Innovative Cabin Concepts in Next-Gen Mobility

• Innovative cabin concepts are defined by the integration of modular design, quantified user acceptance, and adaptive systems for enhanced comfort and safety.
• Research employs user-centered design and rapid prototyping, using metrics like comfort scores and SINR to optimize layouts and communication infrastructures.
• Advanced systems leverage AI and reinforcement learning to adjust environmental controls in real time, ensuring personalized and responsive cabin experiences.

Innovative cabin concepts define a research frontier at the intersection of user-centered design, advanced material systems, optical wireless communication, adaptive environmental control, and AI-driven comfort and safety systems. Enabled by iterative methodologies and performance-driven evaluation, next-generation cabin environments—across aircraft, urban air mobility vehicles, and automotive interiors—are increasingly characterized by modularity, personalization, and quantified user acceptance. This article synthesizes key methodologies, system architectures, user studies, and technical performance indicators guiding state-of-the-art cabin innovation.

1. User-Centered Design and Iterative Prototyping

Applied design thinking frameworks are central to innovative cabin concepts, as demonstrated by the German Aerospace Center’s (DLR) Horizon UAM project on air-taxi interiors (Reimer et al., 2023). User-centered processes incorporate iterative stages adapted from the Stanford d.school’s model: Empathize, Define, Ideate, Prototype, and Test. Empirical user engagement (focus groups, online surveys, 3D prototyping) traces requirements and acceptance drivers, such as privacy, daylight exposure, and accommodating passenger with reduced mobility (PRM).

A typical workflow involves:

• Benchmarking: Comparative analysis with luxury and automotive interiors to set reference standards for aesthetics and functional ergonomics.
• Persona Construction and Constraint Definition: Translating anthropometric and operational constraints (e.g., 90 kg/passenger, 20 kg luggage, seating pitch 625–690 mm) into parametric floorplans and systemic boundaries.
• Concept Prioritization: Clustering ideation outputs into pillars—comfort & experience, safety & security, luggage & storage, seating & configuration—for rigorous downselection.
• Rapid Prototyping: Digital 3D design cycles yielding modular, reconfigurable, and lightweight components (e.g., camping-chair–inspired aluminum/natural-fiber seating, modular U-shaped headrests, snap-in partitions).
• Quantitative Testing: Online surveys (N=202), Likert ratings, and mixed-reality mock-ups provide direct user feedback on specific layout, privacy, and comfort schemes.

The integration of key user metrics into design scoring functions enables objective decision-making:

• Comfort score: $$S_{comfort} = 0.4 L_{legroom} + 0.3 L_{lighting} + 0.3 A_{armrests}$$
• Privacy score: $$S_{privacy} = w_p H_{headrest} + w_s D_{seat–seat} + w_m P_{partition}$$
• Luggage accessibility: $S_{luggage}=f(V_{stow},E_{access},S_{fix})$

These metrics, grounded in direct user measurement, drive iterative selection and guide layout, material, and interface choices.

2. Advanced Cabin Communication and Illumination Infrastructures

Modern cabin environments increasingly mandate seamless high-throughput data access while minimizing electromagnetic interference with onboard safety-critical avionics. Optical Wireless Communication (OWC) systems, particularly Visible-Light Communication (VLC), achieve this by leveraging the dual-functionality of cabin lighting for data transmission (Alsulami et al., 2019).

System architecture highlights:

• Transmitter Design: RYGB laser diode (LD) engines embedded in reading lights, forming Angle‐Diversity Transmitters (ADT) with branch count and semi-angle engineered for specific seat locations (Type-A: φ½=14°, Type-B: φ½=10°).
• Receiver Architectures: 4-branch Angle Diversity Receivers (ADR) offer robust multipath mitigation; 25-pixel Imaging Receivers (ImR) enable fine-grained spatial discrimination with per-pixel FOV ≈ 1.6°.
• Optical Channel Modeling: Monte Carlo ray-tracing quantifies impulse response, delay spread (στ), and Signal-to-Interference-plus-Noise Ratio (SINR), with imaging receivers achieving low delay spread (στ ≈ 0.1–0.3 ns) and consistent SINR (18–24 dB).
• Achievable Data Rates: Simple OOK modulation supports rates up to 22.8 Gbps/user with uniform performance across seat positions.
• Integration and Power: System reuses existing lighting infrastructure with marginal increase in per-user power (~8 W compared to 5 W for conventional lights), preserving illumination quality (CRI>90).

Table: Key OWC Cabin Parameters

Parameter	ADR	ImR
Delay Spread (σ_τ)	0.6–0.9 ns	0.1–0.3 ns
SINR (MRC)	14–20 dB	18–24 dB
Peak Data Rate (OOK)	15–18 Gbps/user	20–22.8 Gbps/user

Eye-safety, dynamic beam allocation, and hybrid IR/VLC for dimming resilience constitute active research areas.

3. Adaptive Environmental Control via AI and Reinforcement Learning

Cabin comfort is increasingly managed by intelligent, adaptive control systems that leverage user profiles, environmental sensing, and continuous feedback (Vashishtha et al., 2023). A representative architecture (Editor’s term: "QFIS," for Q-learning Fuzzy Inference System) blends expert-rule-based fuzzy inference with online reinforcement learning:

• Input Vector: Environmental (e.g., Daily Glare Index via photopic sensor), user (age, activity, chronotype), and real-time passenger feedback.
• Baseline FIS: 180-rule Takagi–Sugeno system with Gaussian membership functions for sensor and profile inputs, producing a defuzzified light setting.
• Q-Learning Adaptation: Passenger adjustments logged as rewards, which incrementally tune rule consequents and MF means for long-term personalization.
• Performance Metrics: Rapid convergence (e.g., 48–62 trials for ±2 unit stability, depending on learning rate α), minimal overshoot, and demonstrable adaptation in N=18 user studies.

Integration prospects include multi-modal comfort orchestration (HVAC, window tint, seat heating/cooling) and fleet-wide cross-context profile learning.

4. Artificial Intelligence Support for Safety, Comfort, and Human–Machine Interface

Comprehensive AI frameworks now underpin multiple cabin functionalities in both autonomous and conventional transport settings (Rong et al., 2021). These systems separate into safety and comfort application domains:

• Safety: Emotion, fatigue, distraction, and gaze monitoring (via CNNs, SVMs, fuzzy logic, and physiological sensing); intention analysis and take-over readiness leveraging time-series models (RNN/LSTM, HMM) on fused sensor streams; up to 92% accuracy in emotion and fatigue labeling, ~90% anticipation precision for maneuver intent.
• Comfort: Activity recognition (3D-CNN architectures on multi-modal sensor data), context-aware entertainment and climate control (NNs, RL), and robust gesture/speech-based HMI (RF sensors, RGB-D CNNs, LSTMs).
• Sensor Fusion and System Architecture: Standardized blocks combine video (RGB/NIR), physiological data, vehicle dynamics, radar/lidar, and audio.

System performance is validated against metrics such as classification accuracy, energy reduction, reaction time, and subjective user comfort scores.

5. Quantitative Evaluation of Cabin Layouts and Systems

Quantitative validation drives the evolution of innovative cabin concepts. For example, the DLR air-taxi interior achieved:

• Mass Savings: Target cabin mass ≤ 580 kg, digital design masses 380–579 kg, benchmarking against commercial-bus standards (~700 kg).
• Accessibility and Comfort: Floorplan 1.53 m × 0.565 m per row; headroom ≥ 1.60 m; PRM-compliant aisle width (≥450 mm); seat comfort and privacy scores parameterized from survey-derived weights.
• User Acceptance: Top-rated layouts reached 52.5% “good/very good” (traveling with companion), with user-preferred configurations selected based on statistical significance (χ², p<0.05).
• Lighting and HMI: Panoramic windows (window–area ratio ~0.22), tunable LED illumination, adaptive personal controls at each seat.

Similarly, OWC communication trials quantify delay spread, SINR, and data rates, ensuring both technical robustness and passenger quality of experience.

6. Emerging Trends, Integration Challenges, and Future Directions

Active research trajectories and open questions include:

• Unified AI Frameworks: Toward orchestrated, cross-functional controllers that mediate input sharing and concurrent decision-making across emotion, fatigue, and comfort monitoring modules.
• Personalization and Lifelong Learning: Adoption of GMM-based driver/passenger modeling, real-time adaptation, and federated architectures for privacy-compliant user profiling.
• Collaborative Agents and Explainability: Move towards conversational, negotiation-capable cabin agents integrating real-time sensor fusion, and transparent AR/voice feedback mechanisms.
• Validation and Certification: Need for real-environment studies beyond simulations, especially for regulatory approval of safety– and comfort–critical functionalities (e.g., mixed-reality egress testing, PRM drills).
• Resource Efficiency and Privacy: Edge-processing, lightweight models for SoCs, privacy-preserving sensor fusion, and minimal intrusion architectures.

The convergence of user-centric design, quantifiable performance metrics, adaptive AI control, advanced communication/illumination technologies, and flexible physical layouts typifies the current state and trajectory of innovative cabin concepts in next-generation mobility and aviation platforms (Reimer et al., 2023, Alsulami et al., 2019, Vashishtha et al., 2023, Rong et al., 2021).