Characterized Diffusion Networks for Enhanced Autonomous Driving Trajectory Prediction

TL;DR

This paper introduces a novel trajectory prediction model for autonomous driving that combines a Characterized Diffusion Module with a Spatial-Temporal Interaction Network, improving accuracy and reliability in complex traffic scenarios.

Contribution

The paper presents a new model integrating diffusion and interaction networks, enhancing trajectory prediction in dynamic, heterogeneous traffic environments.

Findings

Outperforms existing state-of-the-art methods on public datasets

Effectively captures spatial-temporal traffic dynamics

Improves prediction accuracy in complex scenarios

Abstract

In this paper, we present a novel trajectory prediction model for autonomous driving, combining a Characterized Diffusion Module and a Spatial-Temporal Interaction Network to address the challenges posed by dynamic and heterogeneous traffic environments. Our model enhances the accuracy and reliability of trajectory predictions by incorporating uncertainty estimation and complex agent interactions. Through extensive experimentation on public datasets such as NGSIM, HighD, and MoCAD, our model significantly outperforms existing state-of-the-art methods. We demonstrate its ability to capture the underlying spatial-temporal dynamics of traffic scenarios and improve prediction precision, especially in complex environments. The proposed model showcases strong potential for application in real-world autonomous driving systems.