5 research outputs found
Multimodal 3D Object Detection from Simulated Pretraining
The need for simulated data in autonomous driving applications has become
increasingly important, both for validation of pretrained models and for
training new models. In order for these models to generalize to real-world
applications, it is critical that the underlying dataset contains a variety of
driving scenarios and that simulated sensor readings closely mimics real-world
sensors. We present the Carla Automated Dataset Extraction Tool (CADET), a
novel tool for generating training data from the CARLA simulator to be used in
autonomous driving research. The tool is able to export high-quality,
synchronized LIDAR and camera data with object annotations, and offers
configuration to accurately reflect a real-life sensor array. Furthermore, we
use this tool to generate a dataset consisting of 10 000 samples and use this
dataset in order to train the 3D object detection network AVOD-FPN, with
finetuning on the KITTI dataset in order to evaluate the potential for
effective pretraining. We also present two novel LIDAR feature map
configurations in Bird's Eye View for use with AVOD-FPN that can be easily
modified. These configurations are tested on the KITTI and CADET datasets in
order to evaluate their performance as well as the usability of the simulated
dataset for pretraining. Although insufficient to fully replace the use of real
world data, and generally not able to exceed the performance of systems fully
trained on real data, our results indicate that simulated data can considerably
reduce the amount of training on real data required to achieve satisfactory
levels of accuracy.Comment: 12 pages, part of proceedings for the NAIS 2019 symposiu
Area-wide real-world test scenarios of poor visibility for safe development of automated vehicles
Principles of neural information processing
In this fundamental book the authors devise a framework that describes the working of the brain as a whole. It presents a comprehensive introduction to the principles of Neural Information Processing as well as recent and authoritative research. The books´ guiding principles are the main purpose of neural activity, namely, to organize behavior to ensure survival, as well as the understanding of the evolutionary genesis of the brain. Among the developed principles and strategies belong self-organization of neural systems, flexibility, the active interpretation of the world by means of construction and prediction as well as their embedding into the world, all of which form the framework of the presented description. Since, in brains, their partial self-organization, the lifelong adaptation and their use of various methods of processing incoming information are all interconnected, the authors have chosen not only neurobiology and evolution theory as a basis for the elaboration of such a framework, but also systems and signal theory. The most important message of the book and authors is: brains are evolved as a whole and a description of parts although necessary lets one miss the wood for the trees