Fusion of Map and Sensor Data in a Modern Car Navigation System

Obradović, Dragan; Lenz, H.; Schupfner, Markus

doi:10.1007/s11265-006-9775-4

Cited by 64 publications

(26 citation statements)

References 7 publications

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“…Digital map contains geographic information about roads, buildings, point of interest, etc. but also the attribute information describing them (Obradovic, 2006). Information can also be supplemented by the Internet connection where the driver receives traffic information.…”

Section: Car Navigationmentioning

confidence: 99%

Implementation of Voyage Assistant Module in Mobile Navigation System for Inland Waters

Kazimierski¹,

Zaniewicz²,

Hyla³

2017

TransNav

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Route monitoring is one of the most important functions in modern navigation systems. In case of marine merchant ship's it usually part of IMO-standardized solutions like ECDIS or INS. In inland waters Inland ECDIS is implemented. There is however large group of ships which do not have to follow standardized solution, like recreational ships. The skippers of these boats more and more often are using mobile devices. The paper presents considerations on implementation of voyage assistant module in mobile navigation system in inland waters. Main problems are defined solutions for them are proposed. In general proposed approach is based on mobile cartography like in car navigation and derives also from water navigation systems. The verification research are described and commented.

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Section: Car Navigationmentioning

confidence: 99%

Implementation of Voyage Assistant Module in Mobile Navigation System for Inland Waters

Kazimierski¹,

Zaniewicz²,

Hyla³

2017

TransNav

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“…Topological map-matching (Yin and Wolfson 2004) algorithms consider the road connectivity and contiguity along with geometric features. Advanced techniques such as Kalman Filters (Obradovic, Lenz, and Schupfner 2006), have also been effectively used for map-matching.…”

Section: Map Matching Problemmentioning

confidence: 99%

Map stream: Initializing what-if analyses for real-time symbiotic traffic simulations

Sunderrajan

Aydt

Cai

et al. 2014

Proceedings of the Winter Simulation Conference 2014

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In the context of a city-scale symbiotic traffic simulation, real-time data about the location of many vehicles are obtained in the form of a continuous data-stream. In this paper, we present a scalable solution for performing map-matching using sliding-windows over a GPS data-stream onto a digital road network for initializing the what-if analysis process involved in symbiotic simulations. We focus on the optimizations performed to ensure that the latency associated with the map-matching process is low while maintaining a high degree of accuracy. Experimental results reveal the range in terms of sampling interval and noise for acceptable reliability and latency.

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“…It has been demonstrated in [1] that this class gives more improved results than the first one by discarding aberrant cases like a matched point jumping suddenly between parallel roads when considering only the proximity between the GPS point and the road. Other approaches have been applied to map matching like the probabilistic approach [2], fuzzy logic [3] and advanced techniques [4] [6]. All these algorithms fall under the category of local/incremental algorithms that rely on the last matches to evaluate the current one.…”

Section: Introductionmentioning

confidence: 99%

A map matching algorithm based on a particle filter

Mokhtari

Reboul²,

Azmani³

et al. 2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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Map matching is the process of finding a match for each GPS point in a vehicle's trajectory to roads on a digital map. Extensive research has been conducted during the last years yielding many algorithms based on different approaches. One of the challenges that face those algorithms is the interruption of GPS signals that occurs specially in dense urban environments. In these cases on-board sensors like odometers and accelerometers can be used temporarily for positioning, however due to the poor accuracy of these methods, the quality of map matching decreases significantly. In this paper, we propose a method that improves the quality of map matching when GPS signals are not available. This method is based on a particle filter using heading and velocity measurement. We evaluate this method through its integration with an existing topological map matching algorithm. We compare the performances when this algorithm is used alone and when associated with the particle filter.

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Fusion of Map and Sensor Data in a Modern Car Navigation System

Cited by 64 publications

References 7 publications

Implementation of Voyage Assistant Module in Mobile Navigation System for Inland Waters

Implementation of Voyage Assistant Module in Mobile Navigation System for Inland Waters

Map stream: Initializing what-if analyses for real-time symbiotic traffic simulations

A map matching algorithm based on a particle filter

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