This paper is focused on the theoretical development and the hardware implementation of low-complexity piecewise-affine direct virtual sensors for the estima-tion of unmeasured variables of interest of nonlinear systems. The direct virtual sensor is designed directly from measured inputs and outputs of the system and does not require a dynamical model. The proposed approach allows one to design estimators which mitigate the effect of the so-called "curse of dimensionality" of simplicial piecewise-affine functions, and can be therefore applied to relatively high-order systems, enjoying convergence and optimality properties. An automatic toolchain is also presented to generate the VHDL code describing the digital cir-cuit implementing the virtual sensor, starting from the set of measured input and output data. The proposed methodology is applied to generate an FPGA imple-mentation of the virtual sensor for the estimation of vehicle lateral velocity, using a hardware-in-the-loop setting.