4th International Workshop on "Data Driven Intelligent Vehicle Applications"

DDIVA 2023

     June, 4th 2023 (8:00-17:00 PT, GMT-8)

A workshop in conjunction with IV 2023 in Anchorage, Alaska, USA

Registration via IV23 Poral Site. Please check the conference website for details.

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This workshop aims to address the challenges in autonomous driving by focusing on Data and Application domains. Spatio-temporal data is crucial to improve accuracy in deep learning applications. In this workshop, we mainly focus on data and deep learning, since data enables through applications to infer more information about environment for autonomous driving. 

Recent advancements in the processing units have improved our ability to construct a variety of architectures for understanding the surroundings of vehicles. Deep learning methods have been developed for geometric and semantic understanding of environments in driving scenarios aim to increase the success of full-autonomy with the cost of large amount of data. Recently proposed methods challenge this dependency by pre-processing the data, enhancing, collecting and labeling it intelligently. In addition, the dependency on data can be relieved by generating synthetic data, which alleviates this need with the cost-free annotations. The aim of this workshop is to form a platform for exchanging ideas and linking the scientific community active in the intelligent vehicles domain. This workshop will provide an opportunity to discuss applications and their data-dependent demands for spatio-temporal understanding of the surroundings while addressing how the data can be exploited to improve results instead of changing proposed architectures.

Please click to view the workshop in the previous years.


Important Dates

DDIVA Workshop: June 4th 2023

Please also check the conference web page for updates.

February 01, 2023: Workshop Paper Submission Deadline

March 30, 2023: Workshop Paper Notification of Acceptance

April 22, 2023: Workshop Final Paper Submission Deadline


Workshop Program

Start End Time Zone: PT (GMT-8)
08:00 08:10 Introduction & Welcome
08:10 08:50 Keynote Speaker
08:50 09:30 Keynote Speaker
09:30 09:45

Accepted Paper Presentation

09:45 10:00 Accepted Paper Presentation
10:00 10:30 Coffee Break / Poster
10:30 10:45 Accepted Paper Presentation
10:45 11:00 Accepted Paper Presentation
11:00 11:40 Panel Discussion 1
11:40 13:00 Lunch
13:00 13:15 Accepted Paper Presentation
13:15 13:30 Accepted Paper Presentation
13:30 14:00

Coffee Break / Poster

14:00 14:40

Keynote Speaker

14:40 15:20

Keynote Speaker

15:20 16:00 Panel Discussion 2
16:00 16:15

Closing


Confirmed Keynote Speakers

Speaker TBD
Affiliation Laboratory for Intelligent & Safe Automobiles, UC San Diego
Title of the talk  
Abstract

TBD

 

 

Speaker Oliver Grau, Korbinian Hagn
Affiliation Intel Labs [https://www.intel.com/content/www/us/en/research/overview.html]
Title of the talk The role of synthetic data in the development of ML-based perception modules
Abstract
Perception is the central component of any automated system. With the introduction of machine learning (ML)-trained deep neural networks (DNNs) as core technology of perception modules, the importance of data for training, but also validation is evident. This presentation contributes to the question of how synthetic data can be used in the AI development process, to improve quality and performance of DNNs and to detect flaws and weaknesses in the validation process. We present VALERIE, our a fully automated parametric synthetic data generation pipeline to generate complex and highly realistic (RGB) sensor data. The demonstrated target is in the domain of urban street scenes for the use in automated driving scenarios. Further, we describe how the metadata from this system can be used to improve the quality and understanding of training and validation datasets.

 

 

Call For Papers

This workshop aims to address the challenges in autonomous driving by focusing on Data and Application domains. Spatio-temporal data is crucial to improve accuracy in deep learning applications. In this workshop, we mainly focus on data and deep learning, since data enables through applications to infer more information about environment for autonomous driving. This workshop will provide an opportunity to discuss applications and their data-dependent demands for understanding the environment of a vehicle while addressing how the data can be exploited to improve results instead of changing proposed architectures. The ambition of this full-day DDIVA workshop is to form a platform for exchanging ideas and linking the scientific community active in intelligent vehicles domain.

To this end we welcome contributions with a strong focus on (but not limited to) the following topics within Data Driven Intelligent Vehicle Applications:

Data Perspective:

  • Synthetic Data Generation
  • Sensor Calibration and Data Synchronization
  • Data Pre-processing
  • Data Labeling
  • Active Learning
  • Data Visualization

Application Perspective:

  • Semantic Segmentation
  • Object Detection and Tracking
  • Point Cloud Processing
  • Simulation
  • Domain Adaptation

 

Contact workshop organizers: walter.zimmer( at )tum.de


Submission

Please check the conference webpage for the details of submission guidelines.

Authors are encouraged to submit high-quality, original (i.e. not been previously published or accepted for publication in substantially similar form in any peer-reviewed venue including journal, conference or workshop) research. Authors of accepted workshop papers will have their paper published in the conference proceeding. For publication, at least one author needs to be registered for the workshop and the conference and present their work.

While preparing your manuscript, please follow the formatting guidelines of IEEE available here and listed below. Papers submitted to this workshop as well as IV2023 must be original, not previously published or accepted for publication elsewhere, and they must not be submitted to any other event or publication during the entire review process.

Manuscript Guidelines:

  • Language: English
  • Paper size: US Letter
  • Paper format: Two-column format in the IEEE style
  • Paper limit: 6 pages, with max. 4 additional pages allowed, but at an extra charge ($100 per page)
  • Abstract limit: 200 words
  • File format: A single PDF file, please limit the size of PDF to be 10 MB
  • Compliance: check here for more info
     

The paper template is also identical to the main IV2023 symposium:

Paper submission site: will be added soon.


Workshop Organizers