신재생에너지 마이크로 그리드 최적화

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1 ADAS & AD October 31 th, 2017 ADAS : Advanced Driver Assistance System AD : Autonomous Driving

2 ProSense :

3 개요 알고리즘 : ADAS & AD 비즈니스모델

4 그래픽기반자율주행시뮬레이터 자율주행차개발의全영역 ( 데이터획득, 데이터처리, 응용개발, 검증 ) 에시뮬레이션방법론을적용함. 개발 Cycle 실차량 센서데이터획득 센서융합 데이터저장 Dataset 마련 딥러닝학습 지도제작 AD SDK CUDA 알고리즘 H/W : Drive PX2 S/W : DriveWorks TensorRT, cudnn 시뮬레 이션 가상센서데이터 가상센서융합 가상 Dataset 자동생성 딥러닝학습 가상지도자동생성 AD SDK CUDA 알고리즘 H/W : Drive PX2 S/W : DriveWorks TensorRT, cudnn

5 Test Platform : Real-time Perception, Self-driving An SUV for real-time perception and a self-driving car including control algorithms Multi-Camera Multi-Radar Drive PX2

6 ADAS Directions : Algorithms Licensing 3D Lane & Road, Radar Fusion High Accuracy AEB : Depth/VD/PD Vision Cruise : High Curvature Robustness

7 AD (Autonomous Driving) Directions Perception : Real Time High Accuracy Better Control by better perception Service : Last Mile Mobility Simulation based AD Development

8 Algorithms : VD, PD, LD, 6D, Tracking, CNN-OD, Stixel, SVM

9 Stereo Matching & Stixel Real-time depth map estimation 20ms (avg.) with GTX ~300ms with Jetson TX1 STIXEL (Stick + Cell) Used to detect and to represent obstacles INPUT Depth STIXEL 9

10 Robust Stereo Matching Matching costs Matching costs Challenging outdoor environment 2 1 Random forest d C(, k) Cˆ( p, d ) Q ( p )C( p,d) (1 Q( p )) p D k D Confidence-based cost modulation Overlaid d Disp. map 3 Learning-based confidence measure selection and training 4 Semi-global matching Proposed method Confidence prediction Min-Gyu Park and Kuk-Jin Yoon, Leveraging Stereo Matching with Learning-based Confidence Measures," IEEE Conf. on Computer Vision and Pattern Recognition (CVPR),

Lane Detection Classical lane detection approach

lane detection Dataset : 1,500 images Train

11 Lane Detection Classical lane detection approach (300fps with 3.00GHz CPU) Input image IPM image Filter & Threshold Detect lanes Result Deep learning-based lane detection Dataset : 1,500 images Train dataset : 60,000 images (augmentation) Fully Convolutional Network 10ms (100fps) with Jetson TX1 11

12 12 Vehicle Detection Real-time vehicle detection Under development HoG + Kernel SVM ACF + Boosting (aggregated channel features) 15ms with Jetson TX1 Feature extraction VGA ROI definition QVGA Expandable to PD (pedestrian detection)

ADAS current project : Vision based Cruise

red vehicle should drive steadily without abrupt

60km/h Target vehicle changes lane after sometime.

target vehicle changes the lane, subject vehicle

13 ADAS current project : Vision based Cruise Control When the blue vehicle changes lane, the red vehicle should drive steadily without abrupt acceleration Current target vehicle speed : 60km/h Target vehicle changes lane after sometime. Current subject vehicle speed : 60km/h After target vehicle changes the lane, subject vehicle speed up to 100km/h (Cruise speed : 100km/h) Radar Radius : 150m Camera

14 Adaptive Cruise Control Implementation details Fusion: Radar + Camera Lane monitoring by Camera Ego-lane detection Medium curvature Proprietary Radar algorithm Calibration Radar + Camera

15 ADTF Architecture for Surround View Monitoring System

16 Key competences Photogrammetry algorithms 3D Reconstruction algorithms ADAS algorithms System engineering Mature development processes (Scrum, Kanban, Waterfall) CEVA MM-3101 & XM4 optimization CUDA & OpenCL intensive computation FPGA programming

17 Stereo vision algorithm High performance 20 fps (Zynq 7020, HD resolution) Low latency 50 ms (Zynq 7020, HD resolution) Sparse disparity map features (HD resolution)

18 Forward Collision Avoidance Functions Stereo SGM, AD-Census Stixels & CNN-based obstacle detection Collision estimation (tracking & trajectory & time to collision) Free space to drive

19 Forward Collision Avoidance CNN Object detection Classes car, pedestrian, cyclist CNN YOLOv2 Training set images map Performance Platform nvidia Drive PX2 20 fps Resolution 512x288 px

20 Traffic sign detection Implementation details SSD-M with MobileNet features High performance 20 fps on Jetson TX2 High accuracy 0.74 map European, Russian, South Korean traffic signs ~30000 images Classifying speed limits ~17000 images Low model size 22 Mb Detection performance TX2: 35 ms, PX2: 24 ms Classification performance TX2: 6 ms, PX2: 4 ms Accuracy F1-score 0.89

21 그래픽기반자율주행시뮬레이터 (1/3) 자율주행을위한그래픽기반가상 Dataset 의효용성이검증됨. 또한실제로구현이어려운상황의경우그래픽기반가상 Dataset 의효용성은더욱증대됨.

22 그래픽기반자율주행시뮬레이터 (2/3) 그래픽기반가상 Dataset 을통한학습과실영상기반학습을병행한경우더욱우수한인식성능 < 그래픽객체예시및가상카메라배치 > < 학습효과의성능비교 >

23 AD current project : Last Mile Mobility We are now collaborating with Korean mini-bus makers and Korean local governments.

24 전기차기반자율주행버스아르메 (ARMA) Lidar 센서 : 환경을매핑하여위치지정, 3D 인식 GPS RTK : GPS 센서와기지국간통신 ( 차량의정확한위치결정 ) Odometer : 변위및바퀴속도측정 차량속도추정, 위치확인 카메라스테레오비전 : 도로환경분석및정보추출 Arma Device Route

25 자율주행버스 EZ10 다중센서 localization 기술, 장애물감지및회피, 탐색, 경로계획및제어, 연결성 (V2V, V2I), 차량관리, 안전및사이버보안 3가지작동모드 (Metro 모드 : 모든역에서정차, Buses 모드 : 요청시에정차, On-demand 모드 : 호출가능 ) 3 modes Hybrid localization

26 Bus Rapid Transit(BRT) : 전용차선에서운영. 속도, 규칙성, 빈도, 다수의수송인원 Buses : 경로명확. 상업적, 경제적, 생태적효율성 Tram-train : 도시지역의경전철트랙과교외지역의철도트랙에서작동 Metro : Roissy-Charles de Gaulle의공항터미널, 기차역및주차장을연결하는전기메트로 CDGVal 운영 BRT Bus Tram-train Metro

27 전용가이드웨이에서운행하는자율주행셔틀 ( 안전성, 유연성보장 ) 4~6 人승객 PRT 차량 ( 택시 ), 16~24 人승객 GRT 차량 ( 버스 ) 대중교통운영모니터링및감독 (TOMS), 차량제어시스템 (VCS), 안내제어시스템 (GCS) TOMS GRT

28 다수의마이크로공장을이용한오픈소스차량디자인의소량생산 자율주행버스 Olli 개발 (IBM Watson의고급인지능력통합 ) 360도센서 원격차량모니터링, 관리및경고시스템 Autonomous vehicle Inside the vehicle

29 자율주행버스개발 ( 대학캠퍼스, 비즈니스파크및주거지역내여행용 - 2 인승, 6 인승, 12 인승셔틀버스 ) 기후제어, Lidar 및 Camera 로 360 도감지, 장애물탐색및회피등 Autonomous vehicle Running the car

30 물류서비스 모바일어플리케이션을통해승용차와운전자를연결 원하는차량서비스선택가능 ( 이코노미, 프리미엄, 카풀등 ) Utilization the Uber taxi Apps for driver partners

31 자율주행을위한확장가능 full-stack 소프트웨어솔루션 대규모자치차량을통해 point-to-point 이동성을제공하는솔루션개발中. ( 도시에서의자율주행, 스마트폰기반승차, 차량경로및관리, 원격조작을통해차량을제어하는소프트웨어 ) 미국, 싱가포르및유럽에서테스트完. Autonomous vehicle NuCore software

32 Thank you very much! Paul Kang

High Resolution Disparity Map Generation Using TOF Depth Camera In this paper, we propose a high-resolution disparity map generation method using a lo

High Resolution Disparity Map Generation Using TOF Depth Camera In this paper, we propose a high-resolution disparity map generation method using a low-resolution Time-Of- Flight (TOF) depth camera and