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Machine Learning for Self-Driving Cars

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High-level Development Process for Autonomous Vehicles. Let’s build a self driving car.

Published in: Automotive

Machine Learning for Self-Driving Cars

  1. 1. Machine Learning for Self-Driving Cars
  2. 2. 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
  3. 3. High-level Development Process for Autonomous Vehicles 3 1 Collect sensors data 3 Autonomous Driving 2 Model Engineering Data Logger Control Unit Big Data Trained Model Data Center 1 Collect sensors data
  4. 4. 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
  5. 5. 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
  6. 6. 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 +++ +++ +++
  7. 7. 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
  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 in Robotics Observations State Estimation Modeling & Prediction Planning Controls f(x) Controls Observations
  10. 10. 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
  11. 11. Machine Learning Workflow Ingest data Data Preprocessing Search Analysis Model Training Re- simulation Reports Results Model Deployment Training data Model Testing Train Test Loop Test data Model Feedback Loop
  12. 12. More Data + Bigger Models Accuracy Scale (data size, model size) other approaches neural networks 1990s https://www.scribd.com/document/355752799/Jeff-Dean-s-Lecture-for-YC-AI
  13. 13. 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
  14. 14. 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?
  15. 15. When to use 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
  16. 16. Compute Workload for Training and Evaluation I/O intensive Compute intensive Single machine Data center
  17. 17. I/O Workload for Simulation and Testing I/O intensive Compute intensive Single machine Data center
  18. 18. Open Machine Learning Platform Training & Test data Compute + Network + Storage Deploy model ML Development & Catalog & REST API ML-Specialists Search Analysis 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
  19. 19. ROS bag data structure https://github.com/valtech/ros_hadoop
  20. 20. Hadoop InputFormat for ROS bags https://github.com/valtech/ros_hadoop
  21. 21. Search & Analysis + 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
  22. 22. Training & Evaluation + Tensorflow Record Reader + 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 Record Reader Ros msg
  23. 23. 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
  24. 24. Time Travel fold(left) t fold(right) reduce/ shuffle
  25. 25. High-level Development Process for Autonomous Vehicles 25 1 Collect sensors data 3 Autonomous Driving 2 Model Engineering Data Logger Control Unit Big Data Trained Model Data Center 3 Autonomous Driving
  26. 26. Architecture Building Blocks http://www.bmw-carit.com/downloads/presentations/AutonomousDrivingNeedsROSScript.pdf
  27. 27. Hadoop InputFormat for ROS Apache License 2.0 Download https://github.com/valtech/ros_hadoop Contact jan.wiegelmann@valtech.de
  28. 28. thank you

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