffusion processes. To
decrease the inference time, we reduce the variable dimensions in the
intention-aware diffusion process and restrict the initial distribution of the
action-aware diffusion process, which leads to fewer diffusion steps. To
validate our approach, we conduct experiments on the Stanford Drone Dataset
(SDD) and ETH/UCY dataset. Our methods achieve state-of-the-art results, with
an FDE of 13.83 pixels on the SDD dataset and 0.36 meters on the ETH/UCY
dataset. Compared with the original diffusion model, IDM reduces inference time
by two-thirds. Interestingly, our experiments further reveal that introducing
intention information is beneficial in modeling the diffusion process of fewer
steps.
