Automotive Granular Synthesizer (AGS) for Electric Vehicles: Development and Affective Impact Analysis

With the rapid expansion of electric mobility, more Electric Vehicles are on the roads, bringing new challenges, notably the "quiet vehicle problem." EVs are significantly quieter than traditional Internal Combustion Engine Vehicles (ICEV), especially at low speeds, raising safety concerns for pedestrians and other road users who may not hear them approaching. This thesis, developed in collaboration with FIAMM Energy Technology and the CSC at the University of Padua, addresses this challenge by creating a Simulink model to generate customizable artificial engine sounds in real-time. The project employs granular synthesis, a technique not widely used in the automotive field. This method led to the development of the Automotive Granular Synthesizer (AGS), which allows users to input audio files and adjust parameters to create specific sounds that enhance pedestrian safety while aligning with vehicle brand identity. The thesis begins with an examination of the quiet vehicle problem and introduces the AGS as a solution. It explores the algorithm's principles, details the tunable parameters within the Graphical User Interface (GUI), and outlines the model's workflow. Finally, it presents an experimental study focusing on the emotional impact and detectability of the sounds generated by the AGS.