FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction

TL;DR

FollowGen introduces a novel diffusion model with transformer-based attention to improve the accuracy and realism of vehicle trajectory predictions by capturing detailed inter-vehicle interactions and car-following behaviors.

Contribution

The paper presents a scaled noise conditional diffusion model that integrates inter-vehicle interactions into trajectory prediction, advancing beyond existing deep learning approaches.

Findings

Achieves state-of-the-art prediction accuracy on real-world datasets.

Effectively models complex inter-vehicle dependencies.

Demonstrates robustness across diverse driving scenarios.

Abstract

Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS). Although deep learning-based approaches - especially those utilizing transformer-based and generative models - have markedly improved prediction accuracy by capturing complex, non-linear patterns in vehicle dynamics and traffic interactions, they frequently overlook detailed car-following behaviors and the inter-vehicle interactions critical for real-world driving applications, particularly in fully autonomous or mixed traffic scenarios. To address the issue, this study introduces a scaled noise conditional diffusion model for car-following trajectory prediction, which integrates detailed inter-vehicular interactions and car-following dynamics into a generative framework, improving both the accuracy and plausibility of predicted trajectories. The model utilizes a novel pipeline to capture historical vehicle dynamics by scaling noise with encoded historical features within the diffusion process. Particularly, it employs a cross-attention-based transformer architecture to model intricate inter-vehicle dependencies, effectively guiding the denoising process and enhancing prediction accuracy. Experimental results on diverse real-world driving scenarios demonstrate the state-of-the-art performance and robustness of the proposed method.