Perceptual Motor Learning with Active Inference Framework for Robust Lateral Control

This paper presents a novel Perceptual Motor Learning (PML) framework integrated with Active Inference (AIF) to enhance lateral control in Highly Automated Vehicles (HAVs). PML, inspired by human motor learning, emphasizes the seamless integration of perception and action, enabling efficient decision-making in dynamic environments. Traditional autonomous driving approaches--including modular pipelines, imitation learning, and reinforcement learning--struggle with adaptability, generalization, and computational efficiency. In contrast, PML with AIF leverages a generative model to minimize prediction error ("surprise") and actively shape vehicle control based on learned perceptual-motor representations. Our approach unifies deep learning with active inference principles, allowing HAVs to perform lane-keeping maneuvers with minimal data and without extensive retraining across different environments. Extensive experiments in the CARLA simulator demonstrate that PML with AIF enhances adaptability without increasing computational overhead while achieving performance comparable to conventional methods. These findings highlight the potential of PML-driven active inference as a robust alternative for real-world autonomous driving applications.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning

The occurrences of cyber attacks on the power grids have been increasing every year, with novel attack techniques emerging every year. In this paper, we address the critical challenge of detecting novel/zero-day attacks in digital substations that employ the IEC-61850 communication protocol. While many heuristic and machine learning (ML)-based methods have been proposed for attack detection in IEC-61850 digital substations, generalization to novel or zero-day attacks remains challenging. We propose an approach that leverages the in-context learning (ICL) capability of the transformer architecture, the fundamental building block of large language models. The ICL approach enables the model to detect zero-day attacks and learn from a few examples of that attack without explicit retraining. Our experiments on the IEC-61850 dataset demonstrate that the proposed method achieves more than $85\%$ detection accuracy on zero-day attacks while the existing state-of-the-art baselines fail. This work paves the way for building more secure and resilient digital substations of the future.

Leveraging Conversational Generative AI for Anomaly Detection in Digital Substations

This study addresses critical challenges of cybersecurity in digital substations by proposing an innovative task-oriented dialogue (ToD) system for anomaly detection (AD) in multicast messages, specifically, generic object oriented substation event (GOOSE) and sampled value (SV) datasets. Leveraging generative artificial intelligence (GenAI) technology, the proposed framework demonstrates superior error reduction, scalability, and adaptability compared with traditional human-in-the-loop (HITL) processes. Notably, this methodology offers significant advantages over machine learning (ML) techniques in terms of efficiency and implementation speed when confronting novel and/or unknown cyber threats, while also maintaining model complexity and precision. The research employs advanced performance metrics to conduct a comparative assessment between the proposed AD and HITL-based AD frameworks, utilizing a hardware-in-the-loop (HIL) testbed for generating and extracting features of IEC61850 communication messages. This approach presents a promising solution for enhancing the reliability of power system operations in the face of evolving cybersecurity challenges.

Towards Human-Like Driving: Active Inference in Autonomous Vehicle Control

This paper presents a novel approach to Autonomous Vehicle (AV) control through the application of active inference, a theory derived from neuroscience that conceptualizes the brain as a predictive machine. Traditional autonomous driving systems rely heavily on Modular Pipelines, Imitation Learning, or Reinforcement Learning, each with inherent limitations in adaptability, generalization, and computational efficiency. Active inference addresses these challenges by minimizing prediction error (termed "surprise") through a dynamic model that balances perception and action. Our method integrates active inference with deep learning to manage lateral control in AVs, enabling them to perform lane following maneuvers within a simulated urban environment. We demonstrate that our model, despite its simplicity, effectively learns and generalizes from limited data without extensive retraining, significantly reducing computational demands. The proposed approach not only enhances the adaptability and performance of AVs in dynamic scenarios but also aligns closely with human-like driving behavior, leveraging a generative model to predict and adapt to environmental changes. Results from extensive experiments in the CARLA simulator show promising outcomes, outperforming traditional methods in terms of adaptability and efficiency, thereby advancing the potential of active inference in real-world autonomous driving applications.