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Proceedings Paper

Track-to-track association for object matching in an inter-vehicle communication system
Author(s): Ting Yuan; Tobias Roth; Qi Chen; Jakob Breu; Miro Bogdanovic; Christian A. Weiss
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Paper Abstract

Autonomous driving poses unique challenges for vehicle environment perception due to the complex driving environment the autonomous vehicle finds itself in and differentiates from remote vehicles. Due to inherent uncertainty of the traffic environments and incomplete knowledge due to sensor limitation, an autonomous driving system using only local onboard sensor information is generally not sufficiently enough for conducting a reliable intelligent driving with guaranteed safety. In order to overcome limitations of the local (host) vehicle sensing system and to increase the likelihood of correct detections and classifications, collaborative information from cooperative remote vehicles could substantially facilitate effectiveness of vehicle decision making process. Dedicated Short Range Communication (DSRC) system provides a powerful inter-vehicle wireless communication channel to enhance host vehicle environment perceiving capability with the aid of transmitted information from remote vehicles.

However, there is a major challenge before one can fuse the DSRC-transmitted remote information and host vehicle Radar-observed information (in the present case): the remote DRSC data must be correctly associated with the corresponding onboard Radar data; namely, an object matching problem. Direct raw data association (i.e., measurement-to-measurement association - M2MA) is straightforward but error-prone, due to inherent uncertain nature of the observation data. The uncertainties could lead to serious difficulty in matching decision, especially, using non-stationary data. In this study, we present an object matching algorithm based on track-to-track association (T2TA) and evaluate the proposed approach with prototype vehicles in real traffic scenarios. To fully exploit potential of the DSRC system, only GPS position data from remote vehicle are used in fusion center (at host vehicle), i.e., we try to get what we need from the least amount of information; additional feature information can help the data association but are not currently considered. Comparing to M2MA, benefits of the T2TA object matching approach are: i) tracks taking into account important statistical information can provide more reliable inference results; ii) the track-formed smoothed trajectories can be used for an easier shape matching; iii) each local vehicle can design its own tracker and sends only tracks to fusion center to alleviate communication constraints. A real traffic study with different driving environments, based on a statistical hypothesis test, shows promising object matching results of significant practical implications.

Paper Details

Date Published: 2 September 2015
PDF: 10 pages
Proc. SPIE 9596, Signal and Data Processing of Small Targets 2015, 959609 (2 September 2015); doi: 10.1117/12.2187108
Show Author Affiliations
Ting Yuan, Mercedes-Benz Research and Development North America, Inc. (United States)
Tobias Roth, Mercedes-Benz Research and Development North America, Inc. (United States)
Qi Chen, Mercedes-Benz Research and Development North America, Inc. (United States)
Jakob Breu, Mercedes-Benz Research and Development, Daimler AG (Germany)
Miro Bogdanovic, Mercedes-Benz Research and Development, Daimler AG (Germany)
Christian A. Weiss, Mercedes-Benz Research and Development, Daimler AG (Germany)


Published in SPIE Proceedings Vol. 9596:
Signal and Data Processing of Small Targets 2015
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)

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