WcDT: World-centric Diffusion Transformer for Traffic Scene Generation
Chen Yang Aaron Xuxiang Tian Dong Chen Tianyu Shi Arsalan Heydarian
In this paper, we introduce a novel approach for autonomous driving
trajectory generation by harnessing the complementary strengths of diffusion
probabilistic models (a.k.a., diffusion models) and transformers. Our proposed
framework, termed the "World-Centric Diffusion Transformer" (WcDT), optimizes
the entire trajectory generation process, from feature extraction to model
inference. To enhance the scene diversity and stochasticity, the historical
trajectory data is first preprocessed and encoded into latent space using
Denoising Diffusion Probabilistic Models (DDPM) enhanced with Diffusion with
Transformer (DiT) blocks. Then, the latent features, historical trajectories,
HD map features, and historical traffic signal information are fused with
various transformer-based encoders. The encoded traffic scenes are then decoded
by a trajectory decoder to generate multimodal future trajectories.
Comprehensive experimental results show that the proposed approach exhibits
superior performance in generating both realistic and diverse trajectories,
showing its potential for integration into automatic driving simulation
systems.