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1 Vol.26-ITS-64 No. 26/3/7,a),b),c),d) 24 TTC THW Driver characteristic estimation using vehicle behavior data while preceding vehicle decelerating Chisato Shibata,a) Yurie Iribe,b) Haruki Kawanaka,c) Koji Oguri,d) Abstract: This Paper describes estimation method of the driver s operating characteristics using vehicle information on a self-vehicle and the preceding vehicle. We extracted self-vehicle s information from driving simulator to 24 subjects when preceding vehicle reduced speed through some experiment under various speed and acceleration. Some correlations between the vehicle information extracted by the experiment and the value of the questionnaire about the driver s characteristics were analyzed. We estimate some driver s characteristics from vehicle information with high correlation to driver s characteristics. Finally, we report the estimated results based on the proposed method. As a result, the TTC minimum, the THW minimum and the inter-vehicle distance minimum are positive correlation with careful of the driver characteristics observed, we estimated careful from the vehicle information by using the Bayes theorem. Keywords: Vehicle Information, Driver Characteristics, Preceding Vehicle Following Situation, Correlation Analysis. Aichi Prefectural University,Nagakute-shi Aichi, Japan a) c.shibata@bme.ist.aichi-pu.ac.jp b) iribe@ist.aichi-pu.ac.jp c) kawanaka@ist.aichi-pu.ac.jp d) oguri@ist.aichi-pu.ac.jp c 26 Information Processing Society of Japan

2 [2] [3] [4] [] TTC 2 9 SAS[6] 2 SAS 4 DS 2. SAS 28 ( 2 ) 6 ( ) (PV:Preceding Vehicle) (FV:Following Vehicle) [7] v f v p D Vol.26-ITS-64 No. 26/3/7 c 26 Information Processing Society of Japan 2

3 3.. THW(Time-Head Way ) THW v f () T HW = D v f () THW [8] 3..2 TTC(Time-To-Collision ) TTC v f v p (2) T T C = D v f v p (2) (v r = v f ) TTC THW 2 p x x p y x B y R xy n D t D min T HW t T HW min T T C min Vol.26-ITS-64 No. 26/3/7 t (t = p, x ) 2 t (t = p, x ) 2 2 TTC 3.2 t v f t v pt A t B t D t (= x rt ) T HW t (A = ) p (B > ) x y n 2 2 p x y R xy (3) R xy = B y B x y x (3) 2 DS Velocity [km/h] [-] Vehicle Distance [m] Preceding Vehicle Following Vehicle Throttle Brake Time [s] (FV:Following Vehicle) (PV:Preceding Vehicle) ± DS 2 ( AQUA ) K 3 FullHD 3 () 3 6 DS c 26 Information Processing Society of Japan 3

4 Vol.26-ITS-64 No. 26/3/7 情報処理学会研究報告 2 [m] R = 4 m 図 7 図 2 実験に使用した DS と前方の 4K モニター Velocity [km/h] 2 6 [m] 走行コースの概形.2G.3G.2G R = 4 m R = 4 m Starting point.4g.2g.g.3g G.G.G.G.G.G.G.G Drove Distance [km] 図 8 先行車の速度 加速度変化の一例 走行につき走行開始直後の加速と計測終了前の減速を除い て計 3 回の加減速を行わせた 先行車の走行速度は [km/h] のいずれかであり この速度に対 図 3 運転席正面 図 4 バックミラー して ± の速度変化を.G.G.G の加速度あるいは.2G.3G.4G.G の減速度で加 えた (G=9.8 m/s2 ) なお 実験用シナリオは 3 種類用意し 計測する日ごと に実験用シナリオ (先行車の速度変化のタイミング) は異な るように設定した また 使用する実験用シナリオの順番 も被験者ごとに異なるように設定した 設定した実験用シ ナリオの一例を図 8 に示す 図 8 の横軸は先行車の走行距 図 サイドミラー (左) 図 6 サイドミラー (右) 離 [km] 縦軸は先行車の速度 [km/h] 図中に書き込まれ ている青吹き出しが加速度 赤吹き出しが減速度を表して 計測プロトコル 運転する日によって運転行動が変化する可能性を考慮し て データ計測は被験者ごとに 3 日間行った 実験では DS を用いて高速道路を運転してもらい 先 行車を追従する運転タスクを約 分間行ってもらった いる. 実験結果. 先行車減速時の自車の車両挙動の変化 本研究では 先行車減速時におけるドライバの車両挙動 被験者には実験に先立ち DS の運転に慣れてもらうた に着目した 先行車減速時における被験者の車両挙動の例 めに 実験用シナリオと類似の練習シナリオにより 日目 を図 9 に示す 図 9 の横軸は先行車が減速し始めた時刻を の計測前に 3 分間の練習走行を行ってもらった その後 [s] としたときの時刻 [s] 縦軸は上段のグラフでは黒色が 被験者に 先行車に対して速度を調節しながら追従走行し 先行車の速度 [km/h] 緑色が自車の速度 [km/h] 中段の て下さい という指示を与えてから走行してもらい 車両 グラフではが青色がアクセル量 [-] 赤色がブレーキ量 操作情報 顔動画を記録した また 各被験者は安全運転 [-] 下段のグラフでは黒色が TTC [s] 緑色が THW 自己診断に回答した [s] を表している 図 9 から 先行車の減速に対応してドラ 走行コースとシナリオ イバの車両操作が行われており ドライバは先行車の減速 先行車追従時における減速のデータを計測するために に対応して まずアクセルペダルを放し ブレーキ操作を 本実験では東名高速道路を模擬した道路を使用した 走行 行っていることが確認できる この間の TTC は まず減 コースは前長約 3 km であり 本実験での所要時間は 少し ある最小値を経て再度増加していることがわかる 分間となる 図 7 に走行コースの概形を示す なお R は 曲率半径を表す 被験者には片側二車線の第一車線を走行し 車線変更は 行わないように乗車前に指示した 第一車線には先行車が 台 第二車線には他車が数台 km/h で走行するように 設定をした 先行車にはランダムなタイミングで 回の 26 Information Processing Society of Japan.2 先行車減速時の車両挙動指標とドライバ特性の度合 いとの相関 先行車減速時から 2 秒間の車両挙動に着目し 以下に 示す車両挙動指標を抽出した 安全運転自己診断で診断された運転特性の度合いと先行 4

5 Vol.26-ITS-64 No. 26/3/7 Velocity [km/h] [-] TTC [s] Preceding Vehicle Following Vehicle Throttle Brake Time To Collision Time-Head Way Time [s] THW [s] low tendency group high tendency group TTC min [s] TTC 6. DS DS 6. TTC [] TTC min. % TTC min % TTC min TTC min 24 TTC min 2 TTC min 4 (4 ) 3 (2 ) TTC min 2 4 TTC min 2 s 4 s TTC min TTC min TTC min 6.2 TTC min () P (B T T C min ) = P (B)P (T T C min B) P (T T C min ) P (T T C min ) = P (B)P (T T C min B) (4) +( P (B))P (T T C min B) () T T C min TTC min B P (B) TTC min P (B) () P (B T T C min ) TTC min P (B T T C min ) P (B) () () P (T T C min B) c 26 Information Processing Society of Japan

6 Vol.26-ITS-64 No. 26/3/7 3 p * x x p y x B y * R xy n **.6 D p * * -.4 D x -.76** -.* D min * * -.7 T HW p * * -.4 T HW x -.79** -.* T HW min * * -.8 T T C min * ** -.3 *p <., **p <. P (T T C min B) TTC min P (T T C min B) P (T T C min B) P (T T C min B) P (T T C min B) TTC min p <. TTC min ( % P(TTC min B).. P(TTC min B) ) TTC min TTC min [s] { } (ln T T Cmin µ P (T T C min B) = B ) 2 exp 2πσB T T C min 2σ 2 B 2σ 2 B (6) { } (ln T T C P (T T C min B) = min µ exp B ) 2 2πσB T T C min (7) P (T T C min B) P (T T C min B) 2 2 T T C min P (T T C min B) P (T T C min B) ( 4 ) 3 ( 2 ) P(TTC min ~B) TTC min [s] P(TTC min ~B) c 26 Information Processing Society of Japan 6

7 [ ] Vol.26-ITS-64 No. 26/3/7 D x 38% 9% 24% % D min 33% 8% 33% 7% 3% T HW x 38% 23% 24% % T HW min 29% 8% 38% 7% 3% T T C min 2% 4% 38% 7% 8% [ ] leave one out cross validation 24 TTC min () 24 P (B) % % P (T T C min B) P (T T C min B) TTC min TTC min D x D min T HW x T HW min 6 DS [] DS 7. DS 24 % 2 TTC. TTC 24 DS 8 TTC TTC [],,, Vol.8, No., pp.88-94, 24. [2], (3), 997. [3],,,,,, 2. [4],,,,,, c 26 Information Processing Society of Japan 7

8 Vol.26-ITS-64 No. 26/3/7, 29. [],,, C, Vol.3, No.6, pp.3-9, 2. [6], SAS, 2. [7],,,,,, Vol.62, No.2, pp.9-64, 28. [8],,,,, No.9-, pp.23-26, 2. [9],,,,, Vol.44, No.2, pp.99-28, 28. [],,,,,, ( ) :,, Vol.38, No.2, pp.2-3, 27. [],,,,,, Vol.38, No.4, pp.9-96, 27. c 26 Information Processing Society of Japan 8

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