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

arXiv: https://arxiv.org/abs/2410.19639 · HTML: https://mlsanity.com/paper/2410.19639/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 23 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://mlsanity.com/paper/2207.05844/) (2022)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [Multimodal Motion Prediction with Stacked Transformers](https://mlsanity.com/paper/2103.11624/) (2021)
- [PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings](https://mlsanity.com/paper/1905.01296/) (2019)
- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)
- [Auxiliary Tasks in Multi-task Learning](https://mlsanity.com/paper/1805.06334/) (2018)
- [PiP: Planning-informed Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2003.11476/) (2020)
- [Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving](https://mlsanity.com/paper/2109.06446/) (2021)
- [Learning to Predict Vehicle Trajectories with Model-based Planning](https://mlsanity.com/paper/2103.04027/) (2021)
- [Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss](https://mlsanity.com/paper/2011.06679/) (2020)
- [Tree-structured Policy Planning with Learned Behavior Models](https://mlsanity.com/paper/2301.11902/) (2023)
- [LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction](https://mlsanity.com/paper/2203.01880/) (2022)
- [Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction](https://mlsanity.com/paper/2302.10873/) (2023)
- [Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles](https://mlsanity.com/paper/2206.08809/) (2022)

## Abstract (arXiv)

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.