InfraParis: A multi-modal and multi-task autonomous driving dataset

• Published in: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) pp. 2961 - 2971
• Main Authors: Franchi, Gianni, Hariat, Marwane, Yu, Xuanlong, Belkhir, Nacim, Manzanera, Antoine, Filliat, David
• Format: Conference Proceeding
• Language: English
• Published: IEEE 03.01.2024
• Subjects: Algorithms; Applications; Autonomous Driving; Computational modeling; Computer vision; Data models; Datasets and evaluations; Multitasking; Object detection; Semantic segmentation; Uncertainty

Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes. Consequently, these models struggle to handle new objects, noise, nighttime conditions, and diverse scenarios, which is esse...