Deep MultiModal Learning for Automotive Applications
Title: Deep MultiModal Learning for Automotive Applications
DNr: Berzelius-2024-79
Project Type: LiU Berzelius
Principal Investigator: Selpi Selpi <>
Affiliation: Chalmers tekniska högskola
Duration: 2024-02-25 – 2024-09-01
Classification: 10207


This project aims to create multimodal sensor fusion methods for advanced and robust automotive perception systems. The project will focus on three key areas: (1) Develop multimodal fusion architectures and representations for both dynamic and static objects. (2) Investigate self-supervised learning techniques for the multimodal data in an automotive setting. (3) Improve the perception system’s ability to robustly handle rare events, objects, and road users.