An Edge-Computing-Based Emotion-Aware Adaptive Lighting System for Intelligent Cockpits

As intelligent cockpits transition into the "third living space", traditional driver monitoring systems face limitations such as rigid monitoring, computationally intensive algorithms, and insufficient engineering robustness. This paper proposes an edge-computing-based emotion-aware ambient lighting system, forming a complete loop of emotion perception-decision-adaptation. A lightweight emotion recognition network is designed for edge computing: the Mini_XCEPTION architecture is optimized with depthwise separable convolutions to reduce parameters, and a Gaussian-smoothed weighted cross-entropy loss function is used to address class imbalance and ambiguous emotion boundaries. After INT8 quantization, the model achieves 47 FPS real-time inference on a Raspberry Pi (Raspberry Pi Ltd., Cambridge, United Kingdom). A high-concurrency asynchronous software-hardware architecture based on PyQt5 5.15.6 and QThread5.15.6 is built, with a serial communication mechanism featuring fixed-length frames and fault recovery to improve the robustness of the hardware-in-the-loop system. Breaking the rigid alarm mode, an emotion-HSV lighting mapping matrix is established based on the Russell Valence-Arousal model, combined with 0.1 Hz bionic breathing rhythm for non-intrusive feedback. An FSM-controlled HSV lighting policy with 0.1 Hz breathing-light feedback was implemented on an in-cabin HIL platform. In a 12-participant simulated road-rage test, the intervention reduced FER-based anger recovery time by 42.6%; independent physiological validation remains necessary.