This live demonstration showcases the integration of Carfield, a heterogeneous automotive RISC-V SoC for mixed-criticality edge intelligence applications, with SHIELD, a non-intrusive, multimodal smart steering wheel. SHIELD enables robust, redundant acquisition of physiological signals to monitor the driver’s state continuously. During the demo, dry electrodes embedded within the steering wheel synchronously measure electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and body temperature from both hands. Raw signals are transmitted via the automotive CAN-FD protocol directly to the Carfield SoC, while simultaneously streaming to a PC GUI via Bluetooth Low Energy (BLE) or WiFi. The RISC-V core processes the incoming CAN-FD data stream in real time. It performs digital signal filtering and employs golden-standard algorithms, including the Pan-Tompkins algorithm for ECG and PPG peak detection, to analyze heart rate (HR) and heart rate variability (HRV) in both the time and frequency domains. In the live session,(see Figure 1), a team member will use the smart steering wheel during a dynamic driving simulation using BeamNG.tech. Attendees will observe the GUI updating in real time, displaying the physiological waveforms alongside the HR and HRV metrics computed by Carfield.
Overall, this demo illustrates how heterogeneous, open-source RISC-V architectures can efficiently handle vital sensor data acquisition and complex biosignal processing at the edge in a real-time automotive contest, paving the road for non-intrusive, real-time driver monitoring systems in next-generation vehicles.