| Name | Desription | Release Date | Download Links | Event |
|---|
| How much real data do we actually need: Analyzing object detection performance using synthetic and real data | Supervised 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 2019 | https://arxiv.org/abs/1907.07061 | International Conference on Machine Learning, ICML 2019 - AI for Autonomous Driving, Long Beach, California, USA |
| Deep Open Space Segmentation using Automotive Radar | This 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 2020 | https://arxiv.org/pdf/2004.03449.pdf | International Conference on Microwaves for Intelligent Mobility, ICMIM 2020, Linz, Austria |
| PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain | Camera and Lidar processing have been revolutionized with the rapid development of deep learning model architectures. Automotive radar is one of the crucial elements of automated driver assistance and autonomous driving systems. Radar still relies on traditional signal processing techniques, unlike camera and Lidar based methods. We believe this is the missing link to achieve the most robust perception system. Identifying drivable space and occupied space is the first step in any autonomous decision making task. Occupancy grid map representation of the environment is often used for this purpose. In this paper, we propose PolarNet, a deep neural model to process radar information in polar domain for open space segmentation. We explore various input-output representations. Our experiments show that PolarNet is a effective way to process radar data that achieves state-of-the-art performance and processing speeds while maintaining a compact size. | Mar 2021 | https://arxiv.org/abs/2103.03387 | 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021) |
| Point Cloud based Hierarchical Deep Odometry Estimation | Processing point clouds using deep neural networks is still a challenging task. Most existing models focus on object detection and registration with deep neural networks using point clouds. In this paper, we propose a deep model that learns to estimate odometry in driving scenarios using point cloud data. The proposed model consumes raw point clouds in order to extract frame-to-frame odometry estimation through a hierarchical model architecture. Also, a local bundle adjustment variation of this model using LSTM layers is implemented. These two approaches are comprehensively evaluated and are compared against the state-of-the-art. | Mar 2021 | https://arxiv.org/abs/2103.03394 | 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021) |
