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

TL;DR

This paper introduces a diffusion-based motion prediction integrated with an Active Inference Framework to improve autonomous vehicle navigation on unmarked roads, demonstrating enhanced safety and efficiency in simulated environments.

Contribution

The paper presents a novel combination of diffusion-based motion prediction with Active Inference, reducing computational load and training requirements compared to traditional methods.

Findings

Successfully navigates complex unmarked road scenarios in simulation.

Outperforms traditional MPC and RL methods in efficiency and safety.

Reduces computational demands and training data needs.

Abstract

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.