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Impressions from CSCS 2017


Computer Science in Car Symposium was the first conference around automotive research
and applications from industry and academic research held in Munich on July, 7th.

We are thankful to our guests, students, speakers and supporters!
If you are interested in videos and topics covered at CSCS 2017, you find them at our vimeo Channel soon.

We had a great time and are looking forward to CSCS 2018

Here are some impressions from CSCS we want to share with you:
Gallery

 

Your Team of  ACM Chapters

CSCS Speaker Announcement: Eyal Amir


parknav


Title:

Commonsense for Cars

Abstract:

Cars increasingly are equipped with sensors that can sense their surroundings and insides. These sensors are about to be connected to the cloud in an online fashion, promising to deliver a new era of connectivity and information flow between cars. Cars of the future increasingly would enable autonomous driving, and so need to know and understand more than before. Common sense, such as understanding the meaning of a ball stuck under a parked car, or the possible intentions of a kid hiding behind that parked car, is traditionally easy for people and hard for computers and artificial intelligence. This kind of understanding and commonsense is necessary to ensure safe driving and safe co-existence of humans with those AI cars. In this presentation, I will describe the tools already developed in AI and those that still need to be developed to reach this goal of common sense for cars.
(http://parknav.com)

Bio:

Eyal Amir is CEO and Chief Data Scientist at Ai Incube (Parknav) and a professor of computer science at University of Illinois Urbana-Champaign. Ai Incube is an AI company that distills real-time data about the world from IoT data, licensing this to automotive OEMs and mobile-payment companies. Parknav is Ai Incube’s first home-run data stream, providing precise large-scale estimates of street parking availability.

Eyal’s research focuses on combining AI and Machine Learning, and his data science specializes in using geo-dynamic data. Previously, he received tenure and Associate Professor at UIUC in 2009, joined UIUC in 2004, and was a researcher at UC Berkeley. He received his Ph.D. in Computer Science from Stanford University, and his B.Sc. and M.Sc. degrees in mathematics and computer science from Bar-Ilan University, Israel. He is co-author of more than 100 scientific peer-reviewed papers, was chosen by IEEE as one of the „10 to watch in AI“ (2006), awarded the Arthur L. Samuel award for best Computer Science Ph.D. thesis (2001-2002) at Stanford University, and received the CAREER Award from the National Science Foundation. He splits his time between San Francisco, CA and Munich, Germany.

ACM Chapters Computer Science in Cars Symposium 2017

>> Details about CSCS 2017 here.

CSCS Speaker Announcement: Robert Bücher


buecher
Robert Bücher

 


Title:

Limitations of Inside-Out-Tracking in industrial applications

Abstract:

The traditional way of tracking is limited to visibility of the markers. ART discusses a new approach of markerless solutions for industrial use and introduces its solutions.

Bio:

Robert Bücher holds a degree in Industrial Engineering for Production Technologies from the University of Aachen (RWTH) and a Black Belt in Lean Six Sigma. He has worked in the Automotive Industries as Product Manager for assemblies and later on as consultant for innovation and new technologies. He holds patents in diverse business fields, so in optoelectronics. His positions in optoelectronics and human machine interfaces distinguishes him as an expert in AR. With the company Physoptics he was involved in the development of natural 3D glasses with integrated physiognomic eye tracking.  He designed interactive video transmission and surveillance systems with long-range remote functionality. Since 2017 he is responsible as Business Development Manager for markerless tracking applications at ART GmbH, Weilheim, a leading developer of tracking technologies.

ACM Chapters Computer Science in Cars Symposium 2017

>> Details about CSCS 2017 here.

CSCS Speaker Announcement: Chih-Hong Cheng and Georg Nührenberg


02_fortiss_logo_rgb_2

Title:

Formal Methods for Dependable Neural Networks

Abstract:

The deployment of Artificial Neural Networks (ANNs) in safety-critical applications poses a number of new verification and certification challenges. For scenarios such as autonomous driving, it is important to establish sound claims on safety properties over the demonstrated behavior of ANNs. Examples include the absence of tendencies to change to occupied lanes, and resilience of ANNs to noisy or even maliciously manipulated sensory input. In this talk, we highlight some developments as an initial step towards realizing the full potential of formal methods for ANNs and their deployment in new safety-critical functionalities such as self-driving cars. Concretely, we demonstrate concepts such as (1) how to perform automatic verification of ANNs via a reduction to solving mixed integer optimization problems (MIP), and (2) how to monitor and regulate decisions generated by ANNs, by overlaying ANNs with formally synthesized maximal pervasive controllers.

Bio:

Dr. Chih-Hong Cheng studied Computer Science at Technical University of Munich and obtained his PhD on game-based software synthesis in 2012. After two years working in ABB corporate research as a research scientist, he joined fortiss and is now head of the Software Dependability department. He manages various research projects such as cloud for mission-critical infrastructures, formal methods for dependable machine learning, and techniques for automating software engineering processes.

Georg Nührenberg got a Master’s Degree in Computer Science at ETH Zurich, where he focused on theoretical computer science and mathematical optimization. He wrote his Master’s Thesis at IBM Research Zurich. He obtained his Bachelor’s Degree in Computer Science at FU Berlin. Since November 2016, Georg Nührenberg works in the Software Dependability department of fortiss. His research interests include formal methods for autonomous systems and verification of machine learning algorithms.

ACM Chapters Computer Science in Cars Symposium 2017

>> Details about CSCS 2017 here.

CSCS Speaker Announcement: Prof. Dr. Daniel Cremers


Cremers
Prof. Dr. Daniel Cremers


Title:

Computer Vision for Autonomous Driving

Abstract:

Over the last years, the field of Computer Vision has matured from a fairly small niche area in computer science to one of the hottest topics in technological development today.  In my presentation, I will sketch a number of recent developments in computer vision and in particular in 3D reconstruction from moving cameras.  These methods for Simultaneous Localization and Mapping (SLAM) can accurately localize the camera and recover the observed 3D world.  They will form a central building block of future self-driving cars.

Bio:

Prof. Dr. Daniel Cremers obtained a PhD in
Computer Science from the University of Mannheim, Germany.
Subsequently he spent two years as a postdoctoral researcher
at the University of California at Los Angeles (UCLA) and one year as
a permanent researcher at Siemens Corporate Research in Princeton,
NJ. From 2005 until 2009 he was associate professor at the University
of Bonn, Germany. Since 2009 he holds the chair for Computer Vision
and Pattern Recognition at the Technical University, Munich. His
publications received numerous awards, including the ‚Best Paper of
the Year 2003‘ (Int. Pattern Recognition Society), the ‚Olympus Award
2004‘ (German Soc. for Pattern Recognition) and the ‚2005 UCLA
Chancellor’s Award for Postdoctoral Research‘. For pioneering research
he received a Starting Grant (2009), a Proof of Concept Grant (2014)
and a Consolidator Grant (2015) by the European Research Council. In
December 2010 he was listed among „Germany’s top 40 researchers below
40“ (Capital). Prof. Cremers received the Gottfried-Wilhelm Leibniz
Award 2016, the most important research award in German academia.

ACM Chapters Computer Science in Cars Symposium 2017

>> Details about CSCS 2017 here.

CSCS Speaker Announcement: Prof. Dr. Michael Haller


Prof. Dr. Michael Haller

Prof. Dr. Michael Haller

Title:

Imperceptible Textile Interfaces in automotive use cases

Abstract:

Abstract: The increased interest in interactive soft materials, such as smart clothing and responsive surfaces, means that there is a need for flexible and deformable input devices.

We at the Media Interaction Lab are investigating the implementation and evaluation of next generation interfaces. In this presentation, we show how to implement curved, smart interfaces, ranging from printed film solutions to textile-based interfaces, which are then further used in different automotive use cases. These newly emerging form factors require novel human–computer interaction techniques which will be discussed in this presentation. In this work, we will further describe particular challenges and solutions for the design of flexible input sensors.

Bio:

Michael Haller is a professor at the department of Interactive Media of the University of Applied Sciences Upper Austria (Hagenberg, Austria), where he is founder and director of the Media Interaction Lab (www.mi-lab.org), responsible for computer graphics & human-computer interaction. His core areas of expertise are smart graphics and interaction developing next-generation user interfaces. He received Dipl.-Ing. (1997), Dr. techn. (2001), and Habilitation (2007) degrees from Johannes Kepler University of Linz, Austria. His current focus is on innovative interaction techniques and smart interfaces for next generation working environments. Currently, he leads a team of over 10 researchers and students. He has been awarded the Erwin Schrödinger Fellowship Award, Google Research Award, Europrix Top Talent Award, Best ACM SIGGRAPH Emerging Technologies Award, and Microsoft Imagine Cup. Seven of his papers were awarded best paper or honorable mention at top HCI venues including ACM CHI and ACM UIST.

ACM Chapters Computer Science in Cars Symposium 2017

>> Details about CSCS 2017 here.

CSCS Speaker Announcement: Dr. Sudipta Bhattacharjee


Sudipta. 2

Dr. Sudipta Bhattacharjee (KPIT Technologies Ltd.)

Title:

Impact of quality of training dataset on pedestrian detection for autonomous driving

Abstract:

In autonomous driving, the perception task is a key piece requiring object detectors like pedestrian detection and vehicle detection among other. These object detectors are typically implemented using machine learning algorithms involving pipeline of classifiers or using deep learning networks. Special considerations are required for training dataset for each classifier in the pipeline. The performance of generated classifier models depends not just the size of the dataset but also the on quality of the training dataset in terms of coverage of scenarios. This work also presents the impact of ratio of positive and negative training samples sizes towards improving the performance of different types of classifiers in the pipeline.

Bio:

Sudipta Bhattacharjee is Associate Solution Architect at the KPIT Technologies Ltd. for ADAS practice. He did his Ph. D. from Indian Institute of Technology, Kharagpur, India. He has more than 10 years of experience for conducting industrial R&D work. His areas of work includes computer vision techniques, machine learning, data analytics, wireless sensor network, electronic system design & integration. He has filed 5 patents, and published 1 book, 9 journals and 11 conference papers. He is the winner of scholarship A for 2014 ISRM International Symposium (ARMS8), Sapporo, Japan. He is the peer reviewer of IEEE transactions on industrial electronics, Journal of optics & lasers in engineering, IEEE sensor journal and IEEE wireless communication magazine.

ACM Chapters Computer Science in Cars Symposium 2017

>> Details about CSCS 2017 here.

 

 

CSCS Speaker Announcement: Dr. Mario Fritz


mario_fritz

Dr. Mario Fritz (Max Planck Institute for Informatics)

 

Title:

Towards Anticipation in Traffic Scene Understanding

Abstract:

Scene understanding e.g. in terms of semantic segmentations and object detections has made great advances in recent years. In order to facilitate safe autonomous or assisted driving in real-world scene, we have to go beyond assessing the current traffic scene and rather predict possible consequences and future states.

I will present our latest work in this direction that aims at „predicting the future“. In particular, we have been developing Deep Learning techniques that encode prior observations and decode them in a recursive fashion and thereby extrapolate them into the future.

Bio:

Mario Fritz is senior researcher at the Max Planck Institute for Informatics. He is heading a group on Scalable Learning and Perception. His research interest are centred around computer vision and machine learning but extend to natural language processing, robotics, graphics, HCI, privacy and more general challenges in AI. He did his postdoc at the International Computer Science Institute as well as UC Berkeley on a Feodor Lynen Research Fellowship of the Alexander von Humboldt Foundation. He received his PhD from TU Darmstadt and graduated from the University of Erlangen-Nuremberg.

ACM Chapters Computer Science in Cars Symposium 2017

>> Details about CSCS 2017 here.

 

 

CSCS Speaker Announcement: Dr. Alexey Dosovitskiy


Dr. Alexey Dosovitskiy

Dr. Alexey Dosovitskiy (Intel Visual Computing Lab)

 

Title:

Learning sensorimotor control from experience and demonstration

Abstract:

An intelligent agent should be capable of performing useful actions based on sensory observations – a feat known as sensorimotor control. The notion of usefulness depends on a specific situation – for instance, in a driving scenario useful actions would get the vehicle to the destination point as fast as possible without crashing or violating traffic rules. I will talk about two general approaches to learning sensorimotor control – learning from experience and learning from demonstration – and about recent research projects in our lab in both of these directions. In one, we train an agent to navigate in three-dimensional environments based purely on its experience, without any human supervision. In another, we use imitation learning to train an agent to drive in busy urban environments and follow passenger’s commands.

Bio:

Alexey Dosovitskiy received his MSc and PhD degrees in mathematics (functional analysis) from Moscow State University in 2009 and 2012 respectively. He spent 2013-2015 as a postdoctoral researcher at the Computer Vision Group of Prof. Thomas Brox at the University of Freiburg in Germany, with research focus on deep learning, specifically unsupervised learning, image generation with neural networks, motion and 3D structure estimation. Since May 2016 Alexey works on deep learning and sensorimotor control at Intel Visual Computing Lab led by Dr. Vladlen Koltun.

ACM Chapters Computer Science in Cars Symposium 2017

>> Details about CSCS 2017 here.

 

 

Link

cscs_logo_v09_3_crop

ACM Chapters Computer Science in Cars Symposium 2017
Munich, Germany | July 6, 2017

>> Event Details

>> Get your Ticket here

Position Papers and Posters

We invite PhD and master students to submit position papers that will be compiled into proceedings of the event that will be available online on the Web pages. Each accepted position paper will be presented as a poster or might be selected for a short oral presentation. We plan also to invite the top ranked position papers and invited speakers to submit long papers for publication in a special issue of an international journal.

Submission information: Position papers should have the length of 2-4 pages in LNCS style formatting1. Please submit your position papers as PDF- file to the following email address:

munich-cscs@siggraph.org

Dates

  • Deadline for position papers:  June 6th, 2017

  • Notifications: June 14th, 2017

  • Event: July 6th, 2017

 

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1 ftp://ftp.springer.de/pub/tex/latex/svproc/guidelines/Springer_Guidelines_for_Authors_of_Proceedings.pdf
and
https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
(important downloads for authors)