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Efficient Graph-Based V2V Free Space Fusion

Luthardt, Stefan ; Han, Chao ; Willert, Volker ; Schreier, Matthias (2017):
Efficient Graph-Based V2V Free Space Fusion.
pp. 985-992, Darmstadt, 2017 IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, CA, USA, June 11-14, 2017, [Conference or Workshop Item]

Luthardt_IV_2017_PFS_Fusion.pdf - Accepted Version
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Item Type: Conference or Workshop Item
Title: Efficient Graph-Based V2V Free Space Fusion
Language: English

A necessary prerequisite for future driver assistance systems as well as automated driving is a suitable and accurate representation of the environment around the vehicle with a sufficient range. To extend the range of the environment representation, sharing the environment detections of multiple vehicles via vehicle-to-vehicle (V2V) communication is a promising approach. In this paper, we present a method to fuse shared free space detections from multiple vehicles. The detections are represented as Parametric Free Space (PFS) maps, which are especially suitable for real-time radio V2V-transmission due to their compactness.

A graph-based algorithm to fuse PFS maps is proposed that solves possible contradictions between the maps and incorporates the maps' uncertainty attributes. By solely operating on the contour, the fusion can be carried out by a simple path search in a fusion graph that is constructed from the maps. This results in an efficient method that finds the fusion result within few iterations.

To account for possible errors in the relative poses between the PFS maps, we furthermore present an adapted Iterative Closest Point (ICP) matching to align the maps before the fusion. Therein we employ a modified soft-assign scheme for robust outlier rejection, and incorporate the PFS maps' boundary orientation to improve the matching process. We show the capabilities of our method by presenting results on real test drive data.

Place of Publication: Darmstadt
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics (from 01.08.2022 renamed Control Methods and Intelligent Systems)
Event Title: 2017 IEEE Intelligent Vehicles Symposium (IV)
Event Location: Redondo Beach, CA, USA
Event Dates: June 11-14, 2017
Date Deposited: 16 Jan 2019 12:16
Last Modified: 13 Dec 2022 11:15
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-83584
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/8358
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