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 MiniXCEPTION 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.
He et al. (2026) studied this question.