July 11, 2021
A workshop in conjunction with IV 2021 in Nagoya, Japan
Recent advancements in the processing units have improved our ability to construct a variety of archi- tectures 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 suc- cess 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, as well as using the test drive data from the sensors and hardware mounted on a vehicle. Nevertheless, state of the driver and passengers inside the cabin have been also of a big importance for the traffic safety and the holistic spatio-temporal perception of the environment.
Aim of this workshop is to form a platform for exchanging ideas and linking the scientific community active in intelligent vehicles domain. This workshop will provide an opportunity to discuss applications and their data-dependent demands for spatio-temporal understanding of the surroundings as well as inside of a vehicle 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.
Workshop paper submission: (extended) May 10th, 2021
Notification of workshop paper acceptance: May 15th, 2021
Final Workshop paper submission: May 31st, 2021
DDIVA Workshop: July 11, 2021
Please also check the conference web page for updates.
|9:00||9:10||Introduction & Welcome|
|11:55||12:30||Panel Discussion 1|
|16:00||16:35||Panel Discussion 2|
|Affiliation||Laboratory for Intelligent & Safe Automobiles, UC San Diego|
|Title of the talk|
|Speaker||Prof. Dr. Abhinav Valada|
|Affiliation||Robot Learning Lab, Albert-Ludwigs-Universität Freiburg|
|Title of the talk||Learning Holistic Scene Understanding Models of Dynamic Urban Environments|
|Speaker||Dr. Julian F.P. Kooij|
|Affiliation||Intelligent Vehicles Group, TU Delft|
|Title of the talk|
|Speaker||Dr.-Ing. Fabian Oboril|
|Affiliation||Research Scientist for Dependable Driving Automation, Intel Labs|
|Title of the talk||Using the CARLA simulator for AV test and validation|
Automated vehicles (AVs) are gaining increasing interest and their development is making great progress. However, assuring safe driving operation under all possible road and environment conditions is still an open challenge. In this regard, vehicle simulation is seen as a major corner stone for test and validation. Recorded real world challenges can be rebuild in simulation (e.g. NHTSA pre-crash scenarios) and in addition artificial corner cases can be added on top. Those can then be utilized to test the complex software stack in various configurations to find possible safety or availability issues. For example, the same situation can be tested with different settings of the planning modules (driving policy) or road conditions to ensure that all possibilities result in safe driving. In this talk, we will present how the CARLA vehicle simulator in combination with an open source scenario editor can be used to re-create traffic scenarios, play those under various operating conditions and by that means make one step towards safe autonomous driving.
|Speaker||Dr. Alexander Carballo|
|Affiliation||Designated Associate Professor, Nagoya University|
|Title of the talk|
|Speaker||Dr. Nazım Kemal Üre|
|Affiliation||Artificial Intelligence Research Center, Istanbul Technical University & Eatron Technologies|
|Title of the talk|
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:
- Synthetic Data Generation
- Sensor Data Synchronization
- Sequential Data Processing
- Data Labeling
- Data Visualization
- Data Discovery
- Visual Scene Understanding
- Large Scale Scene Reconstruction
- Semantic Segmentation
- Object Detection
- In Cabin Understanding
- Emotion Recognition
Contact workshop organizers: walter.zimmer( at )tum.de
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 IV2021 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.
- Language: English
- Paper size: US Letter
- Paper format: Two-column format in the IEEE style
- Paper limit: For the initial submission, a manuscript can be 6-8 pages. For the final submission, a manuscript should be 6 pages, with 2 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 IV2021 symposium:
To go paper submission site, please click here.