One-size-fits-all car design flawed: study shows posture prediction needs customization
Researchers compared two digital modeling methods for predicting how drivers position themselves in vehicles and found neither works equally well across all car designs. The findings suggest automakers need to tailor their occupant packaging approach by vehicle type, potentially affecting product development timelines and design efficiency.
Originaltitel: A case study of digital human modelling assisted occupant packaging design: comparing driving posture and position prediction methods
<p>Accurately predicting driving postures and positions is crucial for occupant packaging design to accommodate diverse drivers. However, this is challenging due to individual variability and limited access to user data. Digital human modelling (DHM) tools enable posture prediction in virtual environments. This paper presents a case study comparing two driving prediction methods: a statistical prediction method (SPM) and an optimisation prediction method (OPM). Both were evaluated using data from two car models with different seat heights, involving 199 participants whose seat, eye-point, and steering wheel positions were measured. Results showed SPM was more accurate for vertical positioning, whereas OPM for fore-aft positioning. The effectiveness of each method varied by car model, with SPM aligning better in the higher-seated vehicle and OPM performing better in the lower-seated vehicle. These findings highlight the practical, context-specific performance of posture prediction methods. Methodological insights guide the improvement of DHM tool use in occupant packaging.</p>