The present invention relates to automotive interface systems and methods. In one embodiment, an automotive interface system includes a steering wheel and an integrated interpolated variable impedance array that comprises a grid of sensing elements. The sensing elements are configured to power on simultaneously and to simultaneously generate multiple currents along multiple current paths in response to sensing a touch wherein the amount of current generated by a sensing element of the grid is directly proportional to the force applied by the touch. The automotive interface system also includes an analog-to-digital converter (ADC) and a processor communicatively coupled to the interpolated variable impedance array that are configured to receive the multiple currents along multiple current paths and determine a location, a duration, an area, and a force of the touch from the multiple currents along multiple current paths.

The present invention relates to automotive interface systems and methods. In one embodiment, an automotive interface system includes a steering wheel and an integrated interpolated variable impedance array that comprises a grid of sensing elements. The sensing elements are configured to power on simultaneously and to simultaneously generate multiple currents along multiple current paths in response to sensing a touch wherein the amount of current generated by a sensing element of the grid is directly proportional to the force applied by the touch. The automotive interface system also includes an analog-to-digital converter (ADC) and a processor communicatively coupled to the interpolated variable impedance array that are configured to receive the multiple currents along multiple current paths and determine a location, a duration, an area, and a force of the touch from the multiple currents along multiple current paths.

A latent transformer architecture with latent attention mechanisms and expert processing systems for federated deep learning. The latent transformer operates entirely within latent space, eliminating traditional embedding and positional encoding layers while maintaining full attention capabilities. Input data is compressed into latent vectors via variational autoencoder encoding, then processed by a latent attention module that computes query, key, and value matrices directly from latent representations. The architecture incorporates expert processing systems including gated latent expert networks for sparse computation and latent mixture of experts for collaborative processing. In the gated approach, a routing network selectively activates specialized expert modules based on latent vector characteristics. The mixture approach enables all experts to contribute through weighted combination, facilitating distributed computation and enhanced model expressiveness.