Occupant body size in vehicles varies significantly, encompassing differences in height, weight, and overall body composition. Adaptive restraint systems, featuring adjustable parameters such as belt load limiters, steering column load limiters and stroke, seat pan stiffness, and airbag pressure, can offer more equitable protection tailored to individual body sizes. In this study, a test rig modeled after the Volvo XC90 was used to collect data from 47 seated participants. Key seatbelt-related parameters, including D-ring angle, belt payout length, lap belt length, and buckle tension, were measured. These measurements were used to train machine learning models to predict occupant characteristics: height, weight, sitting height. The results demonstrate that low-cost sensors embedded in the seatbelt system can provide sufficiently accurate occupant body size estimations to inform adaptive restraint systems. The prediction errors across both training and test datasets were as follows: height 0.39 ± 6.96 cm, weight 1.72 ± 6.99 kg, and sitting height 0.02 ± 3.78 cm. A subsequent analysis revealed that buckle tension contributed minimally to prediction accuracy, whereas lap belt length emerged as a dominant feature across models.
Keywords: machine learning, occupant size estimation, adaptive restraint system, adaptive safety