Elena Rita Trovato
Pedestrians trajectory forecasting for Automated Driving and Driving Assistance systems.
Rel. Fabrizio Lamberti, Lia Morra. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2020
Abstract
A key functionality of Automated driving and Driver assistance systems is the ability to detect obstacles in order to constantly adapt the vehicle trajectory to a changing environment. To guarantee a safe path planning has always been the main challenge of all the self-driving car projects, in which artificial intelligence software represent the core part. The most important type of road obstacle are of course pedestrians, which present a particular walking behavior that take into consideration many factors from neighbors' path to environmental influences and road characteristics, but sometimes car detectors fails to interpret their information causing risky situations. The problem of recent automated car systems is that they base their decision just on the present moment failing to consider the next steps of a human trajectory, which is something important that has to be taken into account in order to better decide whether to stop the vehicle or not in front of a potential dangerous scenario.
For all these reasons the overall objective of the project is to develop an Artificial Intelligence module able to predict possible human trajectories, along with their level of confidence, in order to represent the last module of a detection, tracking and prediction architecture that can lead to a robust and complete obstacle detection
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