Designing GPU-accelerated applications with RTMaps (Real-Time Multisensor Applications) Framework and NVIDIA DriveWorks

Transcription

1 MUNICH OCT 10-12, 2017 Designing GPU-accelerated applications with RTMaps (Real-Time Multisensor Applications) Framework and NVIDIA DriveWorks Xavier Rouah Lead Software Engineer

2 Brief introduction about Intempora

3 Intempora Software editor company Created in Over 15 years experience in ADAS/A.D. and embedded computing Over 15 years of experience Strong partnership with World distribution

4 History Over 15 years of experience in providing advanced solutions Multisensor Technology since 1998! Darpa Challenge 2007 (Dotmobil Team) NAVYA ARMA Video speed: 1x. Over 100 kmh 65 mph

5 ADAS & AD Challenges

6 Challenges ADAS and AD 1 2 Time coherency in distributed/multi-core multisensor applications Execution performance / Number crunching 3 4 Offline development Ease of use / Ease for deployment 5 6 Development costs / Time to market Test & validation

7 RTMaps middleware

8 Sensors Radar, LiDAR, GPS, Maps, V2X, Simulators, Cloud RTMaps Actuators Motor, Wheel, Brake, DB, Cloud, V2X Input DATA PROCESSING Output

9 Features Graphical User interface Large library of off-the-shelf components Record & Playback Optimized (multithread, pre-allocated buffers, copyless) Preserves time coherency Portability and Scalable

10 Develop your own RTMaps components Applications Machine Learning Positioning & Navigation SLAM Perception Data Fusion A cross platform / multi-language API C++ (Cuda, Caffe, TensorFlow) Python Simulink QML (QT) Collaborate and share your components with your team & partners! 2D/3D Big Data / Cloud Computer Vision HMIs

11 RTMaps as software integration and interoperability framework Simulators (MotionDesk, ASM, PreScan, Pro-SiVIC..) ADAS toolchain (VEOS, ControlDesk, MicroAutoBox) Advanced HMIs (Qt, QML) Sensors / Actuators RTMaps Communication (DDS, TCP, UDP, LCM, RTSP ) Applied Mathematics (Deep learning, machine learning algorithms, Caffe, Cuda, TF) Digital maps Robotics (ROS bridge) Signal processing & Control Actuators (Simulink, MathWorks) Image processing (OpenCV, others libraries )

12 RTMaps workflow

13 1 Offline Simulation From R&D to production 2 In-vehicle Data Recording 3 Offline Data Playback 4 Embedded in prototype (PC/eSPU+ MABX) 5 Prototyping RTMaps apps to embedded targets 6 RTMaps applications to embedded ECU RTOS From COTS solutions to custom developments

14 Model based perception Sensors Perception Applications Actuators Data processing, Data fusion, tracking fcn 1 (ECU a) Scene Interpretation fcn 2 (ECU b) Environment Model fcn 3 (ECU b) Model-based perception Model-based controller design High performance sensor data processing GigE MATLAB/Simulink

15 RTMaps Embedded & NVIDIA

16 RTMaps on Nvidia boards since 2013 ELA Project 2013 Automotive Electronics and Software RTMaps Embedded on ARM architecture DriveWorks B2.0 DriveWorks B3.0 Nvidia Jetson Tegra K Nvidia Jetson Tegra X Nvidia Drive PX Nvidia Drive PX2 «AutoCruise» 2015 Q Nvidia Drive PX2 «Autochauffeur» Today

17 Intensive computing taking advantage of hardware acceleration RTMaps Runtime engine runs on the ARM CPU OS ARM GPU Components (image processing for instance) can wrap intensive computing algorithms taking advantage of GPU Supported OS: Windows, Linux, Embedded Linux (Yocto/Poky)

18 RTMaps Embedded RTMaps Studio operating on separate laptop with direct SSL connection with the runtime engine on the target. Used for diagrams edition (design and configuration). Work & Edit your algorithms from PC RTMaps Runtime Engine and components compiled on chosen target SSL Easily deploy on board RTMaps SDK for cross-compilation on a Linux PC, or available directly on the target

19 RTMaps Remote Studio RTMaps Studio (Developer Version) RTMaps Embedded (Runtime Version) RTMaps Remote Studio SSL connection, TCP/IP RTMaps Remote Studio Connector RTMaps Diagram PC Connection to RTMaps Embedded Developing and editing your algorithm from a PC Use of components already compiled for embedded platforms Embedded platforms Deploy Easily on embedded platforms Execution of your algorithms Taking advantage of hardware acceleration

20 RTMaps Embedded / QNX RTOS

21 RTMaps & NVIDIA DriveWorks

22 RTMaps & NVIDIA DriveWorks * DriveWorks Components (C++ / CUDA) RTMaps DRIVE PX 2 DriveNet LaneNet Feature Tracker Nvidia_DriveWorks etc *Easily deploy on target with RTMaps Remote Studio (SSL)

23 Conlusion: Video Live Demonstration Reduce and enhance your development cycles

24 RTMaps Remote Studio & NVIDIA DriveWorks

25 RTMaps & KITTI

26 Thanks for your attention Q&A? Evaluate RTMaps! Download RTMaps on Xavier Rouah Lead Software Engineer