## 2 Background and Motivation  
As Advanced Driver Assistant Systems (ADAS) are increasingly adopted by both car manufacturers and regulators, there is a growing need to monitor the physiological states of drivers/occupants to assist in the engagement of the ADAS system [1; 2; 3; 4; 5]. For example, the EuroNCAP regulation requires the driver/occupants monitoring system (DMS/OMS) to detect sudden sickness as well the presence of a younger child in the vehicle [6]. Both HR and RR are shown to be useful indicators to predict the awareness level of the driver, which could, in turn, be used to determine if ADAS functions such as automatic lane keeping or front collision avoidance should be activated to ensure the safety of the driver [3; 4; 5; 7]. Additionally, with emerging autonomous services such as robotic taxis gaining traction, an unobtrusive CVSM system can be a valuable addition to ensure the well-being of the passengers [3]. Several sensors, such as cameras, radars, electrocardiograms (ECG), and ballistocardiograph sensors, have been studied to extract HR and RR from the driver [4; 5; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Table 1 summarizes the characteristics of various sensors commonly used for HR and RR monitoring of the driver/occupants in the vehicle. Among various sensors, the electrocardiogram measures HR from the electrical potential of the heart, while the ballistocardiograph sensor measures HR through the micro-motion of the body caused by the heartbeat. In comparison, both radars and cameras promise unobtrusive and contactless monitoring of HR and RR. Between these two non-contact sensors, cameras hold an advantage by offering other unique functions such as facial recognition and gaze detection that cannot be achieved with radars. With a conventional 2D camera, HR is usually acquired through imaging photoplethysmography (PPG), where the volumetric change of blood flow from the heartbeat causes different amounts of light to be absorbed by the user's face [17; 18; 19; 20; 21; 22]. Several past studies (such as chrominance (CHROM) or independent component analyses (ICA) based method) have shown the robustness of using a camera to contactlessly measure HR in controlled environments [23; 24; 25; 26; 27] by utilizing the RGB color channels in an RGB camera. Compared to measuring HR and RR in a controlled indoor environment, additional challenges from the varying illumination as well as the motion artifacts need to be addressed when measuring HR and RR while driving. In a moving vehicle, the illumination level can vary significantly based on factors such as weather or time of day. Any imaging-based sensor will need to be unaffected by the ambient light. Secondly, during driving, both the passive motions from the road or active motions from the users can interfere with the measurement result as well. Thus, for a camera-based sensor, a method for rejecting motion-induced artifacts is important to ensure reliable HR/RR measurement performance. Recently, several studies have discussed the use of a camera to measure HR in a vehicle [11; 12; 13; 14]. For example, [PERSON] et al. demonstrated HR monitoring in a driving vehicle with a RGB camera [12]. [PERSON] et al. demonstrated an in-vehicle HR monitoring system (Autosparse PPG) using an NIR camera with LED illuminations, where a sparse frequency estimation method is introduced to improve the HR measurement reliability against external artifacts [11]. However, for the Autosparse PPG method to be effective, uniform illumination of the user is required, which may not be satisfied when a compact sensor with a localized/point illumination source (such as Vertical Cavity Surface Emitting Lasers (VCSEL)) is used. In our previous study [28], we demonstrate a system using an indirect NIR ToF camera to monitor HR and RR in an indoor environment. We expand upon the previous study and demonstrate a CVSM system for HR and RR measurements in a moving vehicle.