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Veranstaltungen im Wintersemester 2012/2013 / Master-Seminar: Künstliche Intelligenz und Maschinelles Lernen: Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) refer to a set of methodologies which aim at facilitating the challenges of transportation in the modern era. The ITS objective are multi-dimensional, ranging from the improvement of driving safety, minimization of travel time, reduction of fuel consumption, accurate trip routing, up to driving assistance and autonomous navigation.

Throughout this seminar students will have an opportunity to get deeper insight on the recent methodologies of ITS. Recent publications will be offered in one of the enumerated ITS tasks:

  • Traffic Sign/Pedestrian Detection: Detecting and accurately classifying traffic signs and/or pedestrians via camera sensors.
  • Routing: Selecting the 'best' path on the map between two desired endpoints by exploiting an optimization in terms of travel time, shortest distance and fuel consumption.
  • Analysis of Motion Patterns: Detection of large scale traffic and motion patterns from historical GPS records.
  • Traffic Management: Management of traffic flow and balancing of load in order to optimize the traffic through put per time unit.
  • Trajectory Planning and Collision Avoidance: Estimation of the motion dynamics of surrounding vehicles in order to predict their trajectories and subsequently avoid collision accidents.
  • Eco-Driving: Real time driver awareness feedback aiming at adopting ecologically friendly driving behaviors.
  • Smart Parking: Methods that facilitate parking via searching for optimal parking free slot.

The student load in this course consists of selecting one of the proposed publications, analyzing and understanding the method(s) described, and finally presenting it on the audience of the classroom. The student shall be able to reason on various advantages/disadvantages of the method and shall be prepared to answer ‘on-topic’ questions by the course members and the instructor(s). In the end of the course an in-depth report is expected to be delivered, which includes not only a description of the prepared study, but also personal analysis and criticism regarding the method.

Betreuer: Prof. Dr. Dr. Lars Schmidt-Thieme und Josif Grabocka

Zeit: Do 14-16 c.t.
Ort: B26, Spl
Zuordnung:Informatik / BI+KI+ML (MSc)
Modul- Handbuch:MHB
Voheriger Durchlauf:SoSe 2012