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Deep Learning for Autonomous Driving

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End-to-end machine learning pipeline using the Robot Operating System (ROS) on Hadoop with Spark and TensorFlow.

Publié dans : Industrie automobile
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Deep Learning for Autonomous Driving

  1. 1. 1 Deep Learning for Autonomous Driving
  2. 2. 2 Jan Wiegelmann @janwgl Data Analytics at Valtech Data Science, Engineering Distributed Deep Learning Hadoop Ecosystem Meetups in Munich Robot Operating System Big Data in Automotive
  3. 3. High-level Development Process for Autonomous Vehicles 1 Collect sensors data 3 Autonomous Driving 2 Model Engineering Data Logger Control Unit Big Data Trained Model Data Center Agenda
  4. 4. High-level Development Process for Autonomous Vehicles 4 1 Collect sensors data 3 Autonomous Driving 2 Model Engineering Data Logger Control Unit Big Data Trained Model Data Center 1 Collect sensors data
  5. 5. Sensors Udacity Lincoln MKZ Camera 3x Blackfly GigE Camera, 20 Hz Lidar Velodyne HDL-32E, 9.5 Hz IMU Xsens, 400 Hz GPS 2x fixed, 1 Hz CAN bus, 1,1 kHz Robot Operating System Data 3 GB per minute https://github.com/udacity/self-driving-car
  6. 6. Robot Operating System + Popular open source robotics framework + Reliable distributed architecture + Wide use in the robotics research community + Huge selection of “off-the-shelf” software packages for hardware/algorithms/etc. + Used by Bosch, BMW, KUKA, Google, Siemens, etc. https://roscon.ros.org/2015/presentations/ROSCon-Automated-Driving.pdf
  7. 7. Sensors Spec Sensor blinding, sunlight, darkness rain, fog, snow non-metal objects wind/ high velocity resolution range data Ultrasonic yes yes yes no + + + Lidar yes no yes yes +++ ++ + Radar yes yes no yes ++ +++ + Camera no no yes yes +++ +++ +++
  8. 8. High-level Development Process for Autonomous Vehicles 8 1 Collect sensors data 3 Autonomous Driving 2 Model Engineering Data Logger Control Unit Big Data Trained Model Data Center 2 Model Engineering
  9. 9. Machine Learning 101 Observations State Estimation Modeling & Prediction Planning Controls
  10. 10. Machine Learning 101 Observations State Estimation Modeling & Prediction Planning Controls f(x) Controls Observations
  11. 11. AI history à Perceptron 1958 F. Rosenblatt, “Perceptron” model, neuronal networks 1943 W. McCulloch, W. Pitts, “Neuron” as logical element OR function XOR function 1969 M. Minsky, S. Papert, triggers first AI winter feed forward
  12. 12. AI history à AI winter 1958 F. Rosenblatt, Perzeptron model, neuronal networks 1987-1993 the second AI winter, desktop computer, LISP machines expensive 1943 W. McCulloch, W. Pitts, neuron as logical element 1980 Boom expert systems, Q&A using logical rules, Prolog 1969 M. Minsky, S. Papert, trigger first AI winter 1993-2001 Moore’s law, Deep blue chess- playing, Standford DARPA challenge
  13. 13. AI history Accuracy Scale (data size, model size) other approaches neural networks 1990s https://www.scribd.com/document/355752799/Jeff-Dean-s-Lecture-for-YC-AI
  14. 14. More Data + Bigger Models + More Computation Accuracy Scale (data size, model size) other approaches neural networks Now https://www.scribd.com/document/355752799/Jeff-Dean-s-Lecture-for-YC-AI more compute
  15. 15. Machine Learning for Autonomous Driving + Sensor Fusion clustering, segmentation, pattern recognition + Road ego-motion, image processing and pattern recognition + Localization simultaneous localization and mapping + Situation Understanding detection and classification + Trajectory Planning motion planning and control + Control Strategy reinforcement and supervised learning + Driver Model image processing and pattern recognition
  16. 16. Car data from sensors and bus traces CAN, Flexray, Camera, Radar, Lidar, IMU, etc. Pre-select signals, aggregate and prepare for sending Parse traces and signals (dbc, fibex, autosar...) Receive signals, analysis, and machine learning Real-time or batch analysis based on sensors data publish/subscriberealtime Car Layer Data Logger Data Center Realtime Data Analytics Real-time Analysis of car data
  17. 17. Train and evaluate machine learning models at scale Single machine Data center How to run more experiments faster and in parallel? How to share and reproduce research? How to go from research to real products?
  18. 18. Distributed Machine Learning Data Size Model Size Model parallelism Single machine Data center Data parallelism training very large models exploring several model architectures, hyper- parameter optimization, training several independent models speeds up the training
  19. 19. Compute Workload for Training and Evaluation I/O intensive Compute intensive Single machine Data center
  20. 20. I/O Workload for Simulation and Testing I/O intensive Compute intensive Single machine Data center
  21. 21. Machine Learning Cycle Data collection for training/test Feature engineering I/O workload Model development and architecture Compute workload I/O workload Training and evaluation Re- Simulation and Testing Scaling and monitoring Model deployment versioning 1 2 3 Model tuning
  22. 22. Flux – Open Machine Learning Stack Training & Test data Compute + Network + Storage Deploy model ML Development & Catalog & REST API ML-Specialists Feature Engineering Training Evaluation Re-Simulation Testing CaffeOnSpark Sample Model Prediction Batch Regression Cluster Dataset Correlation Centroid Anomaly Test Scores ü Mainly open source ü No vendor lock in ü Scale-out architecture ü Multi user support ü Resource management ü Job scheduling ü Speed-up training ü Speed-up simulation
  23. 23. Flux – Open Machine Learning Stack Training & Test data Compute + Network + Storage Deploy model ML Development & Catalog & REST API ML-Specialists Feature Engineering Sample Model Prediction Batch Regression Cluster Dataset Correlation Centroid Anomaly Test Scores ü Mainly open source ü No vendor lock in ü Scale-out architecture ü Multi user support ü Resource management ü Job scheduling ü Speed-up training ü Speed-up simulation
  24. 24. Feature Engineering + Hadoop InputFormat and Record Reader for Rosbag + Process Rosbag with Spark, Yarn, MapReduce, Hadoop Streaming API, … + Spark RDD are cached and optimized for analysis Ros bag Processing Engine Computer Network Storage Advanced Analytics RDD Record Reader RDD DataFrame, DataSet SQL, Spark APIs NumPy Ros Msg
  25. 25. ROS bag data structure https://github.com/valtech/ros_hadoop
  26. 26. Hadoop InputFormat for ROS bags https://github.com/valtech/ros_hadoop
  27. 27. Flux – Open Machine Learning Stack Training & Test data Compute + Network + Storage Deploy model ML Development & Catalog & REST API ML-Specialists Training Evaluation CaffeOnSpark Sample Model Prediction Batch Regression Cluster Dataset Correlation Centroid Anomaly Test Scores ü Mainly open source ü No vendor lock in ü Scale-out architecture ü Multi user support ü Resource management ü Job scheduling ü Speed-up training ü Speed-up simulation
  28. 28. Training & Evaluation + Tensorflow ROSRecordDataset + Protocol Buffers to serialize records + Save time because data conversion not needed + Save storage because data duplication not needed Training Engine Machine Learning Ros bag Computer Network Storage ROS Dataset Ros msg
  29. 29. Flux – Open Machine Learning Stack Training & Test data Compute + Network + Storage Deploy model ML Development & Catalog & REST API ML-Specialists Re-Simulation Testing Sample Model Prediction Batch Regression Cluster Dataset Correlation Centroid Anomaly Test Scores ü Mainly open source ü No vendor lock in ü Scale-out architecture ü Multi user support ü Resource management ü Job scheduling ü Speed-up training ü Speed-up simulation
  30. 30. Re-Simulation & Testing + Use Spark for preprocessing, transformation, cleansing, aggregation, time window selection before publish to ROS topics + Use Re-Simulation framework of choice to subscribe to the ROS topics Engine Re-Simulation with framework of choice Computer Network Storage Ros bag Ros topic core subscribe publish
  31. 31. Time Travel fold(left) t fold(right) reduce/ shuffle
  32. 32. High-level Development Process for Autonomous Vehicles 32 1 Collect sensors data 3 Autonomous Driving 2 Model Engineering Data Logger Control Unit Big Data Trained Model Data Center 3 Autonomous Driving
  33. 33. Flux – Open Machine Learning Stack Training & Test data Compute + Network + Storage Deploy model ML Development & Catalog & REST API ML-Specialists Feature Engineering Training Evaluation Re-Simulation Testing CaffeOnSpark Sample Model Prediction Batch Regression Cluster Dataset Correlation Centroid Anomaly Test Scores ü Mainly open source ü No vendor lock in ü Scale-out architecture ü Multi user support ü Resource management ü Job scheduling ü Speed-up training ü Speed-up simulation
  34. 34. Flux – Open Machine Learning Stack + Native format support e.g. rosbags (Robot Operating System) + End-to-end machine learning pipeline + Layered API (provisioning, operating, processing, storage) + Optimized for scale-out based on cost, time, space + One-click on premise and cloud deployment + Apache License 2.0 – release Q4/2017 + http://flux-project.org
  35. 35. Flux Apache License 2.0 release Q4/2017 http://flux-project.org

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