Keynote Talk I

Prof. Dr. Roland Siegwart
Autonomous System Lab
Institute of Robotics and Intelligent Systems ETH, Zürich

• 2006 - Full Professor for Autonomous Systems, Swiss Federal Institute of Technology Zürich (ETH);
• Director of the Autonomous System Lab (ASL) with, around 6 Postdocs, 25 doctoral students and 10 technical and administrative collaborators;
• Curriculum Vitae.

Keynote Talk

Key Tecnologies for Intelligent and Safer Cars - from Motion Estimation to Predictitive Motion Planning

Abstract: This talk presents approaches to three of the major challenges for (semi-) autonomous driving in urban environments: self-localization and ego-motion estimation, objects such as cars and pedestrians, and path planning in dynamic environments.

For each of these tasks we present a summary of the tecnhiques we employ and results on real data. All modules have been implemented and tested on our autonomous car plataform SmartTer, except from the path planning part which was tested on an indoor robot and is currently being adapted to the SmartTer.

Key Words: Driver assistance, motion estimation, object detection, predictive path planning

Keynote Talk II

Philippe Bonnifait
Research Laboratory Heudiasyc
University of Technology of Compiègne (UTC), France

• Professor in Robotics and Automation, University of Technology of Compiègne (UTC), France;
• Head of the research group “Automation, Embedded Systems and Robotics” ASER of the lab Heudiasyc (13 permanents team);
• Curriculum Vitae.

Keynote Talk

Positioning Integrity for Intelligent Vehicles

Abstract: Intelligent Vehicles are robotic systems that assist the driver in safe and comfortable operation by providing pertinent information or by controlling the vehicle itself. Real-time and safe perception of the driving environment is one of the key issues.

In this perception process, global positioning (also called self-localization) and map-matching are useful for retrieving contextual information stored in geographical databases.

This talk first recalls the essential attributes of the quality of service of a positioning system. In particular, we will focus on the concept of integrity that is nowadays well standardized in the aeronautical domain (for safety of life reasons). For robotized land vehicles, integrity is a new concern.

We will describe methods to compute Positioning Protection Levels using robust state observation approaches, in a multi-sensor context, since modern vehicles are often equipped with a GPS receiver, dead-reckoning sensors (such as wheel-speed measurements, easily accessible on a CAN bus), road navigable maps, lidars and cameras.

In a second part, we will present how to deal with map-matching integrity using multi-hypothesis road tracking.

Experimental results obtained with different vehicles in the framework of the POMA/CVIS European project will be presented.