Analysis and Development of Algorithms for High-Performance Vehicles
Advanced Driving Assistance Systems (ADAS) and autonomous driving are among the most investigated and promising technologies of automotive research. ADAS are electronic systems that can actuate autonomously under certain condition increasing significantly driving performance and safety. The introduction of self-driving cars has the potential to revolutionize the mobility sector catalysing the adoption of electric vehicles, reducing CO2 emissions and enhancing passenger safety. Nowadays, the field of vehicle dynamics and control is facing a pivotal moment, with significant advancements emerging to address the challenges of modern high-performance vehicles. Within this context, the research focuses on the development of estimation algorithms and vehicle control systems leveraging cutting-edge technologies. During the first year, as a seamless extension of the research work conducted during the MSc thesis, the focus has been on estimating the friction coefficient during combined longitudinal and lateral maneuvers. The next phase of the research will extend beyond grip estimation, with the objective of exploring its integration into classical vehicle control algorithms, such as traction control, to improve performance and robustness.
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