# Navigating Autonomous Vehicle on Unmarked Roads with Diffusion-Based Motion Prediction and Active Inference

arXiv: https://arxiv.org/abs/2406.00211 · HTML: https://mlsanity.com/paper/2406.00211/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 31 May 2024.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 8 of 8)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Maximum Likelihood Training of Score-Based Diffusion Models](https://mlsanity.com/paper/2101.09258/) (2021)
- [A Survey on Generative Diffusion Model](https://mlsanity.com/paper/2209.02646/) (2022)
- [Guided Conditional Diffusion for Controllable Traffic Simulation](https://mlsanity.com/paper/2210.17366/) (2022)
- [Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective](https://mlsanity.com/paper/2209.08466/) (2022)
- [Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics](https://mlsanity.com/paper/2011.09756/) (2020)
- [Deep Active Inference for Partially Observable MDPs](https://mlsanity.com/paper/2009.03622/) (2020)
- [A Novel Adaptive Controller for Robot Manipulators based on Active Inference](https://mlsanity.com/paper/1909.12768/) (2019)

## Built on by (most cited first, 2 of 2)

- [Deep Active Inference Agents for Delayed and Long-Horizon Environments](https://mlsanity.com/paper/2505.19867/) (2025)
- [Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machines](https://mlsanity.com/paper/2406.09322/) (2024)

## Abstract (arXiv)

This paper presents a novel approach to improving autonomous vehicle control in environments lacking clear road markings by integrating a diffusion-based motion predictor within an Active Inference Framework (AIF). Using a simulated parking lot environment as a parallel to unmarked roads, we develop and test our model to predict and guide vehicle movements effectively. The diffusion-based motion predictor forecasts vehicle actions by leveraging probabilistic dynamics, while AIF aids in decision-making under uncertainty. Unlike traditional methods such as Model Predictive Control (MPC) and Reinforcement Learning (RL), our approach reduces computational demands and requires less extensive training, enhancing navigation safety and efficiency. Our results demonstrate the model's capability to navigate complex scenarios, marking significant progress in autonomous driving technology.