Francesca Mangone
Autonomous Vehicle Integration: Pedestrian Behaviour Prediction.
Rel. Andrea Bottino. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024
Abstract
In the realm of automotive safety and innovation, Advanced Driver Assistance Systems (ADAS) play a pivotal role in enhancing vehicle operation and road safety. This thesis explores the development and evaluation of ADAS, with a specific emphasis on Automatic Emergency Braking (AEB) systems. The thesis delves into the challenges faced by autonomous vehicles in navigating complex urban environments designed for human drivers. Unlike humans, autonomous vehicles perceive the environment differently and rely on various sensors for perception, localization, prediction, planning, and control. Each aspect is crucial for safe and efficient autonomous driving. The focus of this work is on perfecting the Prediction task, by tackling questions related to predicting human behavior, distinguishing between normal and abnormal situations, and ethical decision-making in emergency scenarios, the research aims to enhance the predictive capabilities of autonomous vehicles.
The thesis proposes a strategic foundational approach, initially focusing on a 2D environment for analyzing and understanding critical variables in pedestrian movement
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