Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness

no code implementations • 21 Feb 2024 • David Fernández Llorca, Ronan Hamon, Henrik Junklewitz, Kathrin Grosse, Lars Kunze, Patrick Seiniger, Robert Swaim, Nick Reed, Alexandre Alahi, Emilia Gómez, Ignacio Sánchez, Akos Kriston

This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements for trustworthy AI.

Autonomous Vehicles Decision Making +1

Improving self-calibration

no code implementations • 4 Dec 2013 • Torsten A. Enßlin, Henrik Junklewitz, Lars Winderling, Maksim Greiner, Marco Selig

Contemporary self-calibration schemes try to find a self-consistent solution for signal and calibration by exploiting redundancies in the measurements.

NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference

no code implementations • 18 Jan 2013 • Marco Selig, Michael R. Bell, Henrik Junklewitz, Niels Oppermann, Martin Reinecke, Maksim Greiner, Carlos Pachajoa, Torsten A. Enßlin

NIFTY, "Numerical Information Field Theory", is a software package designed to enable the development of signal inference algorithms that operate regardless of the underlying spatial grid and its resolution.

Instrumentation and Methods for Astrophysics Information Theory Mathematical Software Mathematical Physics Information Theory Mathematical Physics Data Analysis, Statistics and Probability Computation