Planning-Aware Diffusion Networks for Enhanced Motion Forecasting in Autonomous Driving

TL;DR

This paper introduces PIFM, a planning-aware diffusion network inspired by neural decision-making, which improves multi-agent trajectory prediction in autonomous driving by integrating contextual information and enhancing interpretability.

Contribution

The paper presents a novel diffusion-based framework that incorporates planning and contextual cues, offering improved accuracy and interpretability over existing models.

Findings

PIFM achieves higher prediction accuracy than baseline models.

The model demonstrates enhanced interpretability aligned with neuroscience principles.

Extensive experiments show low parameter count and robust performance.

Abstract

Autonomous driving technology has seen significant advancements, but existing models often fail to fully capture the complexity of multi-agent environments, where interactions between dynamic agents are critical. To address this, we propose the Planning-Integrated Forecasting Model (PIFM), a novel framework inspired by neural mechanisms governing decision-making and multi-agent coordination in the brain. PIFM leverages rich contextual information, integrating road structures, traffic rules, and the behavior of surrounding vehicles to improve both the accuracy and interpretability of predictions. By adopting a diffusion-based architecture, akin to neural diffusion processes involved in predicting and planning, PIFM is able to forecast future trajectories of all agents within a scenario. This architecture enhances model transparency, as it parallels the brain's method of dynamically adjusting predictions based on external stimuli and other agents'behaviors. Extensive experiments validate PIFM's capacity to provide interpretable, neuroscience-driven solutions for safer and more efficient autonomous driving systems, with an extremely low number of parameters.