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An Edge-Computing-Based Emotion-Aware Adaptive Lighting System for Intelligent Cockpits
Lei He1, Ning Jia1, Jiaqi Zhao1
1National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China.
Sensors (Basel, Switzerland)
|June 12, 2026
Summary
This study introduces an edge-computing system for emotion-aware ambient lighting in intelligent cockpits. The novel system enhances driver well-being by adapting lighting to perceived emotions, reducing anger recovery time by 42.6%.
Area of Science:
- Human-Computer Interaction
- Automotive Engineering
- Affective Computing
Background:
- Traditional driver monitoring systems struggle with limitations like rigidity and computational demands in evolving intelligent cockpits.
- Intelligent cockpits are transforming into personalized living spaces, necessitating more adaptive and robust driver monitoring solutions.
- Existing systems often lack the flexibility and efficiency required for real-time emotional adaptation within the vehicle environment.
Purpose of the Study:
- To propose and evaluate an edge-computing-based emotion-aware ambient lighting system for intelligent cockpits.
- To develop a lightweight, efficient emotion recognition network suitable for edge deployment.
- To create a non-intrusive, adaptive lighting feedback mechanism based on driver emotional states.
Main Methods:
- Designed a lightweight Mini_XCEPTION network optimized with depthwise separable convolutions and a Gaussian-smoothed weighted cross-entropy loss function for emotion recognition.
- Implemented INT8 quantization for real-time inference (47 FPS) on a Raspberry Pi.
- Developed a high-concurrency asynchronous software-hardware architecture using PyQt5 and QThread, featuring robust serial communication.
- Established an emotion-HSV lighting mapping matrix based on the Russell Valence-Arousal model with a bionic breathing rhythm for feedback.
- Implemented an FSM-controlled HSV lighting policy on an in-cabin Hardware-in-the-Loop (HIL) platform.
Main Results:
- The optimized emotion recognition network achieved real-time performance on edge hardware.
- The emotion-aware ambient lighting system demonstrated a 42.6% reduction in anger recovery time in a simulated road-rage test.
- The system successfully provided non-intrusive, adaptive lighting feedback based on driver emotions.
Conclusions:
- The proposed edge-computing-based emotion-aware ambient lighting system offers a robust and efficient solution for intelligent cockpits.
- The system effectively adapts to driver emotions, improving emotional recovery and potentially enhancing the driving experience.
- Further physiological validation is recommended to corroborate the findings on emotion recognition and intervention effectiveness.
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