ITSC 2025 Paper Abstract

Paper TH-LA-T28.3

Schütte, Stefan (TU Dortmund University), Osterburg, Timo (TU Dortmund), Bertram, Torsten (Technische Universität Dortmund)

Improving Camera-Based BEV Map Segmentation by Sampling Repeatedly

Scheduled for presentation during the Regular Session "S28c-Multi-Sensor Fusion and Perception for Robust Autonomous Driving" (TH-LA-T28), Thursday, November 20, 2025, 16:40−17:00, Stradbroke

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Deep Learning for Scene Understanding and Semantic Segmentation in Autonomous Vehicles, Advanced Sensor Fusion for Robust Autonomous Vehicle Perception

Abstract

Semantic segmentation of the scene surrounding a car in Bird's-Eye-View (BEV) is an important task for the safe operation of an automated vehicle. Information about drivable areas and interactions with vulnerable road users such as pedestrians needs to be available. Most methods for local semantic scene segmentation rely on a combination of cameras with expensive lidar sensors for accurate mapping of the BEV scene. We propose a camera-only approach that aims to solve the map segmentation task by estimating ground height in the scene for resource efficient perspective view to BEV lifting.