Design and Validation of Learning Aware HMI For Learning-Enabled Increasingly Autonomous Systems

• URL: http://arxiv.org/abs/2501.18506v1
• Date: Thu, 30 Jan 2025 17:15:39 GMT
• Title: Design and Validation of Learning Aware HMI For Learning-Enabled Increasingly Autonomous Systems
• Authors: Parth Ganeriwala, Michael Matessa, Siddhartha Bhattacharyya, Randolph M. Jones, Jennifer Davis, Parneet Kaur, Simone Fulvio Rollini, Natasha Neogi,
• Abstract summary: This research emphasizes the importance of incorporating human-machine collaboration into the systems engineering process. Our proposed LEIAS architecture emphasizes communication representation and pilot preference learning to boost operational safety.
• Score: 2.913852496198787
• License:
• Abstract: With the rapid advancements in Artificial Intelligence (AI), autonomous agents are increasingly expected to manage complex situations where learning-enabled algorithms are vital. However, the integration of these advanced algorithms poses significant challenges, especially concerning safety and reliability. This research emphasizes the importance of incorporating human-machine collaboration into the systems engineering process to design learning-enabled increasingly autonomous systems (LEIAS). Our proposed LEIAS architecture emphasizes communication representation and pilot preference learning to boost operational safety. Leveraging the Soar cognitive architecture, the system merges symbolic decision logic with numeric decision preferences enhanced through reinforcement learning. A core aspect of this approach is transparency; the LEIAS provides pilots with a comprehensive, interpretable view of the system's state, encompassing detailed evaluations of sensor reliability, including GPS, IMU, and LIDAR data. This multi-sensor assessment is critical for diagnosing discrepancies and maintaining trust. Additionally, the system learns and adapts to pilot preferences, enabling responsive, context-driven decision-making. Autonomy is incrementally escalated based on necessity, ensuring pilots retain control in standard scenarios and receive assistance only when required. Simulation studies conducted in Microsoft's XPlane simulation environment to validate this architecture's efficacy, showcasing its performance in managing sensor anomalies and enhancing human-machine collaboration, ultimately advancing safety in complex operational environments.

Related papers

• SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving [10.041702058108482]
  This study addresses the critical need for enhanced situational awareness in autonomous driving (AD) by leveraging the contextual reasoning capabilities of large language models (LLMs) Unlike traditional perception systems that rely on rigid, label-based annotations, it integrates real-time, multimodal sensor data into a unified, LLMs-readable knowledge base. Experimental results using real-world Vehicle-to-everything (V2X) datasets demonstrate significant improvements in perception and prediction performance.
  arXiv  Detail & Related papers  (2025-01-07T05:15:46Z)
• Leveraging Large Language Models for Enhancing Autonomous Vehicle Perception [0.0]
  Large Language Models (LLMs) are used to address challenges in dynamic environments, sensor fusion, and contextual reasoning. This paper presents a novel framework for incorporating LLMs into AV perception, enabling advanced contextual understanding. Experimental results demonstrate that LLMs significantly improve the accuracy and reliability of AV perception systems.
  arXiv  Detail & Related papers  (2024-12-28T17:58:44Z)
• Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey [14.73689900685646]
  Reinforcement Learning (RL) is a potent tool for sequential decision-making and has achieved performance surpassing human capabilities. As the extension of RL in the multi-agent system domain, multi-agent RL (MARL) not only need to learn the control policy but also requires consideration regarding interactions with all other agents in the environment. Simulators are crucial to obtain realistic data, which is the fundamentals of RL.
  arXiv  Detail & Related papers  (2024-08-19T03:31:20Z)
• Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
  This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems. LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
  arXiv  Detail & Related papers  (2023-11-28T03:13:09Z)
• Hierarchical Framework for Interpretable and Probabilistic Model-Based Safe Reinforcement Learning [1.3678669691302048]
  This paper proposes a novel approach for the use of deep reinforcement learning in safety-critical systems. It combines the advantages of probabilistic modeling and reinforcement learning with the added benefits of interpretability.
  arXiv  Detail & Related papers  (2023-10-28T20:30:57Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach [47.29474858956844]
  Wireless networked control system (WNCS) connecting sensors, controllers, and actuators via wireless communications is a key enabling technology for highly scalable and low-cost deployment of control systems in the Industry 4.0 era. Despite the tight interaction of control and communications in WNCSs, most existing works adopt separative design approaches. We propose a novel deep reinforcement learning (DRL)-based algorithm for controller and optimization utilizing both model-free and model-based data.
  arXiv  Detail & Related papers  (2022-10-03T01:29:40Z)
• Proceedings of the Robust Artificial Intelligence System Assurance (RAISA) Workshop 2022 [0.0]
  The RAISA workshop will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems. Rather than studying robustness with respect to particular ML algorithms, our approach will be to explore robustness assurance at the system architecture level.
  arXiv  Detail & Related papers  (2022-02-10T01:15:50Z)
• Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images [58.287120077778205]
  We introduce a framework for analyzing robustness of the learning algorithm w.r.t varying quality in the image input for autonomous driving. Using the results of sensitivity analysis, we propose an algorithm to improve the overall performance of the task of "learning to steer"
  arXiv  Detail & Related papers  (2021-02-26T02:08:07Z)
• Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems [71.14339738190202]
  democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper. The proposed algorithms demonstrate better results in the generalization performance of learning models in agents compared to the conventional FL algorithms.
  arXiv  Detail & Related papers  (2020-07-07T08:34:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.