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信頼されるLiDARに向けて

Yoshioka Lab (Keio CSG)
September 26, 2022
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 信頼されるLiDARに向けて

信頼されるAIを実現するにはAI自身の習熟度以外にもセンサの"信頼性"が求められる。本講演では自動運転やロボティクスで必需3DセンサであるLiDAR(Light Detection and Ranging)の信頼性を主に高解像度化とセキュリティの二面から問う。まず3D情報を活用したAIが十分な信ぴょう性を持つためにはLiDARの高解像度が不可避である。そのような高解像度LiDARの実現方法を回路システム的見地から議論し、先端LiDARの発展と照らし合わせる。そしてLiDARのハッキング可能性について議論し、セキュリティ方面の研究を紹介し、最後にハッキングの防衛方法を講じる。

Yoshioka Lab (Keio CSG)

September 26, 2022
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  1. 信頼されるLiDARに向けて 吉岡 健太郎 2022/9/26 情報処理学会 連続セミナー2022第6回 Assistant Prof., Keio University

    Japan Rotating-mirror LiDARs (Yoshioka, ISSCC 2018) Solid-state LiDARs (Kondo,Tan, ISSCC/VLSI 2020)
  2. ・2014 Graduate@Keio Univ. ・2014-2021 Toshiba ・2017-2018 Stanford Visiting Scholar @Mark

    Horwitz Group ・2021- Keio Univ. Assistant Prof. Keio Computing and Sensing Group https://sites.google.com/keio.jp/keio-csg/ About me Kaggle/Signate Master
  3. Slide 6 距離センサのベンチマーク • 自動運転の盛り上がり – トヨタも2020年代前半に市街地自動運転の実現を明言 • 自動運転には周辺環境のセンシングが必須 –

    しかしミリ波レーダやソナーでは性能不足 センサ 特徴 超音波 近距離の物体検知を安価に実現。長距離は苦手。 カメラ 自動車以外に歩行者・自転車の検出、標識の認識が可能。距離情報の取得はやや不得意 ミリ波レーダー 耐天候性があり長距離測距が可能。検知範囲が狭く、距離精度・空間分解能が低い。 LiDAR ミリ波レーダーに比べ環境の影響を受けやすいが、距離精度・空間分解能が高い。 自動運転システムではLiDARが重要なセンサに
  4. Automobile LiDAR Requirements Slide 8 • Long range (>200m) 200m

    Braking distance@120km/h >150m 17cm debris = 0.1 deg. @ 100m • High image resolution (<0.1deg)
  5. LiDAR Fundamentals Slide 9 Detects the distance to object by

    measuring ToF LiDAR System Laser SoC SiPM Laser pulse time time SiPM output ToF Light Reflected ToF Laser emitted Distance to object Light speed x ToF 2 = ToF
  6. Noise problem of automotive LiDAR • Sunlight affects the measurement

    result Direct-ToF Laser SoC SiPM Laser time time SiPM Wrong ToF! Sunlight Laser Brighter-than-sunlight laser is required. Focused laser beam is necessary Slide 10
  7. Only scanning LiDARs can afford long range requirement Automotive LiDAR’s

    topology Slide 11 Scanning LiDARs Laser SoC SiPM Flash LiDARs Laser SoC APD array ☺ Focused laser beam ⇒Long range  Low resolution  Bulky scanner ☺ High resolution ☺ Solid state  Unfocused laser beam ⇒Short range Scanner
  8. 1st Gen. LiDARs (or analog LiDARs) Slide 12 Further read:

    Why digital lidar is the future https://ouster.com/blog/why-digital-lidar-is-the-future/ Getting “points” for each component Dedicated focused lasers
  9. 1st Gen. LiDARs (or analog LiDARs) Slide 13 Further read:

    Why digital lidar is the future https://ouster.com/blog/why-digital-lidar-is-the-future/ Getting “points” for each component Dedicated laser paths scanning
  10. 1st Gen. LiDARs (or analog LiDARs) Slide 14 Further read:

    Why digital lidar is the future https://ouster.com/blog/why-digital-lidar-is-the-future/ Analog LiDARs (e.g. Velodyne) • Discrete Laser/RX • APD receivers • EELs Many components..  Expensive LiDARs  Difficult to scale Getting “points” for each component
  11. Next Gen. LiDARs (or digital LiDARs) • Analog LiDARs –

    Discreate components – Unscalable resolution – Poor lasers/detectors (EEL, APD) • Next Gen. LiDARs – Integrated components • Higher performance, but lower price (as in Moore’s law) • Mass production LiDARs can be in $500~1000 range – Scalable performance – SoTA laser/detectors (VCSELs, SPADs) Slide 15
  12. Ouster Integrated LiDAR Slide 16 Signal Processing Image Sensor VCSEL

    array Getting “2D surfaces” instead of “points” Surface scanning T[0] T[1] T[2]
  13. Next Gen. LiDARs (or digital LiDARs) Slide 17 • 大きなゲームチェンジは?

    – APDからSPADに – EELからVCSELに • Image sensor(SPAD array)+ デジタル回路を集積(ASIC) • よりリッチな信号処理がLiDAR上で 可能に • LiDARに特化した信号処理の発展 Image Sensor + ASIC
  14. What can we do with Next Gen. LiDARs? • Laser

    power loss follows inverse square law – Not a big problem @50m – Severe SNR @ 200m LiDAR System SiPM Laser SoC @200m Laser time time SiPM Wrong ToF! Sunlight Laser Laser SoC SiPM Polygon mirror SNR: Num. laser photons Num. noise photons = Laser time time SiPM Wrong ToF! Sunlight Laser
  15. What can we do with Next Gen. LiDARs? While sunlight

    photons are random events, Laser photons are periodic → Accumulation improves SNR by square root of acc. pixels Pix.1 Pix.2 Pix.3 Laser Accumulation of pix.1~3 ToF Sunlight • Accumulation, or oversampling for higher SNR – Strategies: Frame accumulation and Nearby pixel accumulation – If the pixel is “watching” the same object, SNR improves [Niclass, ISSCC2013]
  16. Main Tradeoffs in LiDAR Design • Requirement extremely strict for

    self-driving LiDARs Importantly: Laser power limited by eye-safety but not for attackers.. Measurement Distance Sunlight Tolerance Image Quality (FPS, num.pixels) Image Quality High Low Measurement Distance [m] 100 200
  17. Problem of Simple Accumulation Non-target reflection Target reflection Target object

    Non-target A D G B E H C F I Measuring pixel (MP) Pixel A,B,C Pixel E Pixel D,F,G,H,I time time time Target reflection Simple accumulation A+B+C+D+E+F+G+H+I time Detected peak Wrong!  Function similar to “blur” algorism
  18. Concept of Smart Accumulation Technique (SAT) • Classify objects utilizing

    LiDAR raw-data characteristics • Accumulate pixels watching the same object only; SNR improves significantly – Peak level has high correlation with distance and reflectivity of the object – Floor level also has strong correlation with the object distance and reflectivity A D G B E H C F I Pixel A,B,C Pixel D,E,F,G,H,I time time Peak level Peak level Floor level Floor level
  19. SAT Algorithm (3) Target object Non-target A D G B

    E H C F I Measuring pixel Pixel A,B,C Pixel E Pixel D,F,G,H,I PL High correlation! Peak level Floor level FL Correlation of PL & FL above threshold? Accumulate Skip SAT Algorithm D,F,G,H,I A,B,C Yes No Simple accumulation A+B+C+D+E+F+G+H+I time Detected peak Wrong!  Smart accumulation(SAT) D+E+F+G+H+I time Detected peak Correct! ☺ Accumulated waveforms Long meas. range + High image quality PL FL
  20. Effect of Smart Accumulation Algorithm Imaged blurred Road detected Pedestrian

    undetected Road undetected Pedestrian Camera image “Simple” Accumulation Without Accumulation High image quality, but only short distance Poor image quality, Unable to small objects
  21. Effect of Smart Accumulation Algorithm Pedestrian undetected Road undetected Pedestrian

    Camera image “Simple” Accumulation Without Accumulation Road detected Pedestrian detected “Smart” Accumulation High image quality, Able to detect small obj.
  22. 試作LiDAR • K.Yoshioka, ISSCC 2018 Slide 26 3D point cloud

    view ADC Image TDC Image Luminescence アナログ+デジタル集積チップ