Title:	Perception for Autonomous Driving Systems
DNr:	Berzelius-2024-347
Project Type:	LiU Berzelius
Principal Investigator:	Georg Hess <georghe@chalmers.se>
Affiliation:	Chalmers tekniska högskola
Duration:	2024-09-13 – 2025-04-01
Classification:	10207
Keywords:

Abstract

This project aims at exploring different methods related to perception for autonomous driving. We currently consider two different paths, one for using advanced in neural rendering for automotive data, and one for connecting natural language and point clouds. We ran a successful pilot project with our previous Berzelius application, resulting in two papers at CVPR, a top-tier computer vision conference, and hope to now extend our work further. There are multiple unanswered questions, e.g., whether this is a useful pre-training step for perception models for boosting performance on object detection and/or tracking. This has the potential to reduce the need for costly and laborious manual labeling. Further, we want to train larger models with more capacity and use multiple datasets to improve robustness of our method. We believe this research project has great potential, and we once again aim to publish our results in a top-tier ML-conference.