SAIAD 2020
2nd Workshop on Safe Artificial Intelligence for Automated Driving
In conjunction with IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)
Sunday, June 14, 2020 in Seattle, Washington
In conjunction with IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)
Sunday, June 14, 2020 in Seattle, Washington
Phases of the DNN Development Process
Automotive safety is one of the core topics in the development and integration of new automotive functions. Automotive safety standards have been established decades ago and provide a description of requirements and processes that ensure the fulfillment of safety goals. However, artificial intelligence (AI) as a core component of automated driving functions is not considered in sufficient depth in existing standardizations. It is obvious that these need to be extended as a prerequisite for developing safe AI-based automated driving functions. And this is a challenge, due to the seemingly opaque nature of AI methods.
In this workshop, we raise safety-related questions and aspects that arise under the five phases of the DNN development process (see figure to the left). Our focus is on supervised deep learning models for perception.
Thomas Brox University of Freiburg
Alexandre Haag Autonomous Intelligent Driving
Andreas Geiger University of Tübingen
Zico Kolter Carnegie Mellon University
Patrick Pérez valeo.ai
This workshop aims to bring together various researchers from academia and industry that work at the intersection of autonomous driving, safety-critical AI applications, and interpretability. After a very successful first edition of the SAIAD Workshop at CVPR 2019, we feel that further discussion on the safety aspects of artificial intelligence for automated driving is necessary. The previous edition of this workshop attracted the attention of about 150 participants, 6 high profile keynote speakers, several press releases, as well as 4 oral and 8 poster presentations.
The organizers of this workshop are part of the project “KI-Absicherung”, funded by the German Ministry for Economic Affairs and Energy. The project aims at standardizing strategies for ensuring and proving the safety of perception DNNs in Automated Driving (AD).