8 RESULTS
UncategorizedASD 2023 Call for Contributions

ASD 2023 Call for Contributions

DATE 2023 Initiative on Autonomous Systems Design (ASD)

Fueled by the progress of artificial intelligence, autonomous systems become more and more integral parts of many Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) applications, such as automated driving, robotics, avionics, industrial automation and smart systems in general. Autonomous systems are self-governed and self-adaptive systems that are designed to operate in an open and evolving environment which is not completely defined at design time. This poses a unique challenge to the design and verification of dependable autonomous systems. Following the successful editions from previous years, DATE is again hosting the Initiative on Autonomous Systems together with a workshop. The initiative will include peer-reviewed papers, invited contributions and interactive sessions.

Areas of Interest

We welcome your contributions within the scope of the event. The main areas of interest include but are
not limited to the following:

  • Concepts, Algorithms and Formal Methods for Autonomy
  • Platforms for Autonomous Systems
  • Architectures (from Chip Level to System Level) for Autonomous Systems
  • Middleware and Frameworks (e.g., ROS) for Autonomous Systems
  • Models and HW/SW Mechanisms for Self-Awareness and Self-Adaptation
  • Design of Autonomous Systems
  • Mastering Emergent and Evolving Behavior (Goals, Constraints, …)
  • Design, Verification and Test of High-Assurance Learning-Enabled Systems
  • Design Automation and Methodologies for Autonomous Systems
  • Dependability and Trustworthy Autonomy
  • Functional Safety and Assurance Cases for Evolving and Learning-Enabled Systems
  • HW/SW Mechanisms for Resilient Systems
  • Autonomous Systems Security
  • Applications and Case Studies of Autonomous Systems

Types of Contributions

Conference Papers:

The special initiative features regular sessions for presenting novel technical contributions. Submitted papers will undergo a peer-review process and accepted papers will appear in the DATE conference
proceedings. All manuscripts should be submitted in pdf format not exceeding 6 pages, following the DATE submission instructions. Detailed submission instructions can be found here: https://www.dateconference.com/call-for-papers#Submission-Instructions

  • Abstract Submission: 06 November 2022 AoE
  • Full Paper Submission: 13 November 2022 AoE
  • Acceptance Notification: 18 December 2022 AoE

Special Sessions / Panels:

We additionally solicit proposals for special sessions, panel discussions and invited talks, targeting emerging challenges in the field of autonomous systems, especially that are of interest to the DATE conference participants. Special Session proposals must consist of a title of the session, a summary of up to 500 words in a PDF file, describing the topic, (preferably confirmed) authors/speakers, and the intended format.

Submission deadline is 13 November 2022.

Submission Instructions

All submissions should follow the DATE proceedings submission instructions. Please submit your contribution at https://www.softconf.com/date23/special_ASD/

Workshop Interactive Day Contributions

The workshop Interactive Day will feature highly interactive sessions on emerging or controversial hot topics in the scope of the ASD initiative. We also encourage sessions on business trends or public policies. The form of a session can be a special session with invited talks and panel discussions, leaving enough room for interaction among the participants. Sessions on industrial perspectives, prototypes and demos are also highly welcome.

Organizing Committee

  • Rolf Ernst, Technical University of Braunschweig, Germany
  • Dirk Ziegenbein, Robert Bosch GmbH, Germany
  • Selma Saidi, Technical University Dortmund, Germany
  • Sebastian Steinhorst, Technical University Munich, Germany
UncategorizedASD 2022

ASD 2022

DATE 2022 Initiative on Autonomous Systems Design (ASD)

14-18 March 2022

Fully virtual event

Scope of the Initiative on Autonomous Systems Design (ASD)

Fueled by the progress of artificial intelligence, autonomous systems become more and more integral parts of many Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) applications, such as automated driving, robotics, avionics, industrial automation and smart systems in general. Autonomous systems are self-governed and self-adaptive systems that are designed to operate in an open and evolving environment which is not completely defined at design time. This poses a unique challenge to the design and verification of dependable autonomous systems. Following the successful editions from previous years, DATE is again hosting the Initiative on Autonomous Systems Design on Tuesday, Thursday and Friday. The initiative will include peer-reviewed papers, invited contributions and interactive sessions.

ASD 2022 Program Overview 


Date: Tuesday, 15 March 2022 (online)

Panel Discussion: Autonomous Systems Design as a Research Challenge

Time: 16:30-18:00 CET 

Session Description: Autonomous systems require specific design methods that leave behavioral freedom and plan for the unexpected without losing trustworthiness and dependability. How does this requirement influence research at major research institutions? How is it reflected in public funding? Should autonomous systems design become a new discipline or should the regular design process be adapted to handle autonomy? The panel will begin with position statements by the panelists, followed by an open discussion with the hybrid audience.

PanelistsKarl-Erik Arzen, Lund University and co-director Wallenberg AI, Autonomous Systems and Software Program, Sweden;   Peter Liggesmeyer, Director Fraunhofer Institute for Experimental Software Engineering IESE, Germany;   Axel Jantsch, Director Institute of Computer Technology, TU Wien, Austria

Organizers:

  • Selma Saidi, TU Dortmund, DE
  • Rolf Ernst, TU Braunschweig, DE

Date: Thursday, 17 March 2022 (online)

Chair: Sharon Hu, University of Notre Dame, USA

Co-chair: Selma Saidi, TU Dortmund, DE

Session 1: Interactive Presentations Session

Time: 11:30-12:15 CET (Interactive Presentations Session)

Session Chair: Philipp Mundhenk, Robert Bosch GmbH, DE

Session Co-Chair: Rolf Ernst, TU Braunschweig, DE

  • Deadlock Analysis and Prevention for Intersection Management Based on Colored Timed Petri Nets”, Tsung-Lin Tsou, Chung-Wei Lin and Iris Hui-Ru Jiang, National Taiwan University, Taiwan
  • “Attack Data Generation Framework for Autonomous Vehicle Sensors”, Jan Lauinger1, Andreas Finkenzeller1, Henrik Lautebach2, Mohammad Hamad1 and Sebastian Steinhorst1,  1Technical University of Munich, DE, 2ZF Group, DE
  • Contract-Based Quality-of-Service Assurance in Dynamic Distributed System”, Lea Schönberger1, Susanne Graf2, Selma Saidi1, Dirk Ziegenbein3 and Arne Hamann3, 1TU Dortmund University, DE, 2University Grenoble Alpes, CNRS, FR, 3Robert Bosch GmbH, DE

Lunch Keynote: “Probabilistic and Deep Learning Techniques for Robot Navigation and Automated Driving”

Time: 13:00 – 13:50 CET

Wolfgang Burgard, University of Freiburg, Germany
  • Wolfram Burgard, Technical University of Nuremberg, Germany
  • Bio: Wolfram Burgard is a Professor for Robotics and Artificial Intelligence at the Technical University of Nuremberg. His interests lie in Robotics, Artificial Intelligence, Machine Learning, and Computer Vision. He has published over 400 publications, more than 15 of which received best paper awards. In 2009, he was awarded the Gottfried Wilhelm Leibniz Prize, the most prestigious German research award. In 2010, he received an Advanced Grant from the European Research Council. In 2021, he received the IEEE Technical Field Award for Robotics and Automation. He is a Fellow of the IEEE, the AAAI, the EurAI, and a member of the German Academy of Sciences Leopoldina as well as of the Heidelberg Academy of Sciences and Humanities.
  • Abstract: For autonomous robots and automated driving, the capability to robustly perceive environments and execute their actions is the ultimate goal. The key challenge is that no sensors and actuators are perfect, which means that robots and cars need the ability to properly deal with the resulting uncertainty. In this presentation, I will introduce the probabilistic approach to robotics, which provides a rigorous statistical methodology to deal with state estimation problems. I will furthermore discuss how this approach can be extended using state-of-the-art technology from machine learning to deal with complex and changing real-world environments.

Session 2: An Industrial Perspective on Autonomous Systems Design (Industrial Session)

Time: 14:30-15:30 CET

Session Description This session presents 4 talks from industry sharing current practices and perspectives on autonomous systems and their design. The session discusses several challenges related to software architecture solutions for safe and efficient operational autonomous systems, novel rule-based methods for guaranteeing safety, and requirements on infrastructure for autonomy currently merging CPSs as well as IT domains.    

Session Chair: Rolf Ernst, TU Braunschweig, DE

Session Co-Chair: Selma Saidi, TU Dortmund, DE

  • Symbiotic Safety: Safe and Efficient Human-Machine Collaboration by utilizing Rules”,  Tasuku Ishigooka, Hiroyuki Yamada, Satoshi Otsuka, Nobuyasu Kanekawa and Junya Takahashi,  Hitachi, Ltd., JP
  • A Middleware Journey from Microcontrollers to Microprocessors”, Michael Pöhnl, Alban Tamisier and Tobias Blass,  Apex. AI, DE
  • Reliable Distributed Systems”, Philipp Mundhenk, Arne Hamann, Andreas Heyl and Dirk Ziegenbein, Robert Bosch GmbH, DE
  • “PAVE 360 – A paradigm Shift in  autonomous driving verification with a  Digital Twin”, Tapan Vikas, Siemens EDA GmbH, DE

Session 3: Bringing Robust Deep Learning to the Autonomous Edge: New Challenges and Algorithm-Hardware Solutions (Special Session)

Time: 15:40-16:30 CET

Session Description: Deep neural networks (DNNs) are being continually deployed at the autonomous edge systems for many applications, such as speech recognition, image classification, and object detection. While DNNs have proven to be effective in handling these tasks, their robustness (i.e. accuracy) can suffer postdeployment at the edge. Moreover, designing robust deep learning algorithms for the autonomous edge is highly challenging because such systems are severely resource-constrained. This session includes four different invited talks that present the challenges and propose novel, lightweight algorithm-hardware codesign methods to improve DNN robustness at the edge. The first paper evaluates the effectiveness of various unsupervised DNN adaptation methods on real-world edge systems, followed by selecting the best technique in terms of accuracy, performance and energy. The second paper explores a lightweight image super-resolution technique to prevent adversarial attacks, which is also characterized on an Arm neural processing unit. The third paper tackles the issue of loss in DNN prediction accuracy in resistive memory-based in-memory accelerators by proposing a stochastic fault-tolerant training scheme. The final paper focuses on robust distributed reinforcement learning for swarm intelligence where it analyzes and mitigates the effect of various transient/permanent faults.

Session Organizers: Kshitij Bhardwaj, Maya Gokhale and Bhavya Kailkhura, Lawrence Livermore National Lab (LLNL), USA

Session Chair: Dirk Ziegenbein, Robert Bosch GmbH, DE
Session Co-Chair: Chung-Wei Lin, National Taiwan University, Taiwan

Talks:

  • Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study”, Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura and Maya Gokhale,  Lawrence Livermore National Lab (LLNC Lab), USA
  • Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge”, Kartikeya Bhardwaj1, Dibakar Gope2, James Ward1, Paul Whatmough2 and Danny Loh1, 1ARM Inc,USA, 2ARM Research, USA
  • Fault-Tolerant Deep Neural Networks for Processing-In-Memory based Autonomous Edge Systems”, Siyue Wang, Geng Yuan, Xiaolong Ma, Yanyu Li, Xue Lin and Bhavya Kailkhura,  Northeastern University, USA, Lawrence Livermore National Lab, USA
  • “FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems”, Zishen Wan1, Aqeel Anwar1, Abdulrahman Mahmoud2, Tianyu Jia3, Yu-Shun Hsiao, Vijay Janapa Reddi2 and Arijit Raychowdhury1, 1Georgia Institute of Technology, USA, 2Harvard University, USA, 3Carnegie Mellon University, USA.

Session 4: Safe and Efficient Engineering of Autonomous Systems (Regular Session)

Time: 16:40-17:20 CET

Session Description: This session discusses novel approaches for engineering autonomous systems considering safety and validation aspects as well as efficiency. The first paper uses an ontology-based perception for autonomous vehicles which enables a comprehensive safety analysis, the second paper relies on formal approaches for generating relevant critical scenarios for automated driving. The last paper proposes an efficient method for recharging unmanned Aerial Vehicles (UAVs) to perform a large-scale remote sensing with maximal coverage.

Session Chair: Sebastian Steinhorst, Technical University Munich, DE

Session Co-Chair: Sharon Hu, University of Notre Dame, USA

Talks:

  • Using ontologies for dataset engineering in automotive AI applications”Martin Herrmann1, Christian Witt2, Laureen Lake1, Stefani Guneshka3, Christian Heinzemann1, Frank Bonarens4, Patrick Feifel4 and Simon Funke3, 1Robert Bosch GmbH, DE, 2Valeo Schalter und Sensoren GmbH, DE, 3Understand AI, DE, 4Stellantis, Opel Automobile GmbH, DE. 
  • “Using Formal Conformance Testing to Generate Scenarios for Autonomous Vehicles”, Jean-Baptiste Horel1,3, Christian Laugier1,3, Lina Marsso2, Radu Mateescu3, Lucie Muller3, Anshul Paigwar1,3, Alessandro Renzaglia1,3 and Wendelin Serwe3,  1Univ. Grenoble Alpes, FR, 2University of Toronto, CA, 3INRIA, FR.  
  • “Remote Sensing with UAV and Mobile Recharging Vehicle Rendezvous”, Michael Ostertag, Jason Ma and Tajana Rosing, University of California, San Diego, USA

Date: Friday, 18 March 2022 (online)

For a detailed program of the ASD Friday Interactive Day please visit: https://www.date-conference.com/workshop/w10

Chair: Chung-Wei Lin, National Taiwan University, Taiwan

Co-chair:  Sebastian Steinhorst, Technical University of Munich, DE

Opening Session

Time: 08:30-09:30 CET

8:30 – 8:45: Opening of ASD Friday Interactive Day, Introduction of Sponsors

8:45 – 9:30: Keynote: Trustworthy Autonomous Systems Development

Keynote Speaker: Joseph Sifakis, Verimag Laboratory

Session Chair: Rolf Ernst, TU Braunschweig, DE

Abstract: Autonomous systems emerge from the needs to automate existing organizations by progressive replacement of human operators by autonomous agents. Their development raises multi-faceted challenges, which go well beyond the limits of weak AI.

We attempt an analysis of the current state of art focusing on design and validation.   
First, we explain that existing approaches to agent design are unsatisfactory. Traditional model-based approaches are defeated by the complexity of the problem, while solutions based on end-to-end machine learning fail to provide the necessary trustworthiness guarantees. We advocate “hybrid design” solutions that take the best of each approach and seek tradeoffs between trustworthiness and performance. In addition, we argue that traditional case-by-case risk analysis and mitigation techniques are failing to scale, and we discuss the trend away from correctness at design time and toward reliance on runtime assurance techniques. 
Second, we explain that simulation and testing remain the only realistic approach for global validation, and we show how current methods and practices can be transposed to autonomous systems by identifying the technical requirements involved.

We conclude by discussing the factors that will play a decisive role in the acceptance of autonomous systems, and by arguing for the urgent need for new theoretical foundations.

Session 1: Human-Machine Systems

Time: 09:30-10:45 CET

Session Description: The technology of autonomous vehicles continues advancing, but there will be a long time before human-driven vehicles are completely replaced by fully-autonomous vehicles. Therefore, mixed traffic environments need to be taken care of to provide safe, efficient, and comfortable transportation. In this session, the experts will discuss the roles of autonomous vehicles, human-driven vehicles, and roadside units and share their visions on human-machine systems. The ultimate goal is to support a smooth transition from current transportation to autonomous transportation.

Session Chair: Chung-Wei Lin, National Taiwan University

Agenda of Talks and Speakers/Panelists:

  • 09:30 – 09:45: “Towards Cooperative Autonomous Vehicles for Mixed Traffic Environments”Shunsuke Aoki, National Institute of Informatics, JP
  • 09:45 – 10:00: “Automation as a Service Provider for Shared Mobility”, Meike Jipp, German Aerospace Center (DLR), DE
  • 10:00 – 10:15: “Human-Like Autonomous Driving and Human-Machine Systems”Chen Lv, Nanyang Technological University, SG
  • 10:15 – 10:45: Interactive panel discussion on Human-Machine Systems

Session 2: Hardware and Components

Time: 11:00-12:15 CET

Session Description: For the design of autonomous systems, powerful hardware and system components are as much a core enabler of advanced autonomy as the software running on them. With the increase in cognitive capabilities by integration of computation and sensing platforms, many opportunities as well as challenges arise, such that both hardware and AI-centric software can operate fully synergetic and, hence, reach their full potential. The purpose of this session is to discuss the latest trends in hardware components and their design aspects for efficient and holistic integration of computation, sensing and communication.

Session Chair: Sebastian Steinhorst, Technical University of Munich

Agenda of Talks and Speakers/Panelists:

  • 11:00 – 11:15: PULP: An Open Ultra Low-Power Platform for Autonomous Nano Drones”, Luca Benini, ETH Zürich, CH
  • 11:15 – 11:30: Challenges & Solutions for Next-Generation E/E Architectures”, Bart Vermeulen, NXP, NL
  • 11:30 – 11:45: Efficient Machine Learning: Algorithms-Circuits-Devices Co-design”, Hai (Helen) Li, Duke University, US
  • 11:45 – 12:15: Interactive panel discussion on Hardware and Components

Session 3: Panel Discussion “Autonomous Systems Design”

Time: 13:30-15:00 CET

Organizers:

  • David Harel, Weizmann Institute of Science
  • Joseph Sifakis, Verimag Laboratory

Moderator:

  • David Harel, Weizmann Institute of Science

Panelists:

  • Sandeep Neema, DARPA
  • Alberto Sangiovanni-Vincentelli, UC Berkeley
  • Carlo Ghezzi, Politecnico di Milano
  • Simon Burton, Fraunhofer
  • Michael Paulitsch, Intel
  • Arne Hamann, Bosch

Topics to be discussed by the panel:

1) What is your vision for AS? For example, the role of these systems in the IoT and AI revolutions; autonomy as a step from weak to strong AI; the gap between automated and autonomous systems.

 2) What challenges do you see in AS design? For example, AI-enabled end-to-end solutions; “hybrid design” approaches, integrating model- and data-driven components; systems engineering issues.

3) How should we ensure the reliability of AS? For example, achieving explainable AI; adapting and extending rigorous V&V techniques to ASs; ensuring safety based exclusively on simulation and testing.

4) Looking to the future, is the vision of total autonomy viable? how can we make it happen? For example, decisive factors for acceptance; research challenges; ethical issues; “easy” total autonomy categories.

Session 4: V2X, Edge Computing, and Connected Applications

Time: 15:10-16:50 CET

Session Description: Connectivity realizes many advanced applications for vehicles. Especially, the interactions between vehicles and edge servers (or roadside units) further boost the trend and involve more players in the business. In this session, the experts from an auto-maker, a supplier, a high-tech company, and a start-up will meet together, describe their roles in the connected and edge-computing environment, and discuss potential integration or competition.

Session Chair: Dirk Ziegenbein, Robert Bosch GmbH

Agenda of Talks and Speakers/Panelists:

  • 15:10 – 15:25: Video Uberization Using Edge AI and Mobile Video”Chun-Ting Chou, OmniEyes, TW
  • 15:25 – 15:40: Connected Applications as Driver for Automation”Frank Hofmann, Bosch, DE
  • 15:40 – 15:55: Environmental parity between cloud and embedded edge as a foundation for software-defined vehicles”Stefano Marzani, Amazon, US
  • 15:55 – 16:10: Mobility Digital Twin with Connected Vehicles and Cloud Computing”Ziran Wang, Toyota Motor North America R&D, US
  • 16:10 – 16:50: Interactive panel discussion on V2X, Edge Computing, and Connected Applications

Information from Call for Contributions

Areas of Interest

We welcome your contributions within the scope of the event. The main areas of interest include but are not limited to the following:

  • Concepts, Algorithms and Formal Methods for Autonomy
  • Platforms for Autonomous Systems
    •  Architectures (from Chip Level to System Level) for Autonomous Systems
    • Middleware and Frameworks (e.g. ROS) for Autonomous Systems
    • Models and HW/SW Mechanisms for Self-Awareness and Self-Adaptation
  • Design of Autonomous Systems
    • Mastering Emergent and Evolving Behavior (Goals, Constraints, …)
    • Design, Verification and Test of High-Assurance Learning-Enabled Systems
    • Design Automation and Methodologies for Autonomous Systems
  • Dependability and Trustworthy Autonomy
    • Functional Safety and Assurance Cases for Evolving and Learning-Enabled Systems
    • HW/SW Mechanisms for Resilient Systems
    • Autonomous Systems Security
  • Applications and Case Studies of Autonomous Systems

Types of Contributions

Conference Papers:

The special initiative features regular sessions for presenting novel technical contributions. Submitted papers will undergo a peer-review process and accepted papers will appear in the DATE conference proceedings. All manuscripts should be submitted in pdf format not exceeding 6 pages for standard oral presentation and 4 pages for interactive presentation, following the DATE submission instructions.

Abstract Submission:  12 September 2021

Full Paper Submission:  19 September 2021

Acceptance Notification:  11 November 2021 

Special Sessions / Panels:

We additionally solicit proposals for special sessions, panel discussions and invited talks, targeting emerging challenges in the field of autonomous systems, especially that are of interest to the DATE conference participants. Special Session proposals must consist of an extended summary of up to 1,500 words in a PDF file, describing the topic, the authors/speakers, and the intended format. Submission deadline is 21 September 2021.

Submission Instructions

All submissions should follow the DATE proceedings submission instructions.

Please submit your contribution at: https://www.softconf.com/date22/special_ASD/

Friday Interactive Day  Contributions 

The Friday Interactive Day will feature highly interactive sessions on emerging or controversial hot topics in the scope of the ASD initiative.

We also encourage sessions on business trends or public policies. The form of a session can be a special session with invited talks and panel discussions, leaving enough room for interaction among the participants. Sessions on industrial perspectives, prototypes and demos are also highly welcome. Please send the proposals via email with the subject #ASD2021 to the organizers Chung-Wei Lin (cwlin@csie.ntu.edu.tw) and Sebastian Steinhorst (sebastian.steinhorst@tum.de).

Thursday Chairs:

  • X. Sharon Hu, University of Notre Dame, USA
  • Selma Saidi, Technical University Dortmund, Germany

Friday Chairs:

  • Chung-Wei Lin, National Taiwan University, Taiwan
  • Sebastian Steinhorst, Technical University Munich, Germany

Organizing Committee:

  • Rolf Ernst, Technical University of Braunschweig, Germany
  • Dirk Ziegenbein, Robert Bosch GmbH, Germany
  • Selma Saidi, Technical University Dortmund, Germany
  • Sebastian Steinhorst, Technical University Munich, Germany
ASD 2021Videos: ASD 2021 Recordings

Videos: ASD 2021 Recordings

For the participants of the DATE ASD 2021 event, we are providing recordings of the scientific paper sessions, the ASD reception talk as well as of the Friday interactive day. Please refer to the session details of Thursday and Friday.

Session 10.1 – Reliable Autonomous Systems: Dealing with Failure & Anomalies

Session 11.1 – Safety Assurance of Autonomous Vehicles

Session 12.1 – Designing Autonomous Systems: Experiences, Technology and Processes

Session 13.1 – Predictable Perception for Autonomous Systems

ASD Reception

Friday Interactive Day

ASD 2021Video: ASD’21 Opening Panel Statements

Video: ASD’21 Opening Panel Statements

ASD’21 Opening Panel Statements

In this video edited for the opening panel of the Special Initiative on Autonomous Systems Design at DATE 2021, eminent scientists and industry leaders will talk about their visions of autonomous systems, the challenges they see in the development of autonomous systems as well as how autonomous systems will impact the business in their industries.

Speakers in the video:

Juergen Bortolazzi (Porsche AG)

Pascal Traverse (Airbus)

Thomas Kropf (Robert Bosch GmbH)

Peter Liggesmeyer (Fraunhofer IESE)

Joseph Sifakis (University of Grenoble/VERIMAG)

ASD 2021

DATE’21 Press Release: Special Initiative on Autonomous Systems Design “a great success”

Quoting from the DATE’21 final press release:

“Additionally, DATE 2021 hosted a two-day Special Initiative on Autonomous Systems Design which was a great success with more than 230 participants. The Initiative kicked off with statements of industrial leaders from Porsche, Bosch, and Airbus unanimously highlighting the tremendous importance of autonomous systems for the future of their industries. However, while current research into autonomous systems is mostly focused on improving systems functionality, there is a growing need for the related design processes, platforms and EDA, as addressed by the ASD Initiative. A poll showed that the industrial and academic participants were greatly satisfied by the way DATE has picked up that topic.”

Please see here for the complete press release: https://www.date-conference.com/date-2021-conference-date-community-gathered-virtually-large-numbers-another-unique-conference

ASD 2021

DATE’21 Special Initiative on Autonomous Systems Design (ASD)

A Two-Day Special Initiative

Start: Thursday, 4 February 2021 07:00

End: Friday, 5 February 2021 18:10

Organizers

  • Rolf Ernst, Technical University Braunschweig, Germany
  • Selma Saidi, Technische Universität Dortmund, Germany
  • Dirk Ziegenbein, Robert Bosch GmbH, Germany
  • Sebastian Steinhorst, Technical University of Munich, Germany
  • Jyotirmoy Deshmukh, University of Southern California, United States
  • Christian Laugier, INRIA Grenoble, France

Two-Day Special Initiative

Fueled by the progress of artificial intelligence, autonomous systems become more and more integral parts of many Internet of Things (IoT) and Cyber-Physical Systems (CPS) applications, such as automated driving, robotics, avionics and industrial automation. Autonomous systems are self-governed and self-adaptive systems that are designed to operate in an open and evolving environment that has not been completely defined at design time. This poses a unique challenge to the design and verification of dependable autonomous systems. The DATE Special Initiative on Autonomous Systems Design (ASD) on Thursday and Friday will include high-profile keynotes and panel discussions, as well as peer-reviewed papers, invited contributions and interactive sessions addressing these challenges.

The Thursday of the DATE Special Initiative on Autonomous Systems Design (ASD) will start with an opening session where industry leaders from Airbus, Porsche and Robert Bosch will talk about their visions of autonomous systems, the challenges they see in the development of autonomous systems as well as how autonomous systems will impact the business in their industries. These input will be discussed in an open floor panel with eminent speakers from academia. After the opening session, three sessions will present peer-reviewed papers on “Reliable Autonomous Systems: Dealing with Failure & Anomalies”, “Safety Assurance of Autonomous Vehicles” and “Designing Autonomous Systems: Experiences, Technology and Processes”. Furthermore, a special session will discuss latest research on “Predictable Perception for Autonomous Systems”.

The Friday Interactive Day of the DATE Special Initiative on Autonomous Systems Design (ASD) features keynotes from industry leaders as well as interactive discussions initiated by short presentations on several hot topics. Presentations from General Motors and BMW on predictable perception, as well as a session on dynamic risk assessment will fuel the discussion on how to maximize safety in a technically feasible manner. Speakers from TTTech and APEX.AI will present insights into Motionwise and ROS2 as platforms for automated vehicles. Further sessions will highlight topics such as explainable machine learning, self-adaptation for robustness and self-awareness for autonomy, as well as cybersecurity for connected vehicles.

Autonomous Systems Design (ASD) Thursday Sessions

Detailed Program: Autonomous Systems Design (ASD) Thursday Sessions at DATE’21

  • 07:00 – 08:00 9.1 Autonomous Systems Design: Opening Panel
  • 08:00 – 09:00 10.1 Reliable Autonomous Systems: Dealing with Failure & Anomalies
  • 09:00 – 09:30 IP.ASD_1 Interactive Presentations
  • 09:30 – 10:30 11.1 Safety Assurance of Autonomous Vehicles
  • 15:00 – 15:50 K.5 Keynote AUTONOMY: ONE STEP BEYOND ON COMMERCIAL AVIATION by Pascal Traverse, Airbus, FR
  • 16:00 – 17:00 12.1 Designing Autonomous Systems: Experiences, Technology and Processes
  • 17:00 – 17:30 IP.ASD_2 Interactive Presentations
  • 17:30 – 18:30 13.1 Predictable Perception for Autonomous Systems
  • 18:30 – 19:00 ASD Reception

ASD Friday Interactive Day Sessions

Detailed Program: Friday Interactive Day of the Special Initiative on Autonomous Systems Design (ASD) at DATE’21

  • 08:30 – 09:00 Opening & Introduction
  • 09:00 – 10:00 Dynamic Risk Assessment in Autonomous Systems
  • 10:00 – 11:00 Cybersecurity for Connected Autonomous Vehicles
  • 11:00 – 12:00 Self-adaptive safety- and mission-critical CPS: wishful thinking or absolute necessity? 
  • 14:00 – 15:00 Predictable Perception
  • 15:00 – 16:00 Perspicuous Computing
  • 16:00 – 17:00 Production Architectures & Platforms for Automated Vehicles
  • 17:00 – 18:00 Self-Awareness for Autonomy

Poll Results

http://www.polljunkie.com/poll/qytorw/date-2021-special-initiative-on-autonomous-systems-design-poll/view

Technical Program Committee

  • Houssam Abbas, Oregon State University, USA
  • Rasmus Adler, Fraunhofer IESE, Germany
  • Eric Armengaud, AVL, Germany
  • Bart Besselink, University of Groningen, Netherlands
  • Philippe Bonnifait, UTC Compiegne, France
  • Paolo Burgio, Università degli Studi di Modena e Reggio Emilia, Italy
  • Arvind Easwaran, Nanyang Technological University, Singapore
  • Sebastian Fischmeister, University of Waterloo, Canada
  • Roman Gansch, Robert Bosch GmbH, Germany
  • Sabine Glesner, TU Berlin, Germany
  • Dominique Gruyer, Université Gustave Eiffel, France
  • Mohammad Hamad, Technical University Munich, Germany
  • Xiaoging Jin, Apple, USA
  • Martina Maggio, Saarland University, Germany
  • Philipp Mundhenk, AUDI, Germany
  • Alessandra Nardi, Cadence, USA
  • Gabor Orosz, University of Michigan, USA
  • Claire Pagetti, Onera, France
  • Daniele Palossi, ETH Zurich, Switzerland
  • Alessandro Papadopoulos, Mälardalen University, Sweden
  • Alessandro Renzaglia, INRIA, France
  • Shreejith Shanker, Trinity College Dublin, Ireland
  • Dongwha Shin, Soongsil University, Korea
  • Aviral Shrivastava, Arizona State University, USA
  • Andrei Terechko, NXP Semiconductors, Netherlands
  • Lin Xue, Northeastern University, USA
ASD 2021

Thursday Sessions of the Special Initiative on Autonomous Systems Design (ASD) at DATE’21

9.1 Autonomous Systems Design: Opening Panel

Date: Thursday, 04 February 2021
Time: 07:00 – 08:00 CET
Virtual Conference Room: https://virtual21.date-conference.com/meetings/virtual/ZjsTrqcyv7ocgh8y9

Session chair:
Rolf Ernst, TU Braunschweig, DE

Session co-chair:
Selma Saidi, TU Dortmund, DE

Organizers:
Rolf Ernst, TU Braunschweig, DE
Selma Saidi, TU Dortmund, DE

Fueled by the progress of artificial intelligence, autonomous systems become more and more integral parts of many Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) applications, such as automated driving, robotics, avionics and industrial automation. Autonomous systems are self-governed and self-adaptive systems that are designed to operate in an open and evolving environment that has not been completely defined at design time. This poses a unique challenge to the design and verification of dependable autonomous systems. In this opening session of the DATE Special Initiative on Autonomous Systems Design, industry leaders will talk about their visions of autonomous systems, the challenges they see in the development of autonomous systems as well as how autonomous systems will impact the business in their industries. These inputs will be discussed in an open floor panel. Panelists: – Thomas Kropf (Robert Bosch GmbH) – Pascal Traverse (Airbus) – Juergen Bortolazzi (Porsche AG) – Peter Liggesmeyer (Fraunhofer IESE) – Joseph Sifakis (University of Grenoble/VERIMAG) – Sandeep Neema (DARPA)

10.1 Reliable Autonomous Systems: Dealing with Failure & Anomalies

Date: Thursday, 04 February 2021
Time: 08:00 – 09:00 CET
Virtual Conference Room: https://virtual21.date-conference.com/meetings/virtual/Gz6kTsdb9rNGixqMQ

Session chair:
Rolf Ernst, TU. Braunschweig, DE

Session co-chair:
Rasmus Adler, Fraunhofer IESE, DE

Organizers:
Rolf Ernst, TU. Braunschweig, DE
Selma Saidi, TU Dortmund, DE

Autonomous Systems need novel approaches to detect and handle failures and anomalies. The first paper introduces an approach that adapts the placement of applications on a vehicle platform via adjustment of optimization criteria under safety goal restrictions. The second paper presents a formal worst-case failover timing analysis for online verification to assure safe vehicle operation under failover safety constraints. The third paper proposes an explanation component that observes and analyses an autonomous system and tries to derive explanations for anomalous behavior.

TimeLabelPresentation Title
Authors
08:00 CET10.1.1C-PO: A CONTEXT-BASED APPLICATION-PLACEMENT OPTIMIZATION FOR AUTONOMOUS VEHICLES
Speaker:
Tobias Kain, Volkswagen AG, Wolfsburg, Germany, DE
Authors:
Tobias Kain1, Hans Tompits2, Timo Frederik Horeis3, Johannes Heinrich3, Julian-Steffen Müller4, Fabian Plinke3, Hendrik Decke5 and Marcel Aguirre Mehlhorn4
1Volkswagen AG, AT; 2Technische Universitat Wien, AT; 3Institut für Qualitäts- und Zuverlässigkeitsmanagement GmbH, DE; 4Volkswagen AG, DE; 5Volkswagen AG, Wolfsburg, DE
Abstract
Autonomous vehicles are complex distributed systems consisting of multiple software applications and computing nodes. Determining the assignment between these software applications and computing nodes is known as the application-placement problem. The input of this problem is a set of applications, their requirements, a set of computing nodes, and their provided resources. Due to the potentially large solution space of the problem, an optimization goal defines which solution is desired the most. However, the optimization goal used for the application-placement problem is not static but has to be adapted according to the current context the vehicle is experiencing. Therefore, an approach for a context-based determination of the optimization goal for a given instance of an application-placement problem is required. In this paper, we introduce C-PO, an approach to address this issue. C-PO ensures that if the safety level of a system drops due to an occurring failure, the optimization goal for the successively executed application-placement determination aims to restore the safety level. Once the highest safety level is reached, C-PO optimizes the application placement according to the current driving situation. Furthermore, we introduce two methods for dynamically determining the required level of safety.
08:15 CET10.1.2WORST-CASE FAILOVER TIMING ANALYSIS OF DISTRIBUTED FAIL-OPERATIONAL AUTOMOTIVE APPLICATIONS
Speaker:
Philipp Weiss, TU Munich, DE
Authors:
Philipp Weiss1, Sherif Elsabbahy1, Andreas Weichslgartner2 and Sebastian Steinhorst1
1TU Munich, DE; 2AUDI AG, DE
Abstract
Enabling fail-operational behavior of safety-critical software is essential to achieve autonomous driving. At the same time, automotive vendors have to regularly deliver over-the-air software updates. Here, the challenge is to enable a flexible and dynamic system behavior while offering, at the same time, a predictable and deterministic behavior of time-critical software. Thus, it is necessary to verify that timing constraints can be met even during failover scenarios. For this purpose, we present a formal analysis to derive the worst-case application failover time. Without such an automated worst-case failover timing analysis, it would not be possible to enable a dynamic behavior of safetycritical software within safe bounds. We support our formal analysis by conducting experiments on a hardware platform using a distributed fail-operational neural network. Our randomly generated worst-case results are as close as 6.0% below our analytically derived exact bound. Overall, our presented worstcase failover timing analysis allows to conduct an automated analysis at run-time to verify that the system operates within the bounds of the failover timing constraint such that a dynamic and safe behavior of autonomous systems can be ensured.
08:30 CETIP.ASD_1.1DECENTRALIZED AUTONOMOUS ARCHITECTURE FOR RESILIENT CYBER-PHYSICAL PRODUCTION SYSTEMS
Speaker:
Laurin Prenzel, TU Munich, DE
Authors:
Laurin Prenzel and Sebastian Steinhorst, TU Munich, DE
Abstract
Real-time decision-making is a key element in the transition from Reconfigurable Manufacturing Systems to Autonomous Manufacturing Systems. In Cyber-Physical Production Systems (CPPS) and Cloud Manufacturing, most decision-making algorithms are either centralized, creating vulnerabilities to failures, or decentralized, struggling to reach the performance of the centralized counterparts. In this paper, we combine the performance of centralized optimization algorithms with the resilience of a decentralized consensus. We propose a novel autonomous system architecture for CPPS featuring an automatic production plan generation, a functional validation, and a two-stage consensus algorithm, combining a majority vote on safety and optimality, and a unanimous vote on feasibility and authenticity. The architecture is implemented in a simulation framework. In a case study, we exhibit the timing behavior of the configuration procedure and subsequent reconfiguration following a device failure, showing the feasibility of a consensus-based decision-making process.
08:31 CET10.1.3ANOMALY DETECTION AND CLASSIFICATION TO ENABLE SELF-EXPLAINABILITY OF AUTONOMOUS SYSTEMS
Speaker:
Verena Klös, TU Berlin, DE
Authors:
Florian Ziesche, Verena Klös and Sabine Glesner, TU Berlin, DE
Abstract
While the importance of autonomous systems in our daily lives and in the industry increases, we have to ensure that this development is accepted by their users. A crucial factor for a successful cooperation between humans and autonomous systems is a basic understanding that allows users to anticipate the behavior of the systems. Due to their complexity, complete understanding is neither achievable, nor desirable. Instead, we propose self-explainability as a solution. A self-explainable system autonomously explains behavior that differs from anticipated behavior. As a first step towards this vision, we present an approach for detecting anomalous behavior that requires an explanation and for reducing the huge search space of possible reasons for this behavior by classifying it into classes with similar reasons. We envision our approach to be part of an explanation component that can be added to any autonomous system.
08:46 CETIP.ASD_1.2PROVABLY ROBUST MONITORING OF NEURON ACTIVATION PATTERNS
Speaker and Author:
Chih-Hong Cheng, DENSO AUTOMOTIVE Deutschland GmbH, DE
Abstract
For deep neural networks (DNNs) to be used in safety-critical autonomous driving tasks, it is desirable to monitor in operation time if the input for the DNN is similar to the data used in DNN training. While recent results in monitoring DNN activation patterns provide a sound guarantee due to building an abstraction out of the training data set, reducing false positives due to slight input perturbation has been an issue towards successfully adapting the techniques. We address this challenge by integrating formal symbolic reasoning inside the monitor construction process. The algorithm performs a sound worstcase estimate of neuron values with inputs (or features) subject to perturbation, before the abstraction function is applied to build the monitor. The provable robustness is further generalized to cases where monitoring a single neuron can use more than one bit, implying that one can record activation patterns with a finegrained decision on the neuron value interval.

IP.ASD_1 Interactive Presentations

Date: Thursday, 04 February 2021
Time: 09:00 – 09:30 CET
Virtual Conference Room: https://virtual21.date-conference.com/meetings/virtual/DoZTZhX3vc3YiysKh

Interactive Presentations run simultaneously during a 30-minute slot. Additionally, each IP paper is briefly introduced in a one-minute presentation in a corresponding regular session

TimeLabel
IP.ASD_1.1DECENTRALIZED AUTONOMOUS ARCHITECTURE FOR RESILIENT CYBER-PHYSICAL PRODUCTION SYSTEMS
Speaker:
Laurin Prenzel, TU Munich, DE
Authors:
Laurin Prenzel and Sebastian Steinhorst, TU Munich, DE
Abstract
Real-time decision-making is a key element in the transition from Reconfigurable Manufacturing Systems to Autonomous Manufacturing Systems. In Cyber-Physical Production Systems (CPPS) and Cloud Manufacturing, most decision-making algorithms are either centralized, creating vulnerabilities to failures, or decentralized, struggling to reach the performance of the centralized counterparts. In this paper, we combine the performance of centralized optimization algorithms with the resilience of a decentralized consensus. We propose a novel autonomous system architecture for CPPS featuring an automatic production plan generation, a functional validation, and a two-stage consensus algorithm, combining a majority vote on safety and optimality, and a unanimous vote on feasibility and authenticity. The architecture is implemented in a simulation framework. In a case study, we exhibit the timing behavior of the configuration procedure and subsequent reconfiguration following a device failure, showing the feasibility of a consensus-based decision-making process.
IP.ASD_1.2PROVABLY ROBUST MONITORING OF NEURON ACTIVATION PATTERNS
Speaker and Author:
Chih-Hong Cheng, DENSO AUTOMOTIVE Deutschland GmbH, DE
Abstract
For deep neural networks (DNNs) to be used in safety-critical autonomous driving tasks, it is desirable to monitor in operation time if the input for the DNN is similar to the data used in DNN training. While recent results in monitoring DNN activation patterns provide a sound guarantee due to building an abstraction out of the training data set, reducing false positives due to slight input perturbation has been an issue towards successfully adapting the techniques. We address this challenge by integrating formal symbolic reasoning inside the monitor construction process. The algorithm performs a sound worstcase estimate of neuron values with inputs (or features) subject to perturbation, before the abstraction function is applied to build the monitor. The provable robustness is further generalized to cases where monitoring a single neuron can use more than one bit, implying that one can record activation patterns with a finegrained decision on the neuron value interval.

11.1 Safety Assurance of Autonomous Vehicles

Date: Thursday, 04 February 2021
Time: 09:30 – 10:30 CET
Virtual Conference Room: https://virtual21.date-conference.com/meetings/virtual/WymRmxKbnn387f8Di

Session chair:
Sebastian Steinhorst, TU Munich, DE

Session co-chair:
Simon Schliecker, Volkswagen AG, DE

Organizers:
Rolf Ernst, TU. Braunschweig, DE
Selma Saidi, TU Dortmund, DE

Safety of autonomous vehicles is a core requirement for their social acceptance. Hence, this session introduces three technical perspectives on this important field. The first paper presents a statistics-based view on risk taking when passing by pedestrians such that automated decisions can be taken with probabilistic reasoning. The second paper proposes a hardening of image classification for highly automated driving scenarios by identifying the similarity between target classes. The third paper improves the computational efficiency of safety verification of deep neural networks by reusing existing proof artifacts.

TimeLabelPresentation Title
Authors
09:30 CET11.1.1AUTOMATED DRIVING SAFETY – THE ART OF CONSCIOUS RISK TAKING – MINIMUM LATERAL DISTANCES TO PEDESTRIANS
Speaker:
Bert Böddeker, private, DE
Authors:
Bert Böddeker1, Wilhard von Wendorff2, Nam Nguyen3, Peter Diehl4, Roland Meertens5 and Rolf Johannson6
1private, DE; 2SGS-TÜV Saar GmbH, DE; 3Hochschule München für angewandte Wissenschaften, DE; 4Private, DE; 5private, NL; 6Private, SE
Abstract
The announced release dates for Automated Driving Systems (ADS) with conditional (SAE-L3) and high (SAE-L4) levels of automation according to SAE J3016 are getting closer. Still, there is no established state of the art for proving the safety of these systems. The ISO 26262 for automotive functional safety is still valid for these systems but only covers risks from malfunctions of electric and electronic (E/E) systems. A framework for considering issues caused by weaknesses of the intended functionality itself is standardized in the upcoming release of the ISO 21448 – Safety of the Intended Functionality (SOTIF). Rich experience regarding limitations of safety performance of complex sensors can be found in this standard. In this paper, we highlight another aspect of SOTIF that becomes important for higher levels of automation, especially, in urban areas: `conscious risk taking’. In traditional automotive systems, conflicting goal resolutions are generally left to the car driver. With SAE-level 3 or at latest SAE-level 4 ADS, the driver is not available for decisions anymore. Even ‘safe drivers’ do not use the safest possible driving behavior. In the example of occlusions next to the street, a driver balances the risk of occluded pedestrians against the speed of the traffic flow. Our aim is to make such decisions explicit and sufficiently safe. On the example of crossing pedestrians, we show how to use statistics to derive a conscious quantitative risk-based decision from a previously defined acceptance criterion. The acceptance criterion is derived from accident statistics involving pedestrians.
09:45 CET11.1.2ON SAFETY ASSURANCE CASE FOR DEEP LEARNING BASED IMAGE CLASSIFICATION IN HIGHLY AUTOMATED DRIVING
Speaker:
Himanshu Agarwal, HELLA GmbH & Co. KGaA, Lippstadt, Germany and Carl von Ossietzky University Oldenburg, Germany, DE
Authors:
Himanshu Agarwal1, Rafal Dorociak2 and Achim Rettberg3
1HELLA GmbH & Co. KGaA and Carl von Ossietzky University Oldenburg, DE; 2HELLA GmbH & Co. KGaA, DE; 3University of Applied Science Hamm-Lippstadt & University Oldenburg, DE
Abstract
Assessing the overall accuracy of deep learning classifier is not a sufficient criterion to argue for safety of classification based functions in highly automated driving. The causes of deviation from the intended functionality must also be rigorously assessed. In context of functions related to image classification, one of the causes can be the failure to take into account during implementation the classifier’s vulnerability to misclassification due to high similarity between the target classes. In this paper, we emphasize that while developing the safety assurance case for such functions, the argumentation over the appropriate implementation of the functionality must also address the vulnerability to misclassification due to class similarities. Using the traffic sign classification function as our case study, we propose to aid the development of its argumentation by: (a) conducting a systematic investigation of the similarity between the target classes, (b) assigning a corresponding classifier vulnerability rating to every possible misclassification, and (c) ensuring that the claims against the misclassifications that induce higher risk (scored on the basis of vulnerability and severity) are supported with more compelling sub-goals and evidences as compared to the claims against misclassifications that induce lower risk.
10:00 CET11.1.3CONTINUOUS SAFETY VERIFICATION OF NEURAL NETWORKS
Speaker:
Rongjie Yan, Institute of Software, Chinese Academy of Sciences, CN
Authors:
Chih-Hong Cheng1 and Rongjie Yan2
1DENSO AUTOMOTIVE Deutschland GmbH, Eching, Germa, DE; 2Institute of Software, Chinese Academy of Sciences, CN
Abstract
Deploying deep neural networks (DNNs) as core functions in autonomous driving creates unique verification and validation challenges. In particular, the continuous engineering paradigm of gradually perfecting a DNN-based perception can make the previously established result of safety verification no longer valid. This can occur either due to the newly encountered examples (i.e., input domain enlargement) inside the Operational Design Domain or due to the subsequent parameter fine-tuning activities of a DNN. This paper considers approaches to transfer results established in the previous DNN safety verification problem to the modified problem setting. By considering the reuse of state abstractions, network abstractions, and Lipschitz constants, we develop several sufficient conditions that only require formally analyzing a small part of the DNN in the new problem. The overall concept is evaluated in a 1/10-scaled vehicle that equips a DNN controller to determine the visual waypoint from the perceived image.

K.5 Keynote – Special day on ASD

Date: Thursday, 04 February 2021
Time: 15:00 – 15:50 CET
Virtual Conference Room: https://virtual21.date-conference.com/meetings/virtual/Rv6DcWWGXHvRDApRS

Session chair:
Selma Saidi, TU Dortmund, DE

Session co-chair:
Rolf Ernst, TU. Braunschweig, DE

Autonomy is in the air: on one hand, automation is clearly a lever to improve safety margins; on another hand technologies are maturing, pulled by the automotive market. In this context, Airbus is building a concept airplane from a blank sheet with the objective to improve human-machine teaming for better overall performance. Foundation of this new concept is that when they are made aware of the “big picture” with enough time to analyze it, humans are still the best to make strategic decisions. Autonomy technologies are the main enabler of this concept. Benefit are expected both in a two-crew cockpit and eventually in Single Pilot Operations. Bio: Pascal Traverse is General Manager for the Autonomy “fast track” at Airbus. Autonomy is a top technical focus area for Airbus. The General Manager creates a vision, coordinates R&T activities with the objective to accelerate the increase of knowledge in Airbus. Before his nomination last year, Pascal was coordinating Airbus Commercial R&T activities related to the cockpit and flight operations. Earlier in his carrier, Pascal participated in the A320/A330/A340/A380 Fly-by-Wire developments, certification harmonization with FAA and EASA, management of Airbus safety activities and even of qualities activities in the A380 Final Assembly Line. Pascal has Master and Doctorate’s degrees in embedded systems from N7, conducted research in LAAS and UCLA and is a 3AF Fellow.

TimeLabelPresentation Title
Authors
15:00 CETK.5.1AUTONOMY: ONE STEP BEYOND ON COMMERCIAL AVIATION
Speaker and Author:
Pascal Traverse, Airbus, FR
Abstract
Autonomy is in the air: on one hand, automation is clearly a lever to improve safety margins; on another hand technologies are maturing, pulled by the automotive market. In this context, Airbus is building a concept airplane from a blank sheet with the objective to improve human-machine teaming for better overall performance. Foundation of this new concept is that when they are made aware of the “big picture” with enough time to analyze it, humans are still the best to make strategic decisions. Autonomy technologies are the main enabler of this concept. Benefit are expected both in a two-crew cockpit and eventually in Single Pilot Operations.

12.1 Designing Autonomous Systems: Experiences, Technology and Processes

Date: Thursday, 04 February 2021
Time: 16:00 – 17:00 CET
Virtual Conference Room: https://virtual21.date-conference.com/meetings/virtual/J65mDxoTBDp4Cn857

Session chair:
Selma Saidi, TU Dortmund, DE

Session co-chair:
Philipp Mundhenk, Robert Bosch GmbH, DE

Organizers:
Rolf Ernst, TU. Braunschweig, DE
Selma Saidi, TU Dortmund, DE

This session discusses technology innovation, experiences and processes in building autonomous systems. The first paper presents Fünfliber, a nano-sized Unmanned Aerial Vehicle (UAV) composed of a mudular open-hardware robotic platform controlled by a Parallel Ultra-low power system-on-chip (PULP) capable of running sophisticated autonomous DNN-based navigation workloads. The second paper presents an abstracted runtime for managing adaptation and integrating FPGA-accelerators to autonomous software framework , a show-case study integration into ROS is demonstrated. The third paper discusses current processes in engineering dependable collaborative autonomous systems and new buisness models based on agile approaches for innovative management.

TimeLabelPresentation Title
Authors
16:00 CET12.1.1FüNFLIBER-DRONE: A MODULAR OPEN-PLATFORM 18-GRAMS AUTONOMOUS NANO-DRONE
Speaker:
Hanna Müller, Integrated Systems Laboratory, CH
Authors:
Hanna Mueller1, Daniele Palossi2, Stefan Mach1, Francesco Conti3 and Luca Benini4
1Integrated Systems Laboratory – ETH Zurich, CH; 2Integrated Systems Laboratory – ETH Zurich, Switzerland, Dalle Molle Institute for Artificial Intelligence – University of Lugano and SUPSI, CH; 3Department of Electrical, Electronic and Information Engineering – University of Bologna, Italy, IT; 4Integrated Systems Laboratory – ETH Zurich, Department of Electrical, Electronic and Information Engineering – University of Bologna, CH
Abstract
“Miniaturizing an autonomous robot is a challenging task – not only the mechanical but also the electrical components have to operate within limited space, payload, and power. Furthermore, the algorithms for autonomous navigation, such as state-of-the-art (SoA) visual navigation deep neural networks (DNNs), are becoming increasingly complex, striving for more flexibility and agility. In this work, we present a sensor-rich, modular, nano-sized Unmanned Aerial Vehicle (UAV), almost as small as a five Swiss Franc coin – called Funfliber – with a ¨ total weight of 18g and 7.2cm in diameter. We conceived our UAV as an open-source hardware robotic platform, controlled by a parallel ultra-low power (PULP) system-on-chip (SoC) with a wide set of onboard sensors, including three cameras (i.e., infrared, optical flow, and standard QVGA), multiple Time-of-Flight (ToF) sensors, a barometer, and an inertial measurement unit. Our system runs the tasks necessary for a flight controller (sensor acquisition, state estimation, and low-level control), requiring only 10% of the computational resources available aboard, consuming only 9mW – 13x less than an equivalent Cortex M4-based system. Pushing our system at its limit, we can use the remaining onboard computational power for sophisticated autonomous navigation workloads, as we showcase with an SoA DNN running at up to 18Hz, with a total electronics’ power consumption of 271mW”
16:15 CET12.1.2RUNTIME ABSTRACTION FOR AUTONOMOUS ADAPTIVE SYSTEMS ON RECONFIGURABLE HARDWARE
Speaker:
Alex R Bucknall, University of Warwick, GB
Authors:
Alex R. Bucknall1 and Suhaib A. Fahmy2
1University of Warwick, GB; 2KAUST, SA
Abstract
Autonomous systems increasingly rely on on-board computation to avoid the latency overheads of offloading to more powerful remote computing. This requires the integration of hardware accelerators to handle the complex computations demanded by data-intensive sensors. FPGAs offer hardware acceleration with ample flexibility and interfacing capabilities when paired with general purpose processors, with the ability to reconfigure at runtime using partial reconfiguration (PR). Managing dynamic hardware is complex and has been left to designers to address in an ad-hoc manner, without first-class integration in autonomous software frameworks. This paper presents an abstracted runtime for managing adaptation of FPGA accelerators, including PR and parametric changes, that presents as a typical interface used in autonomous software systems. We present a demonstration using the Robot Operating System (ROS), showing negligible latency overhead as a result of the abstraction.
16:30 CETIP.ASD_2.1SYSTEMS ENGINEERING ROADMAP FOR DEPENDABLE AUTONOMOUS CYBER-PHYSICAL SYSTEMS
Speaker and Author:
Rasmus Adler, Fraunhofer IESE, DE
Abstract
Autonomous cyber-physical systems have enormous potential to make our lives more sustainable, more comfortable, and more economical. Artificial Intelligence and connectivity enable autonomous behavior, but often stand in the way of market launch. Traditional engineering techniques are no longer sufficient to achieve the desired dependability; current legal and normative regulations are inappropriate or insufficient. This paper discusses these issues, proposes advanced systems engineering to overcome these issues, and provides a roadmap by structuring fields of action.
16:31 CET12.1.3DDI: A NOVEL TECHNOLOGY AND INNOVATION MODEL FOR DEPENDABLE, COLLABORATIVE AND AUTONOMOUS SYSTEMS
Speaker:
Eric Armengaud, Armengaud Innovate GmbH, AT
Authors:
Eric Armengaud1, Daniel Schneider2, Jan Reich2, Ioannis Sorokos2, Yiannis Papadopoulos3, Marc Zeller4, Gilbert Regan5, Georg Macher6, Omar Veledar7, Stefan Thalmann8 and Sohag Kabir9
1Armengaud Innovate GmbH, AT; 2Fraunhofer IESE, DE; 3University of Hull, GB; 4Siemens AG, DE; 5Lero @DKIT, IE; 6Graz University of Technology, AT; 7AVL List GmbH, AT; 8University of Graz, AT; 9University of Bradford, GB
Abstract
Digital transformation fundamentally changes established practices in public and private sector. Hence, it represents an opportunity to improve the value creation processes (e.g., “industry 4.0”) and to rethink how to address customers’ needs such as “data-driven business models” and “Mobility-as-a-Service”. Dependable, collaborative and autonomous systems are playing a central role in this transformation process. Furthermore, the emergence of data-driven approaches combined with autonomous systems will lead to new business models and market dynamics. Innovative approaches to reorganise the value creation ecosystem, to enable distributed engineering of dependable systems and to answer urgent questions such as liability will be required. Consequently, digital transformation requires a comprehensive multi-stakeholder approach which properly balances technology, ecosystem and business innovation. Targets of this paper are (a) to introduce digital transformation and the role of / opportunities provided by autonomous systems, (b) to introduce Digital Depednability Identities (DDI) – a technology for dependability engineering of collaborative, autonomous CPS, and (c) to propose an appropriate agile approach for innovation management based on business model innovation and co-entrepreneurship

IP.ASD_2 Interactive Presentations

Date: Thursday, 04 February 2021
Time: 17:00 – 17:30 CET
Virtual Conference Room: https://virtual21.date-conference.com/meetings/virtual/nM6kYLXg8nwB5C4un

Interactive Presentations run simultaneously during a 30-minute slot. Additionally, each IP paper is briefly introduced in a one-minute presentation in a corresponding regular session

TimeLabelPresentation Title
Authors
17:00 CETIP.ASD_2.1SYSTEMS ENGINEERING ROADMAP FOR DEPENDABLE AUTONOMOUS CYBER-PHYSICAL SYSTEMS
Speaker and Author:
Rasmus Adler, Fraunhofer IESE, DE
Abstract
Autonomous cyber-physical systems have enormous potential to make our lives more sustainable, more comfortable, and more economical. Artificial Intelligence and connectivity enable autonomous behavior, but often stand in the way of market launch. Traditional engineering techniques are no longer sufficient to achieve the desired dependability; current legal and normative regulations are inappropriate or insufficient. This paper discusses these issues, proposes advanced systems engineering to overcome these issues, and provides a roadmap by structuring fields of action.

13.1 Predictable Perception for Autonomous Systems

Date: Thursday, 04 February 2021
Time: 17:30 – 18:30 CET
Virtual Conference Room: https://virtual21.date-conference.com/meetings/virtual/HEQdxwdJ4stmTYZMC

Session chair:
Soheil Samii, General Motors R&D,, US

Session co-chair:
Qing Rao, BMW AG, DE

Organizers:
Selma Saidi, TU Dortmund, DE
Rolf Ernst, TU Braunschweig, DE

Modern autonomous systems – such as autonomous vehicles or robots – consist of two major components: (a) the decision making unit, which is often made up of one or more feedback control loops, and (b) a perception unit that feeds the environmental state to the control unit and is made up of camera, radar and lidar sensors and their associated processing algorithms and infrastructure. While there has been a lot of work on the formal verification of the decision making (or the control) unit, the ultimate correctness of the autonomous system also heavily relies on the behavior of the perception unit. The verification of the correctness of the perception unit is however significantly more challenging and not much progress has been made here. This is because the algorithms used by perception units now increasingly rely on machine learning techniques (like deep neural networks) that run on a complex hardware made up CPU+accelerator platforms. The accelerators are made up of GPUs, TPUs and FPGAs. This combination of algorithmic + implementation platform complexity and heterogeneity currently makes it very difficult to provide either functional or timing correctness guarantees of the perception unit, while both of these guarantees are needed to ensure the correct functioning of the control loop and the overall autonomous system. This is a part of the overall challenge of verifying the correctness of autonomous systems. This session will feature four invited talks, with each of them focusing on different aspects of this problem – some on the timing predictability of the perception unit, others on the functional correctness of the processing algorithms used in the perception units, and the remaining on the reliability and the performance/cost tradeoffs involved in designing perception units for autonomous systems.

TimeLabelPresentation Title
Authors
17:30 CET13.1.1TIMING-PREDICTABLE VISION PROCESSINGFOR AUTONOMOUS SYSTEMS
Speaker:
Tanya Amert, UNC Chapel Hill, US
Authors:
Tanya Amert1, Michael Balszun2, Martin Geier3, F. Donelson Smith4, Jim Anderson4 and Samarjit Chakraborty5
1University of North Carolina at Chapel HIll, US; 2TU-München, DE; 3TU Munich, DE; 4University of North Carolina at Chapel Hill, US; 5UNC Chapel Hill, US
Abstract
Vision processing for autonomous systems today involves implementing machine learning algorithms and vision processing libraries on embedded platforms consisting of CPUs, GPUs and FPGAs. Because many of these use closed-source proprietary components, it is very difficult to perform any timing analysis on them. Even measuring or tracing their timing behavior is challenging, although it is the first step towards reasoning about the impact of different algorithmic and implementation choices on the end-to-end timing of the vision processing pipeline. In this paper we discuss some recent progress in developing tracing, measurement and analysis infrastructure for determining the timing behavior of vision processing pipelines implemented on state-of-the-art FPGA and GPU platforms.
17:45 CET13.1.2BOUNDING PERCEPTION NEURAL NETWORK UNCERTAINTY FOR SAFE CONTROL OF AUTONOMOUS SYSTEMS
Speaker:
Qi Zhu, Northwestern University, US
Authors:
Zhilu Wang1, Chao Huang2, Yixuan Wang1, Clara Hobbs3, Samarjit Chakraborty4 and Qi Zhu1
1Northwestern University, US; 2Department of Electrical and Computer Engineering, Northwestern University, US; 3University of North Carolina at Chapel Hill, US; 4UNC Chapel Hill, US
Abstract
Future autonomous systems will rely on advanced sensors and deep neural networks for perceiving the environment, and then utilize the perceived information for system planning, control, adaptation, and general decision making. However, due to the inherent uncertainties from the dynamic environment and the lack of methodologies for predicting neural network behavior, the perception modules in autonomous systems often could not provide deterministic guarantees and may sometimes lead the system into unsafe states (e.g., as evident by a number of high-profile accidents with experimental autonomous vehicles). This has significantly impeded the broader application of machine learning techniques, particularly those based on deep neural networks, in safetycritical systems. In this paper, we will discuss these challenges, define open research problems, and introduce our recent work in developing formal methods for quantitatively bounding the output uncertainty of perception neural networks with respect to input perturbations, and leveraging such bounds to formally ensure the safety of system control. Unlike most existing works that only focus on either the perception module or the control module, our approach provides a holistic end-to-end framework that bounds the perception uncertainty and addresses its impact on control.
18:00 CET13.1.3HARDWARE- AND SITUATION-AWARE SENSING FOR ROBUST CLOSED-LOOP CONTROL SYSTEMS
Speaker:
Dip Goswami, Eindhoven University of Technology, NL
Authors:
Sayandip De1, Yingkai Huang2, Sajid Mohamed1, Dip Goswami1 and Henk Corporaal3
1Eindhoven University of Technology, NL; 2Electronic Systems Group, Eindhoven University of Technology, NL; 3TU/e (Eindhoven University of Technology), NL
Abstract
While vision is an attractive alternative to many sensors targeting closed-loop controllers, it comes with high time-varying workload and robustness issues when targeted to edge devices with limited energy, memory and computing resources. Replacing classical vision processing pipelines, e.g., lane detection using Sobel filter, with deep learning algorithms is a way to deal with the robustness issues while hardware-efficient implementation is crucial for their adaptation for safe closed-loop systems. However, while implemented on an embedded edge device, the performance of these algorithms highly depends on their mapping on the target hardware and situation encountered by the system. That is, first, the timing performance numbers (e.g., latency, throughput) depends on the algorithm schedule, i.e., what part of the AI workload runs where (e.g., GPU, CPU) and their invocation frequency (e.g., how frequently we run a classifier). Second, the perception performance (e.g., detection accuracy) is heavily influenced by the situation – e.g., snowy and sunny weather condition provides very different lane detection accuracy. These factors directly influence the closed-loop performance, for example, the lane-following accuracy in a lane-keep assist system (LKAS). We propose a hardware- and situation-aware design of AI perception where the idea is to define the situations by a set of relevant environmental factors (e.g., weather, road etc. in an LKAS). We design the learning algorithms and parameters, overall hardware mapping and its schedule taking the situation into account. We show the effectiveness of our approach considering a realistic LKAS case-study on heterogeneous NVIDIA AGX Xavier platform in a hardware-in-the-loop framework. Our approach provides robust LKAS designs with 32% better performance compared to traditional approaches.
18:15 CET13.1.4ORCHESTRATION OF PERCEPTION SYSTEMS FOR RELIABLE PERFORMANCE IN HETEROGENEOUS PLATFORMS
Speaker:
Soumyajit Dey, Indian Institute of Technology Kharagpur, IN
Authors:
Anirban Ghose1, Srijeeta Maity2, Arijit Kar1 and Soumyajit Dey3
1Indian Institute of Technology, Kharagpur, IN; 2student, IN; 3IIT Kharagpur, IN
Abstract
“Delivering driving comfort in this age of connected mobility is one of the primary goals of semi-autonomous perception systems increasingly being used in modern automotives. The performance of such perception systems is a function of execution rate which demands on-board platform-level support. With the advent of GPGPU compute support in automobiles, there exists an opportunity to adaptively enable higher execution rates for such Advanced Driver Assistant System tasks (ADAS tasks) subject to different vehicular driving contexts. This can be achieved through a combination of program level locality optimizations such as kernel fusion, thread coarsening and core level DVFS techniques while keeping in mind their effects on task level deadline requirements and platform-level thermal reliability. In this communication, we present a future-proof, learning-based adaptive scheduling framework that strives to deliver reliable and predictable performance of ADAS tasks while accommodating for increased task-level throughput requirements.”

ASD.REC ASD Reception

Date: Thursday, 04 February 2021
Time: 18:30 – 19:00 CET
Virtual Conference Room: https://virtual21.date-conference.com/meetings/virtual/jPSN2q5euq43mmf2G

Session chair:
Rolf Ernst, TU Braunschweig, DE

Session co-chair:
Selma Saidi, TU Dortmund, DE

Organizers:
Rolf Ernst, TU Braunschweig, DE
Selma Saidi, TU Dortmund, DE

In the virtual reception session of the DATE Special Initiative on Autonomous Systems Design, we will enjoy (1) the talk of Dr. Gabor Karsai (Vanderbilt University) on “Towards Assurance-based Learning-enabled Cyber-Physical Systems”, (2) introduce the Friday topics, (3) exchange thoughts on autonomous systems design, and (4) collect feedback regarding the special initiative.

TimeLabelPresentation Title
Authors
18:30 CETASD.REC.1TOWARDS ASSURANCE-BASED LEARNING-ENABLED CYBER-PHYSICAL SYSTEMS
Speaker and Author:
Gabor Karsai, Vanderbilt University, US
Abstract
This talk will provide an overview of the DARPA Assured Autonomy program and give project examples.
ASD 2021

Friday Interactive Day of the Special Initiative on Autonomous Systems Design (ASD) at DATE’21

Start: Friday, 5 February 2021 08:30

End: Friday, 5 February 2021 18:10

The Friday Interactive Day of the DATE Special Initiative on Autonomous Systems Design (ASD) features keynotes from industry leaders as well as interactive discussions initiated by short presentations on several hot topics. Presentations from General Motors and BMW on predictable perception, as well as a session on dynamic risk assessment will fuel the discussion on how to maximize safety in a technically feasible manner. Speakers from TTTech and APEX.AI will present insights into Motionwise and ROS2 as platforms for automated vehicles. Further sessions will highlight topics such as explainable machine learning, self-adaptation for robustness and self-awareness for autonomy, as well as cybersecurity for connected vehicles.

Registration

Sponsored by

Argo AI

Free registration for the ASD Friday Interactive Day (W05) sponsored by Argo AI can be obtained here: https://www.date-conference.com/registration 

Program

ASDW05-01 Opening & Introduction

Session Start Fri, 08:30 Session End Fri, 09:00

  • Welcome and Introduction by ASD Organizers
  • “Argo AI’s mission and technology at a glance” by Alexandre Haag, Managing Director, Argo AI GmbH

ASDW05-02 Dynamic Risk Assessment in Autonomous Systems

Session Start Fri, 09:00 Session End Fri, 09:55

  • Organizers / Chairs:
    • Peter Liggesmeyer, Fraunhofer IESE
    •  Rasmus Adler, Fraunhofer IESE 
    • Richard Hawkins, University of York 
  • Session Abstract:   An autonomous system is capable of independently achieving a predefined goal in accordance with the demands of the current situation. In safety-critical applications, the operational situations may demand some actions from the system in order to keep risks at an acceptable level. This motivates the implementation of algorithms that estimate, assess and control risks during operation. In particular, the risk assessment at runtime is challenging as it implies moral decision making about acceptability of risks: “How safe is safe enough?”. However, it is also challenging to find a suitable notion of risk. IEC and IEC standards define the term “risk” differently following two “root” definitions: “combination of the probability of occurrence of harm, and the severity of that harm” and “effect of uncertainty on objectives”. The first definition is related to the way how integrity levels like SIL and ASIL are determined at design-time. In the session, we will discuss in how far existing design-time approaches can be adopted to implement an autonomous risk management at runtime. For instance, is it reasonable to implement algorithms that determine integrity levels at runtime?
  • Speakers:
    • Detlev Richter, TüV SüD:   Digital twin-based hazard analysis at runtime for resilient production
    • Simon Burton, Fraunhofer IKS:   Prerequisites for dynamic risk management 
    • Patrik Feth, Sick AG:    Sensors for Dynamic Risk Assessment 
    • Michael Woon, retrospect:   Being Certain of Uncertainty in Risk 

ASDW05-03 Cybersecurity for Connected Autonomous Vehicles

Session Start Fri, 10:00 Session End Fri, 10:55

  • Organizers / Chairs:  
    • Sebastian Steinhorst, Technical University of Munich, Germany
    • Mohammad Hamad, Technical University of Munich, Germany 
  • Session Abstract:  Today’s vehicles are increasingly connected and tomorrow’s vehicles will be automated, autonomous, capable of sensing their environment and navigating through cities without human input. This comes at the cost of a new set of threats and cyber-attacks that can yield high recall costs, property loss, and even jeopardize human safety. In this session, three partners of the nIoVe H2020 EU project will present security challenges and solutions to improve future autonomous vehicles’ security. The session starts with a short presentation about insights on the challenges and limitations of providing cyberthreat protections for public transport autonomous shuttles. The second talk discusses the need to make autonomous vehicles proactively able to react to intrusions and the challenges to achieving such a capability. The last talk addresses further cyber-security challenges that nIoVe aims to solve for autonomous vehicles. The session will continue with an open discussion to discuss all the introduced challenges and other related aspects. 
  • Talks: 
    • Niels Nijdam, University of Geneva (UNIGE), Switzerland  The Perils of Cybersecurity in Connected Automated Vehicles
    • Mohammad Hamad, Technical University of Munich (TUM), Germany  Toward a Multi-layer Intrusion Response System for Autonomous Vehicles
    • Konstantinos Votis, Institute/Centre for Research and Technologies Hellas (CERTH/ITI), Greece  Cyber-security Solutions for Autonomous Vehicles

ASDW05-04 Self-adaptive safety- and mission-critical CPS: wishful thinking or absolute necessity?

Session Start Fri, 11:00 Session End Fri, 11:55

  • Organizers / Chairs:
    • Andy Pimentel (University of Amsterdam, Netherlands)
    • Martina Maggio (University of Saarland, Germany) 
  • Session Abstract:  Due to the increasing performance demands of mission- and safety-critical Cyber Physical Systems (CPS), these systems exhibit a rapidly growing complexity, manifested by an increasing number of (distributed) computational cores and application components connected via complex networks. However, with the growing complexity and interconnectivity of these systems, the chances of hardware failures as well as disruptions due to cyber-attacks will also quickly increase. System adaptivity, for example in the form of dynamically remapping of application components to processing cores, represents a promising technique to handle this challenging scenario. In this session, we address the (consequences of the) idea of deploying runtime adaptivity to mission- and safety-critical CPS, yielding dynamically morphing systems, to establish robustness against computational hurdles, component failures, and cyber-attacks.
  • Speakers: 
    • Clemens Grelck (University of Amsterdam, Netherlands)  The TeamPlay Coordination Language for Dependable Systems
    • Sasa Misailovic (University of Illinois at Urbana-Champaign, USA)  Programming Systems for Helping Developers Cope with Uncertainty
    • Stefanos Skalistis (Raytheon Technologies, Ireland)  Certification challenges of adaptive avionics systems

ASDW05-05 Predictable Perception

Session Start Fri, 14:00 Session End Fri, 14:55

  • Organizers / Chairs: 
    • Samarjit Chakraborty (U North Carolina, Chapel Hill, USA)
    • Petru Eles (Linköping University, SE)
  • Session Abstract: Modern autonomous systems – such as autonomous vehicles or robots – consist of two major components: (a) the decision making unit, which is often made up of one or more feedback control loops, and (b) a perception unit that feeds the environmental state to the control unit and is made up of camera, radar and lidar sensors and their associated processing algorithms and infrastructure. While there has been a lot of work on the formal verification of the decision making (or the control) unit, the ultimate correctness of the autonomous system also heavily relies on the behavior of the perception unit. The verification of the correctness of the perception unit is however significantly more challenging and not much progress has been made here. This is because the algorithms used by perception units now increasingly rely on machine learning techniques (like deep neural networks) that run on a complex hardware made up CPU+accelerator platforms. The accelerators are made up of GPUs, TPUs and FPGAs. This combination of algorithmic + implementation platform complexity and heterogeneity currently makes it very difficult to provide either functional or timing correctness guarantees of the perception unit, while both of these guarantees are needed to ensure the correct functioning of the control loop and the overall autonomous system. This is a part of the overall challenge of verifying the correctness of autonomous systems.
  • Speakers:
    • Qing Rao (BMW, Munich, Germany)  New Era in Autonomous Driving and the Role of IT – Will Traditional Carmakers Keep Pace?
    • Soheil Samii (General Motors R&D, USA)  Dependable sensing system architecture for predictable perception in autonomous vehicles
    • Deepak Shankar (Mirabilis Design, USA)  Design Tools for Predictable Hw/Sw Architectures for Autonomous Vehicles
    • Cong Liu (UT Dallas, USA)   Towards Timing-Predictable & Robust Autonomy in Autonomous Embedded Systems
    • Hamed Tabkhi (University of North Carolina at Charlotte, USA)  Toward AI-in-the-Loop Autonomous Safety System – Algorithmic and Timing Challenges

ASDW05-06 Perspicuous Computing

Session Start Fri, 15:00 Session End Fri, 15:55

  • Organizers:
    • M. Christakis (MPI SWS)
    • H. Hermanns (U Saarland, Germany)
  • Session Abstract:   From autonomous vehicles to Industry 4.0, from smart homes to smart cities – cyber-physical technology increasingly participates in actions and decisions that affect humans. However, our understanding of how these applications interact and what is the cause of a specific automated decision is lagging far behind. This comes with a gradual loss in understanding. The root cause of this problem is that contemporary systems do not have any built-in concepts to explicate their behaviour. They calculate and propagate outcomes of computations, but are not designed to provide explanations. They are not perspicuous. The key to enable comprehension in a cyber-physical world is a science of perspicuous computing. This session will discuss the foundational, the industrial and the societal dimensions of the perspicuous computing challenge. It is organized by the Center for Perspicuous Computing – TRR 248 – a Collaborative Research Center funded by the German Research Foundation DFG.
  • Session Structure:
    • Introduction: “Enabling comprehension in a cyber-physical world with the human in the loop”
      Holger Hermanns, Universität des Saarlandes
    • Panel: “Is industry or society in need for perspicuous computing? Both? Or neither?”
      • Panel Moderator: Christel Baier, Technische Universität Dresden
      • Panelists:
        • Bernd Finkbeiner CISPA Helmholtz Center for Information Security
        • Christof Fetzer Technische Universität Dresden
        • Raimund Dachselt Technische Universität Dresden
        • Prof. Rupak Majumdar Max Planck Institute for Software Systems
        • Dr. Lena Kästner Universität des Saarlandes, representing EIS
  • More details: https://www.perspicuous-computing.science/date-2021-session-perspicuous-computing/

ASDW05-07 Production Architectures & Platforms for Automated Vehicles

Session Start Fri, 16:00 Session End Fri, 16:55

  • Organizer / Chair: Rolf Ernst, TU Braunschweig, Germany 
  • Session Abstract:   Highly automated vehicles need high performance HW/SW platforms to execute complex software systems for safety critical functions. This is a usually underestimated challenge when automation comes to production vehicles. The session starts with two short presentations of platform architectures that approach the resulting design quality and safety challenge with different methods. The session will continue with an open discussion of these and possibly other approaches.  
  • Speakers:
    • W. Steiner, TTTech, Austria  MotionWise – A Brief Introduction and Outlook 
    • D. Pangercic, APEX.AI, USA  Open-source and Developer Centric SW Platform for the New Breed of Vehicles 

ASDW05-08 Self-Awareness for Autonomy

Session Start Fri, 17:00 Session End Fri, 17:55

  • Organizer / Chair: Nikil Dutt (UC Irvine, USA) 
  • Session Abstract:  Self-awareness principles promise to endow autonomous systems with high degrees of adaptivity and resilience, borrowing from an abundance of examples in biology and nature. However, the engineering of dependable and predictable autonomous systems pose significant challenges for explainability, testing, and bounding safe behaviors. This session begins with short presentations by academic and industry speakers on these topics, and is followed by an interactive discussion with the audience. The first presentation by Prof. Andreas Herkersdorf (TU Munich) discusses how transparent machine learning techniques can be coupled with self-awareness  to improve dependability in autonomous systems. The second presentation by Dr. Ahmed Nassar (Nvidia) addresses issues in training and testing of self-aware autonomous agents. The third presentation by Dr. Prakash Sarathy (Northrup Grumman) describes how to bound the emergent behavior of autonomous systems using a self-aware dataflow computing paradigm. The session is then followed by an open discussion between the audience and the speakers on research challenges and future directions at the intersection of self-awareness and autonomy. 
  • Speakers:
    • A. Herkersdorf, TU Munich, Germany.    Transparent ML as a means of enhancing dependable autonomy 
    • A. Nassar, Nvidia, USA.  Continuous Training and Testing of Autonomous Agents: The Road to Self-Awareness 
    • P. Sarathy, Northrop Grumman, USA.  Self-aware dataflow computing for Bounded Behavior Assurance

ASDW05-09 ASD Closing

Session Start Fri, 18:00 Session End Fri, 18:10

  • Closing address by the ASD organizers.