RSEngineering (Maranello) - Thesis and Internships
1 - Development of a lateral-longitudinal vehicle driver model
To improve the accuracy and flexibility of laptime and duty-cycle simulations, RSE has set the goal of internally developing a new vehicle driver model capable of performing longitudinal, lateral, and combined maneuvers. The driver will be based on advanced techniques such as Reinforcement Learning or Model Predictive Control.
2 - Development of a "one-shot" fast lap time optimizer for track performance
RSE aims to internally develop a tool based on optimal control theories and numerical optimization to estimate lap times for high-performance vehicles. The tool output should include both the optimal speed profile for the given vehicle and the optimal driving trajectory.
3 - Development and implementation of advanced vehicle dynamics control algorithms for high-performance hybrid/electric vehicles
Identification, through a review of scientific literature, of the state of the art in the field of integrated vehicle dynamics control (control of a specific vehicle dynamic, such as lateral dynamics, using multiple actuators—e.g., Torque Vectoring and Rear Wheel Steering—that act in a non-independent way on that dynamic). Development of the high-level control algorithm and subsequently the allocator that optimally distributes the control action among the available actuators (electric motors, active anti-roll bars, active suspensions, etc.).
Integration of the control logic into the RSEngineering vehicle model and subsequent validation through Off-Line simulations (Model-in-the-Loop) and On-Line simulations (Driving Simulator).
4 - Development and implementation of vehicle state estimators
Identification, through a review of scientific literature, of the state of the art in the field of vehicle state estimation (Vx, Beta, Fz at the wheels, etc.). Implementation of the identified logic within the RSEngineering vehicle model and validation through Off-Line simulations (Model-in-the-Loop) and On-Line simulations (Driving Simulator).
5 - Development of driver assistance systems (ADAS) logic
Design of control features in the field of driver assistance systems. The initial focus will be on longitudinal dynamics-related logic, such as Adaptive Cruise Control.
6 - Creation of a component identification and modeling procedure using AI
Continuation of a study already initiated at RSE, aimed at creating a procedure capable of automatically generating neural network-based models to replace simulation components created using traditional modeling or black-box components provided by suppliers.
7 - Development of a neural network training methodology for image recognition
Identification and validation of a methodology for pre-processing, training of the neural network, and subsequent post-processing for image recognition, both for automotive applications (lane and road sign recognition) and industrial applications (generic object identification).
Published on: June 09 2025
