PROSENSE project
Title: PROSENSE project
SNIC Project: Berzelius-2022-225
Project Type: LiU Berzelius
Principal Investigator: Yi Yang <yiya@kth.se>
Affiliation: Kungliga Tekniska högskolan
Duration: 2022-12-01 – 2023-06-01
Classification: 20201
Homepage: https://www.vr.se/english/swecris.html#/project/2020-02963_Vinnova
Keywords:

Abstract

Purpose and goal: Despite rapid research & innovation in scene perception for autonomous driving, many challenges remain open in understanding complex traffic scenes with occluded road users. To overcome these challenges, Prosense aims to extend the state-of-the-art by: 1. Incorporating multi-sensor information to create a scene representation that includes possible occlusions. 2. Enhancing the robustness of scene perception in different traffic scenarios. 3. Integrating scene metadata for context-aware detection & classification of occluded road users, object anticipation & prediction. Expected results and effects: At the end of the project, the advancements in key technology areas will extend the scene perception capabilities. The main results of this project are: 1. Developed algorithms for handling occlusions based on multi-modal sensor data and scene metadata. 2. Integration of advanced algorithms for scene perception in the presence of occluded objects on board an autonomous research vehicle. 3. Pubic demonstration of integrated algorithms emulating complex traffic occlusions.