Simone Santia
Driving Style Estimation for Impaired Driving Detection: An AI-based solution using Reinforcement Learning.
Rel. Angelo Bonfitto, Ilario Gerlero, Nicolo' Chiapello. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2022
Abstract
Safety occupies a major spot when designing a new vehicle today. Carmakers give major importance to this aspect to make mobility and roads safer for vehicle passengers/occupants and pedestrians. The improvement of mobility safety is achieved through the optimisation of structural components, active systems, and the monitoring of driving conditions and driving style. The driving style is defined in terms of how the driver acts on the vehicle controls in response to external situations and stimuli. By characterizing this drive style, it is possible to identify psycho-somatic alterations, by identifying functional drifts with respect to the nominal driving modes. This work focuses on the latter and aims to develop an artificial intelligence-based system capable of detecting the behavioural patterns of a driver.
Many Driver State Monitoring (DSM) methods are already present in vehicles, but they typically implement a camera-based solution that tracks eye movements or blink rate
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