Research, Development and Analysis of Performance Indices for Autonomous Vehicle Steering

• Grant number: 24/14564-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: April 01, 2025
• End date: December 31, 2025
• Field of knowledge: Engineering - Electrical Engineering - Industrial Electronics, Electronic Systems and Controls
• Principal Investigator: Lucas Barbosa Marcos
• Grantee: Bruno Campos Trovatti
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil

Abstract

The research and development of autonomous vehicles has gained a lot of emphasis in recent years due to the development of powerful embedded systems. This development created the possibility of implementing more complex and efficient controllers for vehicular applications. However, practical implementations of these systems are still in development, and the performance of various types of controllers have not yet been tested for a diverse range of applications and situations. This scientific initiation project seeks to create a quantitative and qualitative classification for different types of controllers when they are used to control the steering of autonomous vehicles. This research proposal aims to organize which performance criteria are the most relevant for certain vehicular applications and, from that, to determine which control techniques are most appropriate for each case. To obtain these results, the first step is to research and survey through scientific articles which performance criteria their authors considered most relevant for a variety of use-cases (personal transport or cargo transport vehicles, in paved or dirt roads, etc.). Next, the mathematical models of these vehicles and a series of controllers will be implemented for evaluation in numerical simulations and in physics simulation software. Upon completion, a new methodology will be created to determine quality of controllers in these specific applications, as well as a ranking of the controllers studied. This classification will help future researchers and control engineers to have a foundation to evaluate or choose the most suitable control models for their autonomous vehicles.