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RISS 인기검색어
- 검색키워드
- 저자 : Punithan Xavier (검색결과 3 건)
- 무료
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- 유료
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Extended Information Overlap Measure Algorithm for Neighbor Vehicle Localization
Punithan, Xavier,Seo, Seung-Woo The Institute of Electronics and Information Engin 2013 IEIE Transactions on Smart Processing & Computing Vol.2 No.4
Early iterations of the existing Global Positioning System (GPS)-based or radio lateration technique-based vehicle localization algorithms suffer from flip ambiguities, forged relative location information and location information exchange overhead, which affect the subsequent iterations. This, in turn, results in an erroneous neighbor-vehicle map. This paper proposes an extended information overlap measure (EIOM) algorithm to reduce the flip error rates by exchanging the neighbor-vehicle presence features in binary information. This algorithm shifts and associates three pieces of information in the Moore neighborhood format: 1) feature information of the neighboring vehicles from a vision-based environment sensor system; 2) cardinal locations of the neighboring vehicles in its Moore neighborhood; and 3) identification information (MAC/IP addresses). Simulations were conducted for multi-lane highway scenarios to compare the proposed algorithm with the existing algorithm. The results showed that the flip error rates were reduced by up to 50%.
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King's Graph-Based Neighbor-Vehicle Mapping Framework
Punithan, M. Xavier,Seung-Woo Seo IEEE 2013 IEEE transactions on intelligent transportation sy Vol.14 No.3
<P>Vehicle localization algorithms aim to provide an accurate location estimation of neighbor vehicles for critical applications in intelligent vehicles. For the initial location estimation, localization algorithms use either Global Positioning System (GPS), radio-based lateration techniques, or both. These techniques suffer from three major issues, namely, flip ambiguities, location information exchange (beacon) overhead, and forged relative location information. The accuracy of these algorithms at the early iterations is primarily affected by flip ambiguities, which in turn result in erroneous initial location estimates. The errors from flip ambiguities are a monotonically increasing function of time and propagated to the subsequent iterations to build an erroneous neighbor-vehicle map. In this paper, we propose a novel GPS-free neighbor-vehicle mapping framework that provides reliable initial relative position estimates of neighbor vehicles and mitigates aforementioned issues. This framework uses presence/absence status information of neighbor vehicles in binary form from a vision-based environment sensor system to associate each vehicle's cardinal location with its identification information, such as media access control (MAC)/Internet Protocol addresses. We represent a vehicle's neighborhood region and neighborhood topology using the Moore neighborhood (MN) and King's graph (KG), respectively, by analyzing a typical vehicle formation in a multilane roadway. We also introduce a KG-based neighborhood information overlap measure (IOM) algorithm for neighbor mapping by exploiting the perspective symmetric properties of the MN. Performance analysis and simulation results show that the proposed algorithm builds an accurate relative neighbor-vehicle map and outperforms trilateration- and multilateration-based methods in mitigating flip ambiguities and location information exchange overhead.</P>
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