Real-time Path Prediction and Grid-based Path Modeling Method Using GPS

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1 Iteratioal Joural of Applied Egieerig Research ISSN Volume 12, Number 20 (2017) pp Research Idia Publicatios. Real-time Path Predictio ad Grid-based Path Modelig Method Usig GPS Seogwo Mi *, Jog-Yog Lee** ad Kye-Dog Jug ** * Departmet of Iformatio System, Kwagwoo Uiversity Graduate School of Iformatio Cotets, 20 Kwagwoo-ro, Nowo-gu, Seoul 01897, Korea ** Igeium College of liberal arts, KwagWoo Uiversity, 20 Kwagwoo-ro, Nowo-gu, Seoul 01897, Korea *Correspodig author: Kye-Dog Jug Abstract I this paper, we propose a real-time path predictio ad a grid based path modelig method usig GPS. path predictio method estimates the ext locatio by applyig the RLS algorithm to the GPS. This eables ear-real-time path predictio for the u-modeled path. ad Grid based path modelig method divides the collected path ito the cell ad trasforms the path i the cell ito a equatio. It provides the same role as the existig persoal path model. However, idividual cells ca be used for path predictio of other pedestrias. Also, the path trasformed ito the equatio ca elimiate the coordiates error caused by the GPS update delay. Keywords: V2P, GPS, RLS, Pedestria Path Predictio, Predictio of Pedestria Trajectories INTRODUCTION Accordig to OECD, the risk of traffic accidets has icreased as the umber of vehicles icreases each year. to reduce this problem, the automotive idustry is spreadig the AEB system that detects ad brakes ahead objects. However, the AEB system based o cameras ad ifrared sesors is ot efficiet of the ight ad blid spot. Recetly, research is uderway to improve the characteristics of cameras ad ifrared sesors [1-3]. V2X (Vehicle to Everythig) is a commuicatio techology that exchages iformatio with vehicles ad ifrastructure where IoT etwork is built. Amog them, V2P (Vehicle to Pedestria) is that focuses o pedestrias ad a earby vehicle ad a pedestria perceive each other through a wireless commuicatio etwork ad exchage iformatio. Recetly, studies are uderway to improve the disadvatages of AEB system by applyig pedestria path predictio techique to V2P [4,5]. Existig pedestria path predictio method used the path model to predict the fial destiatio. The path model pre-collects data such as a startig poit, a destiatio, a movig path, ad eviromet variable(time, date, etc) to a specific perso. Although there have bee may studies to ifer pedestrias' destiatios by applyig various algorithms to them, there is a problem that it is difficult to apply them to uspecified persos because it is a persoal model. It also does ot support predictios for u-modeled paths. This method is ot suitable for V2P. I this paper, we propose a real-time path predictio ad gridbased path modelig method usig GPS which is a improve of the existig path predictio method to V2P. The proposed method collects the GPS of the pedestria i real time ad predicts the path withi about 2 secods. We used the RLS (Recursive least squares) algorithm, which is oe of the adaptive algorithms, to predict the path. The grid based path modelig method divides the collected path ito the grid ad trasforms the path of each cell ito a equatio. The compositio of this paper is as follows. Sectio 2 describes the existig model-based pedestria path predictio, ad Sectio 3 describes the proposed method. I Sectio 4, coclusios ad future research issues are explaied. Pedestria Path Predictio Recetly, as the peetratio rate of smart phoes has icreased, studies are beig coducted to predict the pedestria path usig sesors built ito smart phoes. The pedestria path predictio method is a field of persoalizatio services that is beig studied uder the precoditio that most people sped a repetitive routie. The existig method predicts the route through the followig process. First, we aalyze the collected data to fid the patter ad create the model. Ad we apply this to the curret state to predict the destiatio. Figure 1 shows the Actio Logger developed by MDS, which was collected the path o the period of time ad geerated the model based o this path 9997

2 Iteratioal Joural of Applied Egieerig Research ISSN Volume 12, Number 20 (2017) pp Research Idia Publicatios. (a) Figure 1. The path collected via GPS ad the modeled major path (b) Figure 2. Flow chart of the path predictio method which improves the existig method The collected path icludes ot oly GPS data but also various eviromet variables (day, date, time, directio, etc.), ad various path models are created based o the eviromet variables. The collected path by Figure 1-(a) icludes ot oly GPS data but also various eviromet variables which day, time, date, etc. The model that is geerated based o the collected paths icludes eviromet variables ad paths. The geerated model is used to predict the route of the pedestria. It ca select differet destiatios by eviromet variables, eve if you move the same path. PEDESTRIAN PATH PREDICTION AND GRID-BASED PATH LEARNING USING GPS Existig path predictio method focus o predictig destiatios usig path models. This method is ot suitable for predictig paths to uspecified persos. Below is a problem with the existig method. (A). Difficult to predict for u-modeled paths (B). A model of persoality that caot be applied to uspecified persos. I this paper, we propose 'Pedestria Path Predictio ad Grid - based Path Modelig Method' to improve existig problems. Path Predictio Usig RLS(Recursive least squares) The RLS algorithm is a kid of adaptive filter algorithm that ca perform 'forward predictio' that predicts future data with historical data. I particular, it ca be advatageously used to predict a path i a liear form. We applied this algorithm to GPS providig simple latitude ad logitude. These coordiates cotai various iformatio such as the speed ad directio of the pedestria's movemet. The basic RLS algorithm used i this paper is as follows. datas = {(x, y) (x i, y i ), (x i+1, y i+1 ),, (x 1, y 1 ), (x, y )} f(x +1 ) = y +1 = αx +1 + β (1) α = β = x i iy i x i i i y i x 2 i ( i xi ) 2 i x +1 (2) i y i α i x i (3) x

3 Iteratioal Joural of Applied Egieerig Research ISSN Volume 12, Number 20 (2017) pp Research Idia Publicatios. Figure 3. The Cartesia coordiate for latitude ad logitude expressed i the time axis We apply the RLS algorithm to the GPS of the pedestrias collected i real time to predict the path. GPS provides latitude ad logitude, ad i order to predict the path, it eeds to be chaged to iformatio about time. Figure 3 shows the process of separatig collected latitude ad logitude with respect to time. As show i Fig. 3, it ca be see that the path calculated from the actual pedestria path ad the predicted path are similar. This meas that the liear predictio of the RLS algorithm is suitable for the path of the pedestria. Figure 4 shows the predicted path i the curve sectio. d avg = d +d 1 +d 2 +d 3 4 θ = ata((l p about 1 lat L 1 lat) /(L p about 1 lo L 1 lo )) ata((l lat L 1 lat )/(L lo L 1 lo )) If d avg 0.9 > d p or d avg 1.1 < d p (4) ( L p about lo cos θ siθ ) = ( L p about lat siθ cosθ ) (L p about lo ) (5) L p about lat (a) Figure 4. Error of curve sectio It ca be see that the error rate is sigificatly higher tha that of Fig. This is a error that occurs whe the GPS used i predictig the measured GPS ad pedestria path has differet directios whe the pedestria switches directio. (A), it is difficult to improve the path of the pedestria because it ca ot predict the directio chage of the pedestria. However, (b) is the error caused by the liear predictio of RLS. We applied the followig algorithm to improve this problem. (b) Whe L p about is predicted by {L 3,, L }, if Eq. (4) is satisfied, L p about is corrected by usig Eq. (5). Whe L p about is corrected, the L p+1 about is also automatically corrected by the RLS algorithm. Figure 6 shows the coordiates before correctio ad after correctio Figure 5. The predicted path corrected by Equatio (5). d = distace(l, L 1 ) d p = distace(l, L p about 1 ) Figure 6. Actual ad Predicted Paths 9999

4 Iteratioal Joural of Applied Egieerig Research ISSN Volume 12, Number 20 (2017) pp Research Idia Publicatios. Number of GPS markers Table 1. Measured path iformatio Total Distace Movig Time m 2mi 3sec Table 2. Compariso of actual ad predicted paths Predictio Path: T+1 Predictio Path: T+2 Miimum Error Maximum Error Average 0.05 m 2.3 m 0.52 m 0.22 m 4.2 m 1.1 m Eviromet variables are used for path reasoig, ad cell relatioships are defied as the cell-to-cell movemet Grid-based Path Modelig Method We predict the path of the pedestria usig the methods preseted above. Usig RLS, we predict the short path of a pedestria i real time ad use a grid-based model to predict a path a little further tha the previous oe. Figure 9 shows the method of predictig the route of the curret pedestria i the accumulated model by the method proposed i this study Grid-based Path Modelig Method Grid-based path learig is a improved method that ca apply the existig model method to a uspecified perso. We applied the grid to the covetioal method of modelig the path to the destiatio ad added the path modelig for each cell. The grid modelig method divides the collected path ito cells as show i Figure 7 ad models the path for each cell. Figure 9. Path Predictio Method Figure 7. Grid-based pedestria path modelig The modelig process of a cell goes through processes such as path expressio usig equatios, evirometal variables such as time ad day of the week, ad the relatioship of cells. The path expressio usig the equatio is a process of covertig a path ito a equatio usig a plurality of GPS located i a cell. The path is expressed by the maximum N ( (the umber of GPS i the cell) -1) equatio from the first order equatio, ad the equatio with the correlatio coefficiet (R) closest to 1 is used. 'Cell Group' is a set of cell codes used whe searchig for a model with the same path. It cosists of oe or more cell codes. The model searched through the cell group udergoes a additioal classificatio process accordig to each eviromet variable. The fial classified model through the evirometal variables is compared with the path equatio of the cell group, ad the pedestria path is predicted usig the model with the high matchig rate. CONCLUSION I this paper, we proposed a real-time path predictio ad grid - based path modelig method usig GPS. I the proposed method, it is possible to predict i real time by applyig the RLS algorithm to a path that has ot yet geerated a model. I additio, the grid-based path modelig method maitais the characteristics of the existig path model, ad the subdivided model ito cells ca be used i uspecified persos. Ad we elimiated the error caused by the GPS delay by expressig the path as a equatio. Table 1 compares the covetioal path predictio model with the proposed path predictio model. Figure 8. Path represeted by the equatio 10000

5 Iteratioal Joural of Applied Egieerig Research ISSN Volume 12, Number 20 (2017) pp Research Idia Publicatios. Table 3. Compare with Existig Method ad Proposed Method Existig Method Proposed Method Purpose Estimate destiatio Estimate the movemet path after Process (1). Pre-Collect Path (2). Pre-Create Model (3). Predict Destiatio (1). Collect Path (2). Predict Path & Create the Previous Path Model (3). Predict After Path Characteristic Persoalized path model Uiversal path model Uspecified perso Not show Predictio of the path usig the grid method. Without Costructed Path model Usig ear path model Real-time predictio for a short distace The proposed method ca be applied to the V2P eviromet where the path of the uspecified perso should be predicted. I future, it plas to carry out research to reduce the risk of pedestrias' traffic accidets by combiig with short-rage wireless commuicatio such as Bluetooth ad Zigbee. REFERENCES [1] PYYKÖNEN, P., et al. IoT for itelliget traffic system. I: Itelliget Computer Commuicatio ad Processig (ICCP), 2013 IEEE Iteratioal Coferece o. IEEE, p [2] DIEWALD, Stefa, et al. Mobile Device Itegratio ad Iteractio with V2X Commuicatio. I: World Cogress o Itelliget Trasport Systems: 22/10/ /10/ [3] DIEWALD, Stefa, et al. DriveAssist: a V2X-based driver assistace system for Adroid. I: Koferez Mesch & Computer: 09/09/ /09/2012. Oldeburg Wisseschaftsverlag, p [4] BAGHERI, Mehrdad; SIEKKINEN, Matti; NURMINEN, Jukka K. Cellular-based vehicle to pedestria (v2p) adaptive commuicatio for collisio avoidace. I: Coected Vehicles ad Expo (ICCVE), 2014 Iteratioal Coferece o. IEEE, p [5] LEWANDOWSKI, Adreas, et al. Desig ad performace aalysis of a IEEE V2P pedestria protectio system. I: Wireless Vehicular Commuicatios (WiVeC), 2013 IEEE 5th Iteratioal Symposium o. IEEE, p [6] Yuhyoug Hwag, Yousic Lee, Seoggeu Shi, Hyuckkee Lee, Daeryog Ah. Pedestria Trajectory Estimatio for the Collisio Threat Assessmet. The Korea Society of Automotive Egieers, p [7] KIM, Je-Mi; BACK, Hea-Jug; PARK, Youg-Tack. GPS based Smartphoe Users' Persoal Route Model Learig ad Movig Route Predictio. Joural of KIISE: Software ad Applicatios, 2012, 39.1: [8] SCHNEIDER, Nicolas; GAVRILA, Dariu M. Pedestria path predictio with recursive Bayesia filters: A comparative study. I: Germa Coferece o Patter Recogitio. Spriger, Berli, Heidelberg, p [9] KELLER, Christoph G.; GAVRILA, Dariu M. Will the pedestria cross? A study o pedestria path predictio. IEEE Trasactios o Itelliget Trasportatio Systems, 2014, 15.2: [10] KOOIJ, Julia Fracisco Pieter, et al. Cotext-based pedestria path predictio. I: Europea Coferece o Computer Visio. Spriger, Cham, p [11] BROGAN, David C.; JOHNSON, Nicholas L. Realistic huma walkig paths. I: Computer Aimatio ad Social Agets, th Iteratioal Coferece o. IEEE, p [12] MØGELMOSE, Adreas; TRIVEDI, Moha M.; MOESLUND, Thomas B. Trajectory aalysis ad predictio for improved pedestria safety: Itegrated framework ad evaluatios. I: Itelliget Vehicles Symposium (IV), 2015 IEEE. IEEE, p [13] DATTA, Trisha; JAIN, Shubham; GRUTESER, Marco. Towards city-scale smartphoe sesig of potetially usafe pedestria movemets. I: Mobile Ad Hoc ad Sesor Systems (MASS), 2014 IEEE 11th Iteratioal Coferece o. IEEE, p