Title:	Deep MultiModal Learning for Automotive Applications
DNr:	Berzelius-2024-312
Project Type:	LiU Berzelius
Principal Investigator:	Selpi Selpi <selpi@chalmers.se>
Affiliation:	Chalmers tekniska högskola
Duration:	2024-09-01 – 2025-03-01
Classification:	10207
Homepage:	https://research.chalmers.se/project/11327
Keywords:

Abstract

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.