Abstract
One of the key challenges of any Automated Driving (AD) system lies in the perception and representation of the driving environment. Data from a multitude of different information sources such as various vehicle environment sensors, external communication interfaces, and digital maps must be adequately combined to one consistent Comprehensive Environment Model (CEM) that acts as a generic abstraction layer for the driving functions. This overview article summarizes and discusses different approaches in this area with a focus on metric representations of static and dynamic driving environments for on-road AD systems. Feature maps, parametric free space maps, interval maps, occupancy grid maps, elevation maps, the stixel world, multi-level surface maps, voxel grids, meshes, and raw sensor data models are presented and compared in this regard.
Zusammenfassung
Eine der Schlüsselherausforderungen des automatisierten Fahrens liegt in der Wahrnehmung und Repräsentation der Fahrumgebung. Daten einer Vielzahl von unterschiedlichen Informationsquellen wie beispielsweise verschiedenen Fahrzeugumfeldsensoren, externen Kommunikationsschnittstellen sowie digitalen Karten müssen in ein konsistentes Umfeldmodell überführt werden, das als generische Abstraktionsschicht zu den Fahrfunktionen dient. Der Übersichtsartikel fasst verschiedene Ansätze aus diesem Gebiet zusammen. Der Schwerpunkt liegt hierbei auf metrischen Repräsentationen der statischen und dynamischen Fahrumgebung für automatisierte Straßenfahrzeuge. Merkmalskarten, parametrische Freiraumkarten, Intervallkarten, Belegungsgitterkarten, Höhenkarten, die Stixelwelt, Mehrebenen-Oberflächenkarten, Voxelgitter, Meshkarten sowie Rohsensordatenmodelle werden vorgestellt und miteinander vergleichen.
About the author
Dr.-Ing. Matthias Schreier was with the Control Methods and Robotics Laboratory, TU Darmstadt until May 2015, where he has been responsible for the environment representation, prediction, and criticality assessment within the driver assistance system development PRORETA 3, a joint cooperation with the Continental AG. Afterwards, he joined the Advanced Engineering department of Continental Teves AG & Co. oHG and continued his work on perception, environment modelling, and scene understanding for automated driving. He is a recipient of the IEEE ITSS Best Ph.D. Dissertation Award 2016 (#2) as well as the Best Dissertation Award 2016 of the Department of Electrical Engineering and Information Technology, TU Darmstadt.
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