The KI-ASIC Dataset

We present a novel dataset captured from a BMW X5 test carrier within the German research project KI-ASIC for use in radar sensor development and autonomous driving research. Our work aims at providing a blueprint for the process of creating labeled datasets for the development of neural networks for pattern recognition in radar data in the automotive environment. With a variety of different sensor types such as wide angle color cameras, a high-resolution color stereo camera, an Ouster OS1-64 laser scanner and three novel Infineon radar sensors, we recorded over 100,000 scenes of real traffic scenarios as well as defined test scenarios with a frequency of 10 Hz. The scenarios in real traffic contain inner-city situations, but also scenes from rural areas with static and dynamic objects. Besides, the defined test scenarios are based on the NCAP scenarios and focus mostly on turning, overtaking and follow-up maneuvers. The data from the different sensors is calibrated, synchronized and timestamped including raw and rectified information. Our dataset also contains labels for all detected objects from a defined class list with distance and angle properties. </p

Industrial AI Technologies for Next-Generation Autonomous Operations with Sustainable Performance

Directeur de l'ouvrage : Ovidiu Vermesan and Mario Diaz NavaCollection : Series in Communications and NetworkingInternational audienc

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Unmanned Aerial Systems (UAS) have rapidly gained attraction in recent years as a promising solution to revolutionize numerous applications and meet the growing demand for efficient and timely delivery services due to their highly automated operation framework. Beyond Visual Line of Sight (BVLOS) operations, in particular, offer new means of delivering added-value services via a wide range of applications. This "plateau of productivity" holds enormous promise, but it is challenging to equip the drone with affordable technologies which support the BVLOS use case. To close this gap, this work showcases the convergence of the automotive and aviation industries to advance BVLOS aviation for UAS in a practical setting by studying a combination of Commercial Off-The-Shelf (COTS) technologies and systems. A novel risk-based approach of investigating the key technological components, architectures, algorithms, and protocols is proposed that facilitate highly reliable and autonomous BVLOS operations, aiming to enhance the alignment between market and operational needs and to better identify integration requirements between the different capabilities to be developed.Signal Processing System

Programmable Systems for Intelligence in Automobiles (PRYSTINE): Final results after Year 3

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3