Takafumi ITO Takahiro NAITO Takeshi KAWASHIMA Harutsugu FUKUMOTO

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1 Takafum ITO Takahro NAITO Takesh KAWASHIMA Harusugu FUKUMOTO Toru YAMAGUCHI We propose new navgaon sysem neracs wh drver. By predcng drver s nenon, he sysem can make adequae gudance n consderaon of he nenon. In hs paper, we show he model for predcng drver s urnng nenon a nex nersecon and verfy ha he model predcs he nenon on drvng smulaor. Our model enables he navgaon sysem o undersand drver s nenon and o mprove communcaon and performance. Key words : Predcon, Car navgaon sysem, HMI In order o make vehcle more comforable and safer, advanced elecronc sysems such as car navgaon, lane keep asssance and auomac brake have been evolved. 1) Wh he advanced elecronc sysems, a vehcle s changng from he machne operaed by drver o he nellgen sysem neracs wh drver. For neracng wh drver, wll be mporan ha vehcle undersands drver s nenon and operaes accordng o he nenon. 2) In hs paper, we propose he neracve navgaon sysem, whch makes adequae gudance accordng o drver s nenon, and show he model of predcon drver s nenon for he sysem. In presen, a navgaon sysem gudes a roue by road and own vehcle nformaon such as map, poson and speed. However, o make a nellgble and frendly gudance, he sysem should be desgned o nclude human facors. 3) 4) For example, n approachng o he nersecon where a navgaon sysem guded o urn, f he drver has no undersandng of he gudance or msses he urnng nersecon, he sysem makes an addonal gudance once agan. For operang n consderaon of drver s suaon, he sysem requres he ably of predcng drver s nenon. In order o realze he sysem, we have developed he model of predcng drver s urnng nenon from drvng behavor, own vehcle nformaon and surroundng suaon. In real world, drver recognzes surroundng suaon and operaes pedals, seerng wheel, ec. o change he surroundng suaon accordng o own nenon. Therefore, we have o recognze boh drvng behavor and surroundng suaon o predc he nenon. 5) Through hs concep, we have developed he model consss of 2 phases, frs phase s recognon of drvng behavor, second phase s nference from surroundng suaon. The model s shown n and we wll explan each phase below. Drver s operaon Car nformaon KNOWLEDGE COMPARISON Recognon of drvng behavor Surroundng nformaon from camera, sensor,...ec REPRESENTATION INFERENCE Inference from surroundng suaon Fg. 1 Model for predcng drver s nenon Drver s nenon In hs phase, we recognze he drver s preparng behavor arranged for nenon such as urnng, soppng and changng he lane from drver s operaon and vehcle nformaon. The preparng behavor has ac feaures such as amoun of brake pedal, acuang me and releasng me. To recognze n consderaon of he feaures, we represen he preparng behavor as some

2 paerns kep he nformaon of amoun and mng and compare hese paerns and acual drvng behavor. In, KNOWLEDGE consss of some preparng behavor paerns and COMPARISON s o deec preparng behavor wh he paern. In hs phase, we verfy he resul of frs phase from he surroundng suaon. If drver s preparng behavor deeced n frs phase s no suable o now surroundng suaon, he deecon s rejeced as beng mpraccal. In, REPRESENTATION exracs he nformaon of surroundng suaon such as own vehcle s near he nersecon, oher vehcle s overakng and sgnal s red from elecronc sysems ncludng laser radar, navgaon and mage processng hrough he camera. INFERENCE judges wheher preparng behavor s suable o he nformaon. For example, f COMPARE deecs urnng preparng behavor and REPERESENTATION exracs near he nersecon, INFERENCE s sure ha drver s nenon s urnng. In hs phase, we mus prepare some paerns o recognze ndvdual drver s behavor ha has dfferen feaures. Therefore, we recorded 1 people s drvng daa ha consss of an acceleraor pedal operaon, a brake pedal operaon and own vehcle speed. Afer ha, we exraced 8 samples of approachng o nersecons durng 1 seconds and classfed hem no some groups wh smlar feaures by k-means cluserng. Consequenly, we acqured 5 groups and generaed he paerns represenng each group by averagng he samples n he group. KOWLEDGE s consued of he 5 paerns shown n. The vercal axs represens a normalzed Wh he expermen on he drvng smulaor ( ), we have consruced he model of predcng drver s urnng nenon a nex nersecon for he neracve navgaon sysem. We now explan each phase of he model. Fg. 2 The expermen on he drvng smulaor Normalzed value Normalzed value Paern 1.8 Paern Tme [s] 1. Paern 4.8 Paern Tme [s] Normalzed value Normalzed value Tme [s] Tme [s] Normalzed value Fg. 3 Paerns of drver s preparng behavor for urnng Paern Tme [s] Amoun of acceleraor Amoun of brake Own vehcle speed

3 value from. o 1.. The horzonal axs represens me o nersecons durng 1 seconds and samplng s n every.5 seconds. For example, he s he ypcal case appeared on many nersecons and he s oher case ha drvers sep brake on wce. In order o deec preparng behavor, we compue he smlary beween each paern and acual drvng behavor. The smlary s compued by: S m, r = m,r where S denoes he dsance beween he subse of paern m, from me r- *n unl me r, and acual drvng behavor a me. v, b and a denoe he normalzed values of drvng behavor a me, own vehcle speed, amoun of m brake pedal and amoun of acceleraor pedal. V rm, B rm and A r denoe he value of paern m a me r. In hs work, seng =.5 seconds and n = 1, smlary s durng 5 seconds. m,r Afer ha, he mnmum of all S s compued by: where smaller S ' means hgher smlary. The goal s herefore o compue: where S denoes he smlary a me, oupued from COMPARE n and max(s T) ' denoes he maxmum of S n pas. n = ( v * V r * ' ) + ( b * B r * ) + ( a * S = mn( R S, ) m M, r R T M A ' ' ' S = (max( S ) S ) / max( S ) T r * For predcng he drver s urnng nenon, mus be mporan o exrac near he nersecon from surroundng suaon. However, such nformaon s an absrac concep and no be able o been represened wh a clear defnon. Therefore wh Fuzzy funcon, we ry o change he dsance o he nersecon, acqured by a navgaon sysem, no he concep of near he nersecon. llusraes o he fuzzy funcon ha represens a relaonshp beween he relably of near he nersecon and he dsance o he nersecon. We desgned ha he relably ncreased from 15 o 1 T ) (1) (2) (3) meers. Snce, n he expermen, drvers ofen gazed a he nersecon a such a dsance, we consdered ha drvers perceved he suaon of near he nersecon a he dsance. However, hs funcon may be dependen on surroundng suaon. If we use anoher scenaro on drvng smulaor, we expec hs dsance changes. Fnally, we compue he relably of urnng by: P = S f() (4) where P denoes he relably of urnng a me, S he smlary of preparng behavor a me and f() he relably of near he nersecon a me. 1. Relably Dsance o he nersecon [m] Fg. 4 Relably of near he nersecon We smulaed he model on he drvng smulaor and verfed ha he model could predc drver s nenon. 6) The resul s llusraed n. The vercal axs represens he relably of urnng, and he horzonal axs represens smulaon me. The flled recangles represen he range of he nersecon where a drver urned, and he empy recangle represens he range of he nersecon where a drver wen sragh. The sold lne represens he resul of INFERENCE wh consderaon of surroundng suaon, and he doed lne represens he resul of COMPARISON wh recognon he drvng behavor. The sold lne shows he relably ncreases grealy only n fron of flled recangles. In conras, he doed lne shows ncreases even excep for he flled recangles. Ths resul shows our model have succeeded n predcng drver s urnng nenon wh nference from surroundng suaon.

4 Relably of urnng The recognon rao s shown n. Ths resul s n he case ha he relably over.5 s judged o be urn a nex nersecon. In, learned drvers mean ha her samples, whou verfcaon daa, are used o consruc KNOWLEDGE and un-learned drvers mean no used. The recognon rao s 82.4% even for unlearned drvers. shows he mng ha he model predcs correcly for un-learned drvers. The vercal axs represens he own vehcle speed a he me, and he horzonal axs represens he dsance o he nersecon hen. As he speed s faser, he model ends o predc far away from he nersecon. The approxmae lne shows ha we can recognze drver s nenon a 8.9 meers n approachng a 6 klomeers an hour. The dsance s far han 3 meers, whch s he suable mng of urnng wnker n Japanese drvng school. Dsance o nersecon [m] mn3s 3mns 3mn3s Smulaon me Fg. 5 The resul of predcng nenon Table 1 The recognon rao of predcng nenon Learned drvers Un-learned drvers Recognon rao 82.4(%) 94.1(%) Own vehcle speed [km/h] Fg. 6 Tmng of predcng nenon In hs chaper, we evaluae wheher our model s avalable for he neracve navgaon sysem. Wh he expermen on drvng smulaor, we examned he requred 7) 8) mng o gude a roue based on our predcon. In he expermen, we showed drvers he marker ndcaes he drecon of urnng a dfferen mng and observed drver s reacons o each maker. We made drvers evaluae each marker no 3 levels, safey, lae and danger. The safey means drver urned wh he margn, he lae means drver urned bu busly and he danger means drver urned wh abrup seerng or could no urn. shows he resul and he approxmae lne of. The feld above he lne means earler mng ha our model can no predc. Snce danger doesn exs n hs feld, our predcon model mus be avalable for gudance. However, one of lae exss above he lne and danger exss n he neghborhood of he lne. Therefore we have o mprove on he predcon mng. Dsance o nersecon [m] Safey Lae Danger Fg. 7 Evaluaon for predcng mng Own vehcle speed [km/h] We proposed he concep of he neracve navgaon sysem and explaned our predcng model for he sysem. Moreover, we verfed he model was able o predc nenon on he drvng smulaor and examned he effec o he navgaon sysem. In he resuls, he recognon rae for learned drvers s 94.1 %, whch s hgher han he rae for un-learned drvers 1% or more. Therefore, we wll develop he model o learn ndvdual drvng behavor

5 auomacally and also mprove predcon mng wh consderaon of he ndvdual feaures. Furhermore, o predc more complex nenon, we wll nclude human facors such as gaze, face drecon and expresson n KNOWLEDGE, and research advanced mage processng o undersandng surroundng suaon well. Fnally we wll nsall he model on vehcle and verfy n he real world. In parallel, we wll fnd he effecve usage of our predcon model for varous sysems. Ths research has been suppored by Tokyo Meropolan Insue of Technology, Deparmen of Elecronc Sysems Engneerng. 1) Sao K, Issues of Drver Suppor Sysems for Praccal Use, JARI Research journal, Vol.23, No.11, pp ) Yamaguch T, Murakam H, Human Cenered Sysem and Nework Inellgence, SICE, PG13/2/-33 3) Sakabe M, Ohno H, Developmen of Collson Sysem Based on he Predcon of Drver s Brakng Acon, AISIN TECHNICAL REVIEW, Vol.7, (23), pp ) Sekguch M, Takayama K, Maeda Y, Nao H, Inellgen for car navgaon sysem, FUJITSU TEN TECHNICAL REVIEW, Vol.17, No.1, pp ) Akamasu M, Drvng Asssance Sysem Based On Drver s Behavoral Characerscs, JSAE SYMPOSIUM, No.6-4, pp ) Kuge N, Yamamura T, Shmoyama O, Lu A, A Sudy on Drver Behavor Recognon Model Usng a Drvng Smulaor, JSAE journal, Vol.31, No.3, pp ) Sasak M, An Expermenal Sudy on Drver s Sense of Relef for Auomac Brake Sysem, JARI Research journal, Vol.23, No.11, pp ) Wakasug T, Sudy of Warnng Tmng for Sde Obsacle Warnng Sysems Based on Drver Lane Change Maneuvers, JARI Research journal, Vol.23, No.11, pp