Lars Töttel (M.Sc.)
Wissenschaftlicher Mitarbeiter
Werdegang
Lars Töttel studierte bis 2019 Elektrotechnik und Informationstechnik an der Dualen Hochschule Baden-Württemberg (DHBW) Mannheim sowie am Karlsruher Institut für Technologie (KIT). Seine Schwerpunkte während des Masterstudiums lagen in der Automatisierungstechnik und Informationsverarbeitung. Seine Masterarbeit "Predictive Trajectory Planning in Situations with Occluded Road Users Using POMDPs" führte er in der Abteilung Technisch Kognitive Systeme (TKS) durch.
Seit August 2019 ist Lars Töttel als wissenschaftlicher Mitarbeiter am FZI Forschungszentrum Informatik in der Abteilung TKS tätig.
Publikationen
Konferenzbeitrag (1)
- Predictive Trajectory Planning in Situations with Hidden Road Users Using Partially Observable Markov Decision ProcessesInfoDetails
Philip Schörner, Lars Töttel, Jens Doll and J. Marius Zöllner, 2019
State of the art emergency brake assistant systems solely based on sensor measurements reduced the number of traffic accidents and casualties drastically in recent years. In order to be able to react on road users who elude a vehicle’s field of view because of sensor limits or occlusions, this paper presents an approach to anticipate potential hidden traffic participants in occluded areas in the decision making process of an autonomous vehicle. A Partially Observable Markov Decision Process is used to determine the vehicle’s longitudinal motion. Observations are made using the vehicle’s field of view. Therefore the field of view is calculated with a generic model of a sensor setup in dependence of the current or predicted the environment. In this way, the vehicle can either observe that it detects a previously hidden road user or receives information that the road is clear. In total, that allows the vehicle to better anticipate future developments. Therefore, assumptions about vehicles that may be located in hidden areas need to be made. We demonstrate the approach in two scenarios. Firstly in a scenario, where the vehicle has to move cautiously into the intersection with a minimum number of actions and secondly in a typical scenario for urban traffic. Evaluation shows, that the approach is able to anticipate hidden road users correctly and act accordingly.
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Kontakt
Telefon: +49 721 9654-376
E-Mail: toettel@fzi.de- Predictive Trajectory Planning in Situations with Hidden Road Users Using Partially Observable Markov Decision ProcessesInfoDetails