InsightPilot: Adaptive Pilot Systems

• InsightPilot is a framework that integrates multimodal sensing, adaptive automation, and explainable AI to enhance pilot decision-making and safety.
• It fuses data from physiological sensors, behavioral indicators, and environmental context to achieve real-time workload estimation and interface adaptation.
• Empirical results demonstrate that adaptive autonomy and explainable interfaces in InsightPilot systems significantly improve operational performance and reduce pilot workload.

InsightPilot refers to a class of systems and research methodologies that integrate real-time human state estimation, human-machine interaction, adaptive automation, and explainable intelligence to enhance the operational effectiveness, safety, and situational awareness of pilots and operators. Across multiple domains, the term “InsightPilot” (and related systems explicitly described as such (Ma et al., 2023), or in closely-related concepts involving workload estimation, intent modeling, feedback adaptation, and autonomy hand-off) encapsulates recent advances in leveraging psycho-physiological sensing, AI-driven analytics, user modeling, and human-centered interface design. These systems aim to turn latent human insight into actionable input for guidance, control, safety, and decision support.

1. Foundations: Sensing, Modeling, and Data Fusion

InsightPilot systems are anchored in the integration of multimodal data streams—such as physiological signals (e.g., heart rate, galvanic skin response, fNIRS, and pupil diameter), behavioral indicators (e.g., eye gaze, grip force, body pose), and operational context (e.g., flight state, semantic scene segmentation)—to estimate pilot state and workload (Park et al., 10 Jun 2024, Duval et al., 2022). The core methodology involves:

• Capturing high-fidelity psycho-physiological data using wearable devices and cockpit-integrated sensors: Empatica E4 (for BVP, HR, temperature, movement), Shimmer GSR+, BIOPAC fNIRS, Tobii eye tracking glasses, joystick force resistors, and Kinect V2 (for body pose) (Park et al., 10 Jun 2024).
• Synchronizing behavioral and situational data: flight control inputs and aircraft state (with middleware such as XPlaneROS), semantic context (object classes in field of view via OneFormer), and environmental variables (scene-encoded wind data in UAV operations) (Tabassum et al., 2023, Park et al., 10 Jun 2024).
• Aggregating raw sensor input into feature-rich representations through statistical summarization, normalization, and dimensionality reduction (PCA), followed by categorization into physiological, behavioral, situational, and flight-derivative domains (Park et al., 10 Jun 2024).
• Fusing data for real-time analysis: using frameworks such as ROS (with ROS4HRI topics for gaze/physiology), transfer-learned deep models (YOLO for object detection), and dynamic ROI tracking to relate gaze and environmental cues (Duval et al., 2022).

The result is a continuous, fine-grained view of operator state and intent that feeds higher-level analytics for adaptive support and interface configuration.

2. Machine Learning and Human State Estimation

A distinguishing feature of modern InsightPilot architectures is the use of supervised and semi-supervised learning to map multimodal features to task-relevant latent constructs such as workload, attention, or stress (Park et al., 10 Jun 2024, Duval et al., 2022). Notable algorithmic components include:

• Multi-class workload estimation using models such as LDA, SVM, Random Forest, and XGBoost; trained on aggregated physiological and behavioral features to predict categorical or continuous workload levels (Park et al., 10 Jun 2024).
• Individualized modeling: enhancing model performance by upsampling each pilot’s own data within the training set, achieving improvements from 51% to 63% accuracy in multiclass workload prediction (Park et al., 10 Jun 2024).
• Handling missing modalities robustly with KNN imputation.
• Gaze semantics weighting: dynamic computation of annotation weights for gaze-derived objects, $$W_{\text{annotation}} = \exp(\text{priority}^3) - 1$$, exponentially prioritizing focus on critical cockpit or environmental elements (Park et al., 10 Jun 2024).
• Integration of HRV and pupil diameter time-series (sliding window/LF filtering, NeuroKit2) for stress and cognitive load detection, supported by real-time feedback loops (Duval et al., 2022).
• In pioneering systems, observer-based inverse reinforcement learning (IRL) is applied to recover pilot cost functionals from trajectory data, even amidst solution nonuniqueness, offering a rigorous means to infer decision strategies and preference structure for explicit assistive control (Town et al., 2023).

These approaches allow systems to not only monitor but also interpret operator state in context, enabling explicit workload guidance, training adaptation, and anomaly detection.

3. Adaptive Interfaces and Feedback Mechanisms

InsightPilot frameworks emphasize multi-modal interface adaptation and actionable feedback to reduce workload and enhance operator comprehension:

• Augmented reality (AR) overlays, head-mounted displays, and dynamic visualization tools (e.g., Meta Quest 3 in FlightAR, immersive 225° FOV X-Plane simulation) afford pilots real-time overlays of operational, safety, and environmental data (Sautenkov et al., 22 Oct 2024, Park et al., 10 Jun 2024).
• Intuitive status cues: Onboard LED feedback (red/green for safe/unsafe), AR visualizations of UAV pose and orientation, and real-time wind widgets (compass arrows, color gradients) support rapid comprehension and situational awareness (Backman et al., 2023, Tabassum et al., 2023).
• Modular and user-preference driven interfaces: users can select among display modalities, and interface elements dynamically adapt to scenario complexity and user workload (Tabassum et al., 2023).
• User experience studies report significant reductions in physical and mental workload (e.g., NASA-TLX $\mu_{\text{physical}}=1.8$, $SD=0.8$; mental demand reduction of ~16% with AR overlays compared to traditional FPV (Sautenkov et al., 22 Oct 2024)). Map touch input methods further minimize cognitive load (NASA-TLX $2.80 \pm 0.48$) and maximize usability (SUS $91.25 \pm 10.31$) (Mean et al., 3 May 2025).
• Transparent communication: Automated and pilot-driven alerting policies are reinforced using learning-aware HMI (as in LEIAS), with severity indicator bars, RL-based tuning of alert thresholds, and explicit red/green markers for pilot feedback (Ganeriwala et al., 30 Jan 2025).

These approaches foster trust, ensure both comprehension and control, and proactively manage threshold crossings in safety-critical situations.

4. Shared Autonomy, Pilot Preference Learning, and Cooperative Decision Making

At the core of InsightPilot is adaptive autonomy, characterized by seamless handoff and blending between manual control and learned assistance:

• Shared autonomy architectures blend control signals from deep RL-trained policies with real pilot input (averaged or weighted per real-time estimates of confidence or workload), allowing gradual and context-dependent intervention (Backman et al., 2023, Yin et al., 2022).
• Policy modules operate under partially observable MDP formulations, inferring pilot intent and adapting blended command signals accordingly; reward functions balance task success, cooperation, and feedback acknowledgment (Backman et al., 2023).
• Reinforcement learning for pilot preference: Systems such as LEIAS employ Soar-based agents that continuously update alerting policy based on explicit Agree/Disagree/No-response pilot feedback, using RL scores as HMI indicators (Ganeriwala et al., 30 Jan 2025).
• Visual attention alignment: Some models compute human and agent “attention profiles” (via eye tracking and network saliency maps), using their (mis)alignment as a switching rule for autonomy handover, with quantified trade-off weights $(Q_h, Q_G)$ for pilot and guardian intervention (Yin et al., 2022).

Simulations and field studies verify that this incremental, cooperative paradigm boosts safety, reduces error rates, and elevates novice performance to near-expert levels (e.g., inspection and landing success rates from $16.67\%$ and $54.29\%$ to $95.59\%$ and $96.22\%$ with shared autonomy) (Backman et al., 2023).

5. Explainability, Visualization, and Data-Driven Insights

A haLLMark of the InsightPilot concept is the conversion of complex system states and operator behaviors into explainable, actionable knowledge:

• LLM-powered data exploration frameworks (explicitly exemplified by “InsightPilot” (Ma et al., 2023)) decompose user intent into a programmatic sequence of “analysis actions” (understand, summarize, compare, explain). These actions operate on formal analysis entities ($AE \equiv \langle\text{agg}(M),S,B\rangle$), guiding an iterative insight sequence for interpretive reporting.
• Insight ranking combines entailment reduction, semantic similarity via cosine vector embedding, and second-order approximation for top-$K$ diverse insights, ensuring comprehensiveness and lack of redundancy (Ma et al., 2023).
• Visualization toolsets augment the entire adaptive control and monitoring chain—displaying uncertainty, bounding confidence ellipses, overlaying suggested paths vs. actual tracks, and integrating alerting and decision-support overlays (Holmberg, 20 Sep 2024, Ganeriwala et al., 30 Jan 2025).
• Systems supporting cooperative flight and data operations leverage explainable action representations, high-level abstractions of sensor reliability, and inspection modules for both human and agent activities, further improving user trust and actionable understanding (Ma et al., 2023, Ganeriwala et al., 30 Jan 2025).

These approaches anchor situational awareness, foster user engagement with automation, and support both safety and efficiency.

6. Empirical Validation and Implications for Operational Safety

Across studies, empirical results support the efficacy of InsightPilot systems in improving operator performance, safety, and user experience:

• Objective metrics: Substantial increases in task success and decreases in error rates for UAV inspection/landing, improved real-time decision accuracy with adaptive overlays, and workload reduction across challenging environmental and operational conditions (Backman et al., 2023, Sautenkov et al., 22 Oct 2024, Tabassum et al., 2023).
• Subjective evaluations: High user satisfaction scores for stimulation and usability, improved trust with transparent communication, and a marked preference for hybrid, feedback-rich, or AR-based modalities over both classical manual and basic digital approaches (Sautenkov et al., 22 Oct 2024, Mean et al., 3 May 2025).
• Quantitative assessments from task performance, usability scales (SUS), and workload assessments (NASA-TLX, SWAT) reinforce the general finding that insight-enhanced, user-aware systems outperform both manual and naively-automated alternatives, especially when operator trust and error recovery are explicit design goals (Tabassum et al., 2023, Mean et al., 3 May 2025).
• A plausible implication is that hybrid systems—integrating interpretable automation, explainable intent modeling, adaptive feedback, and user-controlled modality selection—outperform both “purely manual” and “rigidly automated” approaches for high-stakes, uncertainty-rich pilot operations.

7. Future Directions and Research Challenges

Areas for future work and open challenges highlighted by the literature include:

• Expanding sensor and modeling coverage: Integration with EEG, broader semantic segmentation, and time-series transformer models for improved real-time prediction (Park et al., 10 Jun 2024, Duval et al., 2022).
• Personalization and continual learning: Dynamic adaptation to individual operator strategies and learning curves, exploiting fine-tuned (and potentially federated) models for adaptive assistance (Park et al., 10 Jun 2024, Ganeriwala et al., 30 Jan 2025).
• Robustness and reliability: Self-repair, retrieval-augmented generation, and ranking for explainable outputs in LLM-driven analysis and visualization; formal safety contracts for autonomous handoff and failure mitigation (Ganeriwala et al., 30 Jan 2025, Ma et al., 2023).
• User interface design: Rich multimodal interactions (speech, map touch, keyboard) with real-time error correction and confidence visualization; further reducing the “gulf of execution/evaluation” (Mean et al., 3 May 2025, Tabassum et al., 2023).
• Evaluation and benchmarking: Development of task-specific, multi-modal benchmarks covering the end-to-end operator-autonomy workflow, including behavioral, technical, and trust metrics (Inala et al., 27 Sep 2024).

By facilitating the conversion of real-time operator state and system analytics into actionable, explainable, and context-aware support, InsightPilot systems offer a rigorous, empirically-validated pathway to improved safety, efficiency, and trust in increasingly autonomous operational domains.