Sunday, June 23rd, 2020
A workshop in conjunction with IV 2020 in Las Vegas, NV, United States
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 last year's workshop DDIVA'19.
Workshop paper submission: March 14th, 2020
Notification of workshop paper acceptance: April 18th, 2020
Final Workshop paper submission: May 2nd, 2020
DDIVA Workshop in mid-October, 2020
IV2020 is postponed to October, 2020. Please also check the conference web page for updates.
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: emec.ercelik( at )tum.de / burcu.karadeniz( at )in.tum.de
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 IV2020 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: 300 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 IV2020 symposium:
To go paper submission site, please click here.
|9:00||9:15||Introduction & Welcome|
|9:15||10:00||Keynote Speaker (Data Perspective)|
|10:00||10:15||Accepted Paper 1|
|10:15||10:30||Accepted Paper 2|
|11:00||11:15||Accepted Paper 3|
|11:15||11:30||Accepted Paper 4|
|11:30||12:00||Panel Discussion 1|
|12:00||13:00||Lunch / Poster Session|
|13:00||13:15||Accepted Paper 5|
|13:15||13:30||Accepted Paper 6|
|13:30||13:45||Accepted Paper 7|
|13:45||14:15||Coffee Break / Poster|
|14:15||15:00||Keynote Speaker (Application Perspective)|
|15:00||15:30||Coffee Break / Poster|
|15:30||16:15||Keynote Speaker (Application Perspective)|
|16:15||17:00||Panel Discussion 2|
|Affiliation||BMW Group Research, New Technologies, Innovations - Technology Office USA|
|Title of the talk||Agent and Perception Models for Realistic Simulations|
Simulation and reprocessing are crucial components for the assessment of automated vehicles. Current methods for simulating and reprocessing driving scenarios lack realistic agent and perception models. The lack of such models introduces a significant source of errors and can render experiment outcomes invalid. We present a methodology to leverage infrastructure sensor recordings from the real world to derive both agent and perception models. Such models need to be scenario- / maneuver-specific. When combined with an approach to automatically extract and cluster driving scenarios, these models can increase simulation realism and validity.
Uncertainty-aware semantic segmentation of LiDAR point clouds