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AI in Autonomous Driving - Bhanu Prakash - Continental - AI Dev Days 2018

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This presentation was made by Bhanu Prakash (ADAS, Continental AG.,) as part of AI Dev Days conference held in Bangalore on 9th March 2018. URL: www.aidevdays.com
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* Intro to AD and Levels of AD (Autonomous Driving)
* Building blocks of AD ( ADAS Sensors)
* Different AI/Deep Learning Techniques used in ADAS and AD
* Challenges and Pitfalls of AI and AD

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AI in Autonomous Driving - Bhanu Prakash - Continental - AI Dev Days 2018

  1. 1. Artificial Intelligence in Autonomous Driving Bhanu Prakash P. ADAS, Continental AG., Chassis & Safety | Advanced Driver Assistance Systems
  2. 2. Public BU ADAS 08.03.2018 2Robert Thiel Agenda SAE Levels of Driving Automation2 Building Blocks of AD3 AI in Autonomous Driving4 AI/AD Challenges5 Continental Product Line1
  3. 3. Public BU ADAS 08.03.2018 3Bhanu Prakash P. ADAS Product Portfolio SHORT RANGE RADAR SURROUND VIEW 3D HIGH – RESOLUTION FLASH LIDAR CAMERA (opt. LIDAR) ASS./AUT. DRIVING CONTROL UNIT LONG RANGE RADAR STEREO CAMERA eHorizon cloud services
  4. 4. Public BU ADAS 08.03.2018 4Robert Thiel Agenda Continental Product Line1 Building Blocks of AD3 AI in Autonomous Driving4 AI/AD Challenges5 SAE Levels of Driving Automation2
  5. 5. Public BU ADAS 08.03.2018 5Bhanu Prakash P. Levels of Driving Automation Level 0 Level 1 Level 2 Level 3 Level 4* Level 5 Driver Only Assisted Partial Automation Conditional Automation High Automation Full Automation Chauffeur Assistant Robot Driver Only Assistant Chauffeur ‘Vehicle supports the driver. Driver must monitor the system at all times.’ ‘Vehicle performs driving functions partially or fully.’ * machine is fallback
  6. 6. Public BU ADAS Automated Parking 2 NCAP New Car Assessment Programme 1 Partly / Highly Automated Driving 3 Self Driving Car 4 6Bhanu Prakash P. 08.03.2018 On the Way to Automated Driving
  7. 7. Public BU ADAS 08.03.2018 7Robert Thiel Agenda Continental Product Line1 SAE Levels of Driving Automation2 AI in Autonomous Driving4 AI/AD Challenges5 Building Blocks of AD3
  8. 8. Public BU ADAS 08.03.2018 8Bhanu Prakash P. Building Blocks of Automated Driving IMUHFLRADARCamera Features : › High Resolution › Wider FOVs › Driving path Geometry › Static Scene Semantics › 360° View with Fish eye lens Features : › Cheap › Velocity Accuracy › Weather Robustness › Depth Estimation › Object Detection › Tracking Features : › Low Light › 3D point cloud › Range/Depth Accuracy › Angular Resolution › Road Surface Features: › Where am I? › vehicle’s dynamics in space and time › Odometry
  9. 9. Public BU ADAS 08.03.2018 9 CEMGPSHD MapV2X Features: › Situational awareness › predictive insights, › around-corner View › Real time updates › DSRC/5G › Not affected by weather conditions Features: › Path sensing › foresight purposes › Crowd sourced › Localization Features: › For Localization › “Cm” level accuracy › GNSS/DR Bhanu Prakash P. Building Blocks of Automated Driving Features: › Understand Environment › Trajectory planning › Traffic Participants › Static Objects › Kerbs, lanes › Road Infrastructure - Signs, Traffic lights
  10. 10. Public BU ADAS 10Bhanu Prakash P. Together Solves the problem of • Where am I ? • Where is everyone else? • How do I get from A to B 08.03.2018 Building Blocks of Automated Driving
  11. 11. Public BU ADAS 08.03.2018 11Robert Thiel Agenda Continental Product Line1 SAE Levels of Driving Automation2 Building Blocks of AD3 AI/AD Challenges5 AI in Autonomous Driving4
  12. 12. Public BU ADAS AI is better than classical SW 2005 Source: http://www.vision.caltech.edu Example Pedestrian Detection (caltech pedestrian dataset) x5 x10 08.03.2018 12Bhanu Prakash P. Artificial Intelligence in AD › Deep Learning can be a solution for AD if › There is no correct „physical“ model › There is enough and the right data available › The architecture fits to the problem › We optimize for the right criterions › We can bring it to embedded hardware Examples
  13. 13. Public BU ADAS 08.03.2018 13 › Results based on state of the art Deep Learning architecture. (based on ResNet-50) › Improved by Factor of 5 Ped@Night Bhanu Prakash P. Deep Learning improves performance significantly Pedestrian Pose Estimation › Using Adverserial PoseNets
  14. 14. Public BU ADAS 08.03.2018 14 • Object Detection • TSR/TLR • Pedestrian Detection • Semantic Segmentation • Road Boundary Detection • Free space detection • Debris Detection • Driving policy & Path Planning • Driver Status Monitoring Bhanu Prakash P. Other AI Applications
  15. 15. Public BU ADAS 15Bhanu Prakash P. 08.03.2018 Deep Learning In Action
  16. 16. Public BU ADAS 08.03.2018 16Robert Thiel Agenda Continental Product Line1 SAE Levels of Driving Automation2 Building Blocks of AD3 AI in Autonomous Driving4 AI/AD Challenges5
  17. 17. Public BU ADAS 17 Challenges: Complexity / Portability Complexity Reduction Embedded Hardware 0 10 20 30 40 50 60 70 80 mAP (%) Parameters (Million) GMAC/frame Example: Object Detection @Kitti dataset „cars hard“ original (yolo v2) reduced x15 x10 08.03.2018 Bhanu Prakash P. • High performing Hardware • Lack of flexibility and multitasking • Need Lots and Lots of Data • Need Millions of miles of Labelled data • Data Augmentation © Carla Simulator
  18. 18. Public BU ADAS 18 + 2017 – Metzen et al - Universal Adversarial Perturbations Against Semantic Image Segmentation “Adversarial Examples for Semantic Image Segmentation” Fischer et al., Bosch Center for AI Bhanu Prakash P. Challenges:Adversarial Attacks 08.03.2018
  19. 19. Public BU ADAS 08.03.2018 19 Camouflage graffiti and art stickers or Perturbations cause NN to misclassify • stop signs -> speed limit 45 signs • Right turn -> stop signs Bhanu Prakash P. Challenges:Adversarial Attacks
  20. 20. Public BU ADAS 20 • AI cannot interpret data on its own. • Neural networks are essentially Blackboxes • Need networks that can be explained / Interpretable • Risk: Hundreds of signals and their combinations to plan ahead. Every time there is a mishap, should understand why a certain decision? Challenges: Explainability/Interpretability of DNN 08.03.2018 Bhanu Prakash P. David Gunning DARPA/I2O
  21. 21. Public BU ADAS 21Bhanu Prakash P. 08.03.2018 Summary Performance ScalabilityPerformance Av. Hardware Compute Power Code Complexity Data Engineering Efforts Predictability Featurebased MachineLearning Deep MachineLearning
  22. 22. Public BU ADAS 22Bhanu Prakash P. 08.03.2018 › Deep Learning Architecture › Design, Training, Validation › Deep Machine learning Solutions for Real world Problems › Computer Vision, Radar, Lidar, Sensor Fusion, Planning and Action › Deep Learning Frameworks › Tensorflow, Keras, Theano, nvidia Digits, CuDNN › Scheduling and distribution of compute jobs › Amazon AWS, HPC Cluster › Compute complexity analysis › nVidia Drive PX, Renesas HW What are we working on at Continental ADAS
  23. 23. Public BU ADAS 08.03.2018 23Bhanu Prakash P. From Research to Industry Project CUbE
  24. 24. SensePlanAct

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