Private spaces like apartments and vehicles are not yet fully exploited for health monitoring, which includes continuous measurement of biosignals. This work proposes sensor fusion for robust heartbeat detection in the noisy and dynamic driving environment. We use four sensors: electrocardiography (ECG), ballistocardiography (BCG), photoplethysmography (PPG), and image-based PPG (iPPG). As ground truth, we record a 3-lead ECG with wet electrodes attached to the chest. Twelve healthy volunteers are monitored in rest and during driving, each for 11 min. We propose sensor fusion using convolutional neural networks to detect the sensor combination delivering the most accurate heart rate measurement. For rest, we achieve scores of 95.16% (BCG + iPPG), 96.08% (ECG + iPPG), 96.35% (ECG + BCG), 96.53% (ECG + PPG), 96.58% (PPG + iPPG), and 97.15% (BCG + PPG). In motion, the highest scores are 92.46% (BCG + iPPG, PPG + iPPG, ECG + iPPG), 92.83% (ECG + PPG), 93.03% (BCG + PPG), and 93.08% (ECG + BCG). Fusing all four signals with the best fusion approach results in scores of 97.24% (rest) and 94.38% (motion). We conclude that sensor fusion allows robust heartbeat measurement of car drivers to support continuous and unobtrusive health monitoring for early disease detection.