AI Based Environment Domain Adaptation for Automotive Safety

• Grant number: 24/22985-3
• Support Opportunities: Regular Research Grants
• Start date: June 01, 2025
• End date: May 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Jurandy Gomes de Almeida Junior
• Grantee: Jurandy Gomes de Almeida Junior
• Principal researcher abroad: Werner Huber
• Institution abroad: Technical University of Ingolstadt., Germany
• Host Institution: Centro de Ciências em Gestão e Tecnologia (CCGT). Universidade Federal de São Carlos (UFSCAR). Campus de Sorocaba. Sorocaba , SP, Brazil
• Associated researchers: Roberto Silva Netto ; Tiago Agostinho de Almeida
• Associated research grant: 23/17577-0 - Video understanding through deep learning with minimal human supervision, AP.R

Abstract

This project proposes a collaborative research initiative between the Federal University of São Carlos (UFSCar) and the Sorocaba Engineering School (FACENS), in Brazil, and the Technical University of Ingolstadt (THI), in Germany, under the Seed Fund Call for Proposals 2024, Track 1. The primary objective is to strengthen international partnerships between Brazil and Germany while delivering co-created solutions to complex challenges in vehicle safety, innovative mobility, and artificial intelligence (AI). Through synergistic collaboration and the dynamic exchange of expertise, this initiative will advance state-of-the-art approaches to automotive safety in adverse weather conditions, including rain, road spray, and snow. By leveraging deep learning techniques with minimal human supervision, the project aims to address critical gaps in autonomous vehicle technology, enhancing reliability and safety. This collaboration is strategically embedded within the ongoing projects FAPESP #2023/17577-0, CONECTA 2030 (Fundep, Rota 2030/Linha V, grant 29271.02.01/2022.04-00), AD-Approved, and the EU-Horizon Project ROADVIEW. Together, these initiatives provide a robust framework to integrate and expand the impact of the proposed research, fostering sustainable innovation in AI-driven mobility. (AU)