NDLI: Toward Perception-Driven Urban Environment Modeling for Automated Road Vehicles

Content Provider	IEEE Xplore Digital Library
Author	Rieken, J. Matthaei, R. Maurer, M.
Copyright Year	2015
Description	Author affiliation: Inst. of Control Eng., Tech. Univ. Braunschweig, Braunschweig, Germany (Rieken, J.; Matthaei, R.; Maurer, M.)
Abstract	Automated driving is a widely discussed topic nowadays. Impressive demonstrations have shown the potentials of vehicle automation. However, many projects in the context of automated driving use a priori data in order to compensate insufficiencies in perceiving and understanding the vehicle's environment. Additionally, in terms of functional safety and redundancy, it is not yet known whether such localization-and map-based approaches are really path breaking. This is the reason why we focus on on-board perception also of the stationary urban environment. While object tracking is a commonly used approach, the combination of grid-based and object-based representations for environment perception is still a research topic. The sufficient perception of lanes and drivable areas is an unsolved issue in urban environment. Several perception modules have to collaborate for a suitable representation of the vehicles' surroundings. In this paper, we present the latest contributions of the project Stadtpilot to a perception-driven modeling of urban environments. We propose a lane detection approach which is based on a grid-based representation of different environmental features. Our approach is able to detect multi-lane structures and it is capable to deal with complex lane structures which are typical of urban roads. The extracted features are stabilized by a tracking module. Additionally, we incorporate a free-space representation which data is not derived implicitly from detected targets, but based on an explicit ground representation. Extensions of our dynamic classification module focus on the start/stop behavior of other road users in order to enhance the completeness of track list (mobile objects) and grid (stationary environment). The presented algorithms run in real-time on a standard PC and are evaluated with real sensor data.
Starting Page	731
Ending Page	738
File Size	1409523
Page Count	8
File Format	PDF
ISSN	21530017
e-ISBN	9781467365963
DOI	10.1109/ITSC.2015.124
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-15
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Roads Vehicle dynamics Feature extraction Heuristic algorithms Surface treatment Road vehicles
Content Type	Text
Resource Type	Article

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