[robotics-worldwide] [journals] 2nd CfP - RA-L Special Issue "Long-term Human Motion Prediction"

Palmieri Luigi (CR/AER1) Luigi.Palmieri at de.bosch.com
Thu Mar 26 01:40:28 PDT 2020

2nd Call for Papers

Special Issue RA-L 2020 on
Long-term Human Motion Prediction

Key Facts
Submission opening: April 1, 2020
Submission deadline: May 1, 2020
URL: https://urldefense.com/v3/__https://www.ieee-ras.org/publications/ra-l/special-issues/cfp-special-long-term-human-motion-prediction__;!!LIr3w8kk_Xxm!95ETN1PmW5MF1YrlBB8FFSHAytZ0rvIp4bAQI6Lr8UpNyCS1nuKAU6BQxl5iLmbErzHXe5uc$ 

Topics of interest include, but are not limited to:
- Motion trajectory prediction in 2D and 3D
- Predicting articulated human motion
- Early action and activity recognition
- Predictive motion and task planning in dynamic environments
- Anticipation of group and crowd motion
- Human motion prediction and safety
- Human-Robot Interaction considering predictions
- Evaluation of prediction algorithms: datasets, metrics and benchmarks
- Predictive planning and control
- Applications of motion prediction techniques
- Visual scene prediction

Anticipation is a key ability for advanced autonomous systems, especially if they operate in densely crowded environments and alongside humans. Examples of tasks benefiting from predictive human motion models include robot task and motion planning, automated driving, physical human-robot collaboration, intelligent video surveillance. Foreseeing how a scene involving multiple agents evolves over time and incorporating predictions in a proactive manner allows for novel ways of planning, active perception, model predictive control, or human- robot interaction. The growth of community interest to the area in recent years is evident, in particular from the publication trends in regular paper sessions of the recent robotics conferences (ICRA, IROS, RSS and others). Furthermore, the area is recognized as a major component/research direction by several big players in the automotive industry.

The proposal of this Special Issue is motivated by the success of the workshop on “Long-term Human Motion Prediction”, which took place at ICRA 2019 in Montreal, see https://urldefense.com/v3/__https://motionpredictionicra2019.github.io/__;!!LIr3w8kk_Xxm!95ETN1PmW5MF1YrlBB8FFSHAytZ0rvIp4bAQI6Lr8UpNyCS1nuKAU6BQxl5iLmbEr6J1jjj0$ , and the follow-up event at ICRA 2020, see https://urldefense.com/v3/__https://motionpredictionicra2020.github.io/__;!!LIr3w8kk_Xxm!95ETN1PmW5MF1YrlBB8FFSHAytZ0rvIp4bAQI6Lr8UpNyCS1nuKAU6BQxl5iLmbEry4q31ig$ . With this special-issue we aim to collect most prominent works on the topic and expand the state of the art.

Important Dates
Submission opening: April 1, 2020
Submission deadline: May 1, 2020
Authors receive RA-L reviews and recommendation: July 26, 2020
Authors receive final RA-L decision: September  29, 2020
Accepted RAL Papers appear on Xplore: October 18, 2020
Final Publication: October 28, 2020

Technical Committees Endorsements
This Special Issue proposal is endorsed by the IEEE RAS Technical Committees on: Autonomous Ground Vehicles and Intelligent Transportation Systems, Human Motion Understanding, Collaborative Automation for Flexible Manufacturing.

Guest Editors:
Luigi Palmieri, Robert Bosch GmbH, Germany
Andrey Rudenko, University of Orebro, Sweden
Jim Mainprice, University of Stuttgart, Germany
Alexandre Massoud Alahi, EPFL, Switzerland
Kai O. Arras, Robert Bosch GmbH, Germany
Achim Lilienthal, University of Orebro, Sweden
Marc Hanheide, University of Lincoln, UK

We welcome researchers in the field to submit papers to the special issue. Submitted manuscripts should be formatted according to RA-L standards, more information at https://urldefense.com/v3/__https://www.ieee-ras.org/publications/ra-l/information-for-authors-ra-l*RALTypes__;Iw!!LIr3w8kk_Xxm!95ETN1PmW5MF1YrlBB8FFSHAytZ0rvIp4bAQI6Lr8UpNyCS1nuKAU6BQxl5iLmbEr5a10yzC$ . We encourage authors to submit a video clip to complement their manuscript. Submissions will be selected based on their originality, relevance to the special issue topics, contributions, technical clarity, and presentation.


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