DMC-Diffuser: Driving Mode Centric Denoising Diffusion Model for Trajectory Prediction

The prediction of future trajectories for vehicle in the field of autonomous driving is of utmost importance to ensure safe driving. However, it is challenging as the existing methods still suffer from noise sensitivity, limited representation ability and time-consuming inference. To this end, we propose a novel Driving Mode Centric Denoising Diffusion Model for trajectory prediction, namely DMC-Diffuser. Specifically, we first devise a driving mode token generator by utilizing different time-scale information from noisy trajectories. Subsequently, we employ an attention-based denoiser, which leverages the driving mode token along with the driving context to learn the potential trajectories. Furthermore, aided by a flexible denoiser structure with an accelerated sampling algorithm, the proposed method can achieve the comparable performance at the cost of low-complexity inference. Experimental results on two datasets show that DMC-Diffuser has achieved superior performance compared to the state-of-the-art methods, e.g., a comprehensive performance gain of nearly 9% over other diffusion based methods.