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May 28, 2019: Guest lecture by Prof. Mathieu Joerger, University of Arizona

On Tuesday, 28th May, Dr. Mathieu Joerger (Assistant Professor of Aerospace and Mechanical Engineering, The University of Arizona) will give a talk within the RTG i.c.sens, to which you are cordially invited:


Mathieu Joerger, PhD (Assistant Professor of Aerospace and Mechanical Engineering, The University of Arizona):
„Multisensory Safety Monitoring for Autonomous Vehicles”
When? Tuesday, May 28th 2019, 10:00 - 11:30 am
Where? Room V411, Schneiderberg 50

Abstract

Autonomous vehicles carry the promise of safe, fuel-efficient, and time-efficient mobility for all.  These benefits are predicated on autonomous navigation systems achieving unprecedented levels of safety.  Substantial resources have been deployed over the past 30 years in civilian aviation to meet limits of acceptability on positioning errors, or ‘alerts limits’, of 10 m to 35 m with high probability (on the order of 1-10-9).  In comparison, alert limits for self-driving cars will range from 0.5 m to 1 m, which will only be achievable using new multisensory systems.
This presentation describes the design, analysis and evaluation of new methods to quantify navigation safety for autonomous vehicles.  The first part of the talk provides an overview of Advanced Receiver Autonomous Integrity Monitoring (ARAIM), a cooperative research effort by the European Union and the United States to analyze future Global Navigation Satellite Systems (GNSS).  Research contributions include, for example, the design of optimal estimators, which specifically minimize safety risk rather than maximizing accuracy.  The second part of the talk investigates whether analytical safety methods used in aviation can be leveraged for navigation in automated driving systems (ADS).  Self-driving cars caused fatal accidents, including the May 2016 crash of a Tesla Model S in Florida, and the 2018 collision of an autonomous Uber ADS with a pedestrian in Arizona.  A new method is introduced for quantifying risks in data association, a process aimed at matching sensor measurements with mapped landmarks, which is needed for laser and radar localization.