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1 特別寄稿 ソフトインフラとしてのデジタル地図を活用した自動運転システム Autonomous vehicle using digital map as a soft infrastructure 菅沼直樹 Naoki SUGANUMA 1. はじめに 1) ITS 2) CO 2 3) 4) Door to door Door to door Door to door DARPA( ) Grand Challenge Urban Challenge 5) 6) Fig. 1 Google DAIMLER 7) 8) 3
2 Fig. 1 Google 社の自動運転自動車 2. デジタル地図を活用した自動運転 Fig. 2(a) 1 Fig. 2(b) 17m 17m Fig. 2 認識の難しいシーンの例 9) DENSO TECHNICAL REVIEW Vol 特別寄稿4
3 Fig. 3 著者らの研究室で開発した自動運転自動車 3. 金沢大学の取り組み Fig. 3 (1998 ) (2008 ) 10) 11) ITS ) 3.1 試験車両の概要 Fig. 4 CAN(Controller Fig. 4 公道走行に用いた試験車両の概要 Area Network) GNSS/INS (Applanix POS-LV220) DMI(Distance Measurement Indicator) IMU(Inertial Measurement Unit) 2 GNSS (Global Navi gation Satellite System) 100Hz 3cm
4 LIDAR(Velodyne HDL64E-S2) 9 ( ) LIDAR LIDAR (Point Gray FLEA2) 3.2 デジタル地図と自己位置推定 9 (Localization) GPS(Global Positioning System) GNSS GNSS GNSS INS (Inertial Navigation System) 12) GNSS Fig. 5 LIDAR により作成したオルソ画像の例 ( ) GNSS GNSS/ INS LIDAR GNSS/INS (Post processing) 12) GNSS/INS LIDAR Fig. 5 LIDAR GNSS 13 Fig. 6 DENSO TECHNICAL REVIEW Vol 特別寄稿6
5 Fig. 6 デジタル地図の例 Fig. 7 自動運転中に複数信号機を同時認識している様子 3.3 信号機認識 1 Fig m 3.4 障害物検出と移動物体の予測軌道推定 LIDAR Velodyne LIDAR 360 LIDAR LIDAR LIDAR (Occupancy Grid Maps) 2 Binary Bayes Filter Fig. 8 Binary Bayes Filter 7
6 Fig. 8 周辺環境認識の例 Fig Fig. 9(a) Fig. 9(b) 3.5 パスプランニング 16) Fig. 9 Fig. 10 High Level デジタル地図を用いた軌道予測の効果 Fig. 10 階層構造型パスプランナ High level Fig. 6 High Level DENSO TECHNICAL REVIEW Vol 特別寄稿8
7 Fig. 11 右折時における注視領域の設定例 Middle Level High Level Middle Level Fig. 11 Finite State Machine Low Level Middle Level Low Level Middle Level Middle Level Low Level Low Level Fig. 12 走行軌道と経路の幾何学的関係 2 Low Level t d(t) s(t) Fig. 12 d(t) s(t) x(t) (1) r(s) s n r (s) s r(s) 4. 自動運転自動車の公道走行実証試験 9
8 8) % Fig km km Fig km Fig まとめ Door to door 公道走行中の自動運転自動車 DENSO TECHNICAL REVIEW Vol 特別寄稿10
9 SCOPE ICT (No 参考文献 1) 60 Vol.54 No ) ( 1 ) No pp ). Vol.101 No.285 p (2001) 4) IMTS DC p ) M.Montemerlo, et al., Junior: The Stanford Entry in the Urban Challenge, Journal of Field Robotics, Vol.25, No.9, pp , ) C.Urmson, et al., Autonomous Driving in Urban Environments: Boss and the Urban Challenge, Journal of Field Robotics, Vol.25, No.8, pp , ) Julius Ziegler, et al., Video Based Localization for BERTHA, Proc. of the IEEE Intelligent Vehicle Symposium, pp , ) No,14-15S pp ) Vol.33 No.10 pp ),, C , 1356/1365 (2011) 11) 44-3, 961/966 (2013) 12) J. Hutton, et al., Tight integration of GNSS Post-processed virtual reference station with inertial data for increased accuracy and productivity of airborne mapping The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. pp ) N.Suganuma, D.Yamamoto, Map based localization of autonomous vehicle and its public urban road driving evaluation, Proc. of 2015 IEEE/SICE International 11
10 Symposium on System Integration pp ) Keisuke Yoneda, Naoki Suganuma, Mohammad Amro Aldibaja, Simultaneous State Recognition for Multiple Traffic Signals on Urban Road, Proceedings of Mecatronics-Rem2016, pp , ) Occupancy Grid Maps ) 著者 Vol.57 No.5 pp 菅沼直樹 すがぬまなおき 著者略歴 PD 主な学会活動 主な受賞歴 DENSO TECHNICAL REVIEW Vol 特別寄稿12
特集 自動運転システムにおける情報処理技術の最新動向 3 自動運転自動車のパスプランニング 基応専般 菅沼直樹米陀佳祐 ( 金沢大学新学術創成研究機構 ) 自動運転の判断 近年自動運転自動車に関する研究開発が世界各国において行われている 1). 日本においても, 図 -1 に示すように筆者らの研究室
3 自動運転自動車のパスプランニング 基応専般 菅沼直樹米陀佳祐 ( 金沢大学新学術創成研究機構 ) 自動運転の判断 近年自動運転自動車に関する研究開発が世界各国において行われている 1). 日本においても, 図 -1 に示すように筆者らの研究室が国内の自動車メーカ等の協力のもと国内の大学としては初となる公道走行実験を開始するなど大きな盛り上がりを見せている 2). このような自動運転自動車を安全に目的地に到達させるためには,
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