| KEYWORD |
Model Predictive Control and Artificial Potential Fields for SAE L4 Automated Driving of shuttles in restricted areas
Parole chiave AUTOMATED DRIVING SYSTEMS, AUTONOMOUS VEHICLES, MODEL PREDICTIVE CONTROL
Riferimenti MASSIMO CANALE
Riferimenti esterni Ing. P. Borodani (Centro Ricerche Fiat / Stellantis)
Descrizione Transportation challenge: many transportation networks contain first/last-mile gaps. Low-volume transit routes are often expensive to run. Paratransit is often expensive to operate and may require passengers to book rides in advance.
The focus will be on shuttles that provide rides between fixed, high-demand, waypoints such as transit stops, retail centers and office parks. The shuttle operates on restricted environments, such as service roads or busways, where there are included airport shuttles or transit connectors, e.g., service between bus stops and train stations. Shuttles serve fixed waypoints and operate on a few certified routes (low complexity scenario).
It is considered an eco-system context, consequently a multimodal sensing approach: on-board sensors, environmental data, off-board information, etc.
The core approach relies on the employment of Model Predictive Control (MPC) and Artificial Potential Fields (APF). APF approach, where the environment is represented by the combination of different fields, enables the MPC design to generate an optimal and safe path, given by the minimum energy trajectory over time.
Conoscenze richieste Solid background in the field of Automatic Control including advanced methodology such as Model Predictive Control. Furthermore, deep expertise of MatLab programming and Simulink environment is required. Finally, basics of mechanical modeling of ground vehicles and automated driving tasks are needed as additional specific knowledge.
Note Candidates having at most two exams left are selected based on CV and interview.
Scadenza validita proposta 31/12/2023
PROPONI LA TUA CANDIDATURA