SCOPE: A Synthetic Multi-Modal Dataset for Collective Perception Including Physical-Correct Weather Conditions

TL;DR

SCOPE is a comprehensive synthetic multi-modal dataset designed for collective perception in autonomous driving, featuring realistic weather conditions, diverse scenarios, and digital-twin maps to facilitate development and testing of perception technologies.

Contribution

It introduces the first synthetic multi-modal dataset with realistic weather simulations, diverse scenarios, and digital-twin maps for collective perception research.

Findings

Includes 17,600 frames across 40+ scenarios

Features realistic camera and LiDAR models with weather effects

Contains data from multiple collaborative agents and infrastructure sensors

Abstract

Collective perception has received considerable attention as a promising approach to overcome occlusions and limited sensing ranges of vehicle-local perception in autonomous driving. In order to develop and test novel collective perception technologies, appropriate datasets are required. These datasets must include not only different environmental conditions, as they strongly influence the perception capabilities, but also a wide range of scenarios with different road users as well as realistic sensor models. Therefore, we propose the Synthetic COllective PErception (SCOPE) dataset. SCOPE is the first synthetic multi-modal dataset that incorporates realistic camera and LiDAR models as well as parameterized and physically accurate weather simulations for both sensor types. The dataset contains 17,600 frames from over 40 diverse scenarios with up to 24 collaborative agents, infrastructure sensors, and passive traffic, including cyclists and pedestrians. In addition, recordings from two novel digital-twin maps from Karlsruhe and T\"ubingen are included. The dataset is available at https://ekut-es.github.io/scope