mlsanityData to 14 Oct 2026

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

arXiv 2406.00211 ↗First posted 31 May 2024cs.RO cs.SY eess.SYcited by 2

On the map: End-to-end autonomous driving → Imitation-learning end-to-end planners

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.

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.

Builds on

8 of 21 references.

References · 8

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2021Maximum Likelihood Training of Score-Based Diffusion Modelscited by 672
  3. 2022A Survey on Generative Diffusion Modelcited by 240
  4. 2022Guided Conditional Diffusion for Controllable Traffic Simulationcited by 190
  5. 2022Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objectivecited by 28
  6. 2020Active Inference and Behavior Trees for Reactive Action Planning and Execution in Roboticscited by 23
  7. 2020Deep Active Inference for Partially Observable MDPscited by 20
  8. 2019A Novel Adaptive Controller for Robot Manipulators based on Active Inferencecited by 12

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2025Deep Active Inference Agents for Delayed and Long-Horizon Environmentscited by 4
  2. 2024Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machinescited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties
  2. 2024Diffusion-Based Environment-Aware Trajectory Prediction
  3. 2024Towards Human-Like Driving: Active Inference in Autonomous Vehicle Control
  4. 2024Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting
  5. 2024Intention-aware Denoising Diffusion Model for Trajectory Prediction
  6. 2025Controllable Latent Diffusion for Traffic Simulation
  7. 2026Multimodal Action Diffusion for Robust End-to-End Autonomous Driving
  8. 2025Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning