Diffusion-Based Environment-Aware Trajectory Prediction

TL;DR

This paper introduces a diffusion-based generative model for multi-agent trajectory prediction that effectively captures complex interactions and environmental factors, outperforming existing methods in accuracy and diversity on real-world traffic data.

Contribution

The paper presents a novel diffusion-based approach for environment-aware trajectory prediction, incorporating motion constraints and interaction guidance to enhance realism and adaptability.

Findings

Outperforms existing methods in prediction accuracy

Generates diverse, realistic trajectories

Effectively models complex multi-agent interactions

Abstract

The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model for multi-agent trajectory prediction is proposed. The model is capable of capturing the complex interactions between traffic participants and the environment, accurately learning the multimodal nature of the data. The effectiveness of the approach is assessed on large-scale datasets of real-world traffic scenarios, showing that our model outperforms several well-established methods in terms of prediction accuracy. By the incorporation of differential motion constraints on the model output, we illustrate that our model is capable of generating a diverse set of realistic future trajectories. Through the use of an interaction-aware guidance signal, we further demonstrate that the model can be adapted to predict the behavior of less cooperative agents, emphasizing its practical applicability under uncertain traffic conditions.