Autonomous Vehicles and Sensor Fusion

Topics Cooperative fusion for tracking of pedestrians from a moving vehicle Radar-Video sensor fusion LIDAR-Radar-Video sensor fusion Radar pedestrian detection using deep learning Multimodal vision 2D multi-modal video fusion for wide-angle environment perception Visible-thermal video enhancement for detection of road users Automotive High Dynamic Range (HDR) imaging Classic multi-exposure HDR reconstruction Intelligent HDR tone mapping for traffic applications Learning-based HDR video reconstruction and tone mapping Efficient multi-sensor data annotation tool Point-cloud processing Fast Low-level Point-cloud processing Point-cloud based Object detection and tracking Environment mapping and odometry Liborg - Lidar based mapping LIDAR based odometry Monocular visual odometry Automotive occupancy mapping Obstacle detection based on 3D imaging Real-time sensor data processing for autonomous vehicles using Quasar - demo video Cooperative fusion for tracking of pedestrians from a moving vehicle Autonomous vehicles need to be able to detect other road users and roadside hazards at all times and in all conditions.

GPU accelerated image processing using Quasar

Today's algorithms (e.g., image/video processing, hyperspectral sensor data, ...) require huge amounts of data. For many algorithms, a good computational performance is indispensable for use in practical applications. These applications are often targeted toward a big diversity of devices, such as desktop PCs, tablets, smartphones, mini PCs. To reach a good computational performance, modern GPUs bring speedups of 10x-200x for highly parallel processing tasks, but one main disadvantage is the difficulty of programming: not only does (properly) programming a GPU require an extensive in-depth knowledge of the details of a GPU, the development efforts are usually high, which causes GPUs not easily to be used for research purposes, e.g., for devising and testing of new algorithms. Then, when CPUs and GPUs of different types and models are combined, the development and debugging complexity level further increases. One of our concerns is that training a developer (in academia, sometimes a.k.a. "Ph.

From waste to resource thanks to characterization of waste streams

Results of the UGent, VITO, Suez, and Umicore collaboration within the CHARAMBA project (EIT Raw Materials)