Papers

NameDesriptionRelease DateDownload LinksEvent
How much real data do we actually need: Analyzing object detection performance using synthetic and real dataSupervised training of deep models requires a large amount of annotated data to be available. However, data annotation is a tremendously exhausting and costly task to perform. One alternative is to use synthetic data. This paper provides a comprehensive study of the effects of replacing real data with synthetic data.Jul 2019https://arxiv.org/abs/1907.07061International Conference on Machine Learning, ICML 2019 - AI for Autonomous Driving, Long Beach, California, USA
Deep Open Space Segmentation using Automotive RadarThis paper describes an AI approach for advanced deep segmentation from radar. This neural network is trained to identify open space in parking scenarios. Different deep models are evaluated with various radar input representations. This system achieves low memory usage and real-time processing speeds, and is thus very well suited for embedded deployment.Mar 2020https://arxiv.org/pdf/2004.03449.pdfInternational Conference on Microwaves for Intelligent Mobility, ICMIM 2020, Linz, Austria

Datasets

NameDesriptionRelease DateDownload LinksAssociated Paper
SCORPA publically available dataset of radar observations called SCORP was collected. It is composed of 3913 frames, collected in 11 driving sequences. It is annotated with all drivable open spaces in the scene and accompanied with corresponding camera images The dataset gives access to Analog-to-Digital Converter (ADC) radar signals and annotations.Mar 2020Downloadhttps://arxiv.org/pdf/2004.03449.pdf