Search Results

Automotive seats are commonly described by one-dimensional measurements, including those documented in SAE J2732. However, 1-D measurements provide minimal information on seat shape. The goal of this work was to develop a statistical framework to analyze and model the surface shapes of seats by using techniques similar to those that have been used for modeling human body shapes. The 3-D contour of twelve driver seats of a pickup truck and sedans were scanned and aligned, and 408 landmarks were identified using a semi-automatic process. A template mesh of 18,306 vertices was morphed to match the scan at the landmark positions, and the remaining nodes were automatically adjusted to match the scanned surface. A principal component (PC) analysis was performed on the resulting homologous meshes. Each seat was uniquely represented by a set of PC scores; 10 PC scores explained 95% of the total variance. This new shape description has many applications.

Hand reach movements for manual work, vehicle operation, and manipulation of controls are planned and guided by visual images actively captured through eye and head movements. It is hypothesized that reach movements are based on the coordination of multiple subsystems that pursue the individual goals of visual gaze and manual reach. In the present study, shared control coordination was simulated in reach movements modeled using differential inverse kinematics. An 8-DOF model represented the torso-neck-head link (visual subsystem), and a 9-DOF model represented the torso-upper limb link (manual subsystem), respectively. Joint angles were predicted in the velocity domain via a pseudo-inverse Jacobian that weighted each link for its contribution to the movement. A secondary objective function was introduced to enable both subsystems to achieve the corresponding movement goals in a coordinated manner by manipulating redundant degrees of freedom.