Scene-Aware Explainable Multimodal Trajectory Prediction

TL;DR

This paper presents an explainable multimodal trajectory prediction model for automated vehicles that improves accuracy and interpretability by integrating a diffusion approach and feature importance assessment, aligning with human driving reasoning.

Contribution

The paper introduces a novel explainable diffusion-based trajectory prediction model that jointly reasons about scenario agents and enhances interpretability of predictions.

Findings

Outperforms baseline models in accuracy on Waymo dataset

Effectively identifies critical environmental factors influencing predictions

Aligns model explanations with human driving experience

Abstract

Advancements in intelligent technologies have significantly improved navigation in complex traffic environments by enhancing environment perception and trajectory prediction for automated vehicles. However, current research often overlooks the joint reasoning of scenario agents and lacks explainability in trajectory prediction models, limiting their practical use in real-world situations. To address this, we introduce the Explainable Conditional Diffusion-based Multimodal Trajectory Prediction (DMTP) model, which is designed to elucidate the environmental factors influencing predictions and reveal the underlying mechanisms. Our model integrates a modified conditional diffusion approach to capture multimodal trajectory patterns and employs a revised Shapley Value model to assess the significance of global and scenario-specific features. Experiments using the Waymo Open Motion Dataset demonstrate that our explainable model excels in identifying critical inputs and significantly outperforms baseline models in accuracy. Moreover, the factors identified align with the human driving experience, underscoring the model's effectiveness in learning accurate predictions. Code is available in our open-source repository: https://github.com/ocean-luna/Explainable-Prediction.