11th Workshop on Planning, Perception and Navigation for Intelligent Vehicles

Philippe Martinet1, Christian Laugier2, Christoph Stiller3, Miguel Ángel Sotelo Vázquez4, Marcelo Ang5

  • 1Ecole Centrale de Nantes
  • 2INRIA
  • 3Karlsruher Institut für Technologie
  • 4University of Alcalá
  • 5National University of Singapore

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Category

Workshop

Sessions

09:00 - 18:00 | Mon 4 Nov | LG-R10 | MoW-R10

11th Workshop on Planning, Perception and Navigation for Intelligent Vehicles

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Abstract

The purpose of this workshop is to discuss topics related to the challenging problems of autonomous navigation and of driving assistance in open and dynamic environments. Technologies related to application fields such as unmanned outdoor vehicles or intelligent road vehicles will be considered from both the theoretical and technological point of views. Several research questions located on the cutting edge of the state of the art will be addressed. Among the many application areas that robotics is addressing, transportation of people and goods seem to be a domain that will dramatically benefit from intelligent automation. Fully automatic driving is emerging as the approach to dramatically improve efficiency while at the same time leading to the goal of zero fatalities. This workshop will address robotics technologies, which are at the very core of this major shift in the automobile paradigm. Technologies related to this area, such as autonomous outdoor vehicles, achievements, challenges and open questions would be presented. Topics of interest •Road scene understanding •Lane detection and lane keeping •Pedestrian and vehicle detection •Detection, tracking and classification •Feature extraction and feature selection •Cooperative techniques •Collision prediction and avoidance •Advanced driver assistance systems •Environment perception, vehicle localization and autonomous navigation •Real-time perception and sensor fusion •SLAM in dynamic environments •Mapping and maps for navigation •Real-time motion planning in dynamic environments •Human-Robot Interaction •Behavior modeling and learning •Robust sensor-based 3D reconstruction •Modeling and Control of mobile robot •Deep learning applied in autonomous driving •Deep reinforcement learning applied in intelligent vehicles

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