Characterized Diffusion and Spatial-Temporal Interaction Network for Trajectory Prediction in Autonomous Driving

TL;DR

This paper introduces a novel trajectory prediction model for autonomous driving that combines a Characterized Diffusion Module with a Spatio-Temporal Interaction Module, achieving state-of-the-art results across multiple traffic datasets.

Contribution

The paper presents a new model integrating diffusion-based traffic scenario simulation with spatio-temporal vehicle interaction analysis for improved trajectory prediction accuracy.

Findings

Achieves SOTA results on NGSIM, HighD, and MoCAD datasets.

Effective in complex traffic scenarios including highways and urban streets.

Enhances prediction accuracy with detailed semantic and interaction modeling.

Abstract

Trajectory prediction is a cornerstone in autonomous driving (AD), playing a critical role in enabling vehicles to navigate safely and efficiently in dynamic environments. To address this task, this paper presents a novel trajectory prediction model tailored for accuracy in the face of heterogeneous and uncertain traffic scenarios. At the heart of this model lies the Characterized Diffusion Module, an innovative module designed to simulate traffic scenarios with inherent uncertainty. This module enriches the predictive process by infusing it with detailed semantic information, thereby enhancing trajectory prediction accuracy. Complementing this, our Spatio-Temporal (ST) Interaction Module captures the nuanced effects of traffic scenarios on vehicle dynamics across both spatial and temporal dimensions with remarkable effectiveness. Demonstrated through exhaustive evaluations, our model sets a new standard in trajectory prediction, achieving state-of-the-art (SOTA) results on the Next Generation Simulation (NGSIM), Highway Drone (HighD), and Macao Connected Autonomous Driving (MoCAD) datasets across both short and extended temporal spans. This performance underscores the model's unparalleled adaptability and efficacy in navigating complex traffic scenarios, including highways, urban streets, and intersections.