On the Development of an Acoustic-Driven Method to Improve Driver's Comfort Based on Deep Reinforcement Learning

The safety and comfort of drivers have been improved over the decades as a result of our broadened understanding of driver modeling and behavior prediction. Despite these remarkable advances in autonomous and interactive systems, there is a significant lack of approaches that consider the passengers and the vehicle as components of a dynamical vibro-acoustical system. Sound in vehicles is not only informative of the state of the vehicle and the environment, but can also critically affect the driver's performance, attention, and comfort. This paper aims to investigate the interplay between the perceived sounds of a vehicle and psychoacoustic annoyance (PA) metrics. Our goal is to create an intelligent agent that would act to improve driving pleasantness through acoustic-driven learning. To tackle the problem of choosing the correct actions to reduce the acoustic annoyance, the paper presents a method based on reinforcement learning that learns from the environment, i.e., the vehicle interior. The method actively changes the state inside the vehicle (e.g., closing or opening the window and choosing the cruise speed) in order to minimize acoustic annoyance experienced by the driver. The results of this work, performed using the GTA V simulator, showed that the trained agent successfully learned to take the correct actions to reduce PA metrics. The paper also present to the community a new multi-modal dataset composed of several rides on a real vehicle and an in-depth analysis of the influence of vehicle's signal on the acoustic annoyance.