Research and Implementation of Road Match and Trace Replay Algorithm

Gao, Haihui; Jia, Kebin; He, Jie

doi:10.1109/mmit.2008.106

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…The data from Antilock Braking System (ABS) sensors, a differential Global Positioning System (GPS) receiver and an accurate digital roadmap are used in the multi-sensor fusion strategy to illustrate the performances of the approach. Haihui GAO et al [5] proposed a road match and trace replay algorithm. "Point-to-point" road matching algorithm is used to match the deviation position point to the middle line of road in electronic map.…”

Section: Multi-sensor Fusion Methods Using Kalman Filter To Improve Lomentioning

confidence: 99%

Multi-sensor fusion method using kalman filter to improve localization accuracy based on android smart phone

Wang

Liang

Geng

et al. 2014

2014 IEEE International Conference on Vehicular Electronics and Safety

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Android smart phone can be used in ITS (Intelligent Transportation Systems) to obtain people and vehicle's location since it is integrated with GPS, direction sensor and acceleration sensor. Because the GPS built in smart phone always has an error of dozens of meters, improving the positioning accuracy is necessary before introducing it into ITS. This paper proposed an approach to improve the accuracy to the street level and get a smooth trajectory without jump points. It is convenient for everyone to use it because almost everyone has a smart phone. Firstly, road-matching algorithm is used to improve the localization accuracy to street level; secondly, speed and direction information are introduced to better reflect the real trajectory; thirdly, Kalman filter is used to eliminate the jump points and make the trajectory smooth; finally, the optimal result obtained from the process of Kalman filter is interpolated to reflect more details. The experiment result shows that the approach is effective.

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Section: Multi-sensor Fusion Methods Using Kalman Filter To Improve Lomentioning

confidence: 99%

Multi-sensor fusion method using kalman filter to improve localization accuracy based on android smart phone

Wang

Liang

Geng

et al. 2014

2014 IEEE International Conference on Vehicular Electronics and Safety

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“…However few researches focused on trace interpolation algorithm, which was aimed to solve the longinterval between records problem and to provide a "real time" vehicle trace. Most published works [8] about vehicle trace interpolation use uniform interpolation method. It assumes that the vehicle moves with the same velocity or with a uniform acceleration/deceleration velocity between two consecutive real records.…”

Section: Introductionmentioning

confidence: 99%

Trace Interpolation Algorithm Based on Intersection Vehicle Movement Modeling

Shen¹,

Xue

2010

WSN

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Real vehicle tracking data play an important role in the research of routing in vehicle sensor networks. Most of the vehicle tracking data, however, were collected periodically and could not meet the requirements of real-time by many applications. Most of the existing trace interpolation algorithms use uniform interpolation methods and have low accuracy problem. From our observation, intersection vehicle status is critical to the vehicle movement. In this paper, we proposed a novel trace interpolation algorithm. Our algorithm used intersection vehicle movement modeling (IVMM) and velocity data mining (VDM) to assist the interpolation process. The algorithm is evaluated with real vehicle GPS data. Results show that our algorithm has much higher accuracy than traditional trace interpolation algorithms

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Research and Implementation of Road Match and Trace Replay Algorithm

Cited by 2 publications

References 3 publications

Multi-sensor fusion method using kalman filter to improve localization accuracy based on android smart phone

Multi-sensor fusion method using kalman filter to improve localization accuracy based on android smart phone

Trace Interpolation Algorithm Based on Intersection Vehicle Movement Modeling

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