| KEYWORD |
Development and improvement of Cooperative Adaptive Cruise Control strategies based on Reinforcement Learning
keywords MOBILITY, REINFORCEMENT LEARNING
Reference persons DANIELA ANNA MISUL
Research Groups PT-ERC
Thesis type NUMERICAL AND PROGRAMMING
Description The recent rise of Advanced Driver Assistance Systems (ADAS) and connectivity in the Automotive field applyed to Connected and Autonomous Vehicles (CAVs) has led the research efforts to investigate new control strategies for improving Mobility solutions.
An alternative approach that gained recently interest and which can be applyed to complex control problems is Reinforcement Learning (RL). RL is a branch of Machine Learning that consists in training an Agent to behave in a desired manner, and that has been recently proved to reach comparable or enhanced performance with respect to more common optimal control strategies such as Model Predictive Control, especially in terms of computational costs and in presence of high dimensional and uncertain environments.
This thesis is proposed as a prosecution of previous works about a Cooperative Adaptive Cruise Control (CACC) based on Reinforcement Learning. In particular, it aims on possible improvements and developments concerning performance, safety, comfort and energy-saving features for heavyweight and/or lightweight CAVs.
Deadline 11/11/2023
PROPONI LA TUA CANDIDATURA