Title: TWICE dataset: digital twin of test scenarios in a controlled environment

Authors: Leonardo Novicki Neto; Fabio Reway; Yuri Poledna; Maikol Funk Drechsler; Christian Icking; Werner Huber; Eduardo Parente Ribeiro

Addresses: Federal University of Parana – UFPR, Centro Politecnico, Curitiba, PR, 81531-980, Brazil ' CARISSMA – Institute of Automated Driving, Esplanade 10, Ingolstadt, Germany ' CARISSMA – Institute of Automated Driving, Esplanade 10, Ingolstadt, Germany ' CARISSMA – Institute of Automated Driving, Esplanade 10, Ingolstadt, Germany ' FernUniversität in Hagen, Universitätsstraße 11, Hagen, Germany ' CARISSMA – Institute of Automated Driving, Esplanade 10, Ingolstadt, 85049, Germany ' Federal University of Parana – UFPR, Centro Politecnico, Curitiba, PR, 81531-980, Brazil

Abstract: Ensuring autonomous vehicle safety in adverse weather remains a challenge. To address this, we developed a validation dataset using data from cameras, radar, and LiDAR, collected both on a real test track and in simulation. Our dataset supports the evaluation of object detection algorithms in conditions like rain, nighttime, and snow. Inspired by Euro European New Car Assessment Programme (Euro NCAP), it includes scenarios with cars, cyclists, trucks, and pedestrians. Data was recorded in a simulation-based hardware-in- the-loop testing framework, which utilises the same sensors (camera and radar) used in real-world test drives and features a digital twin of the proving ground. Spanning over 2 h and 280GB, this dataset aids researchers in testing and improving detection algorithms in both real and virtual environments. Available at: https://twicedataset.github.io/site/

Keywords: autonomous driving; environment sensors; camera; radar; LiDAR; hardware-in-the-loop.

DOI: 10.1504/IJVSMT.2025.147353

International Journal of Vehicle Systems Modelling and Testing, 2025 Vol.19 No.2, pp.152 - 170

Received: 08 Apr 2024
Accepted: 15 Jun 2024
Published online: 14 Jul 2025 *

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