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"Improving the Safety and Performance of Automated Vehicles Through Precision Localization," a Presentation from VSI Labs

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For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-magney

For more information about embedded vision, please visit:
http://www.embedded-vision.com

Phil Magney, founder of VSI Labs, presents the "Improving the Safety and Performance of Automated Vehicles Through Precision Localization" tutorial at the May 2019 Embedded Vision Summit.

How does a self-driving car know where it is? Magney explains how autonomous vehicles localize themselves against their surroundings through the use of a variety of sensors along with precision maps. He explores the pros and cons of various localization methods and the trade-offs associated with each of them. He also examines the challenges of keeping mapping assets fresh and up-to-date through crowdsourcing.

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"Improving the Safety and Performance of Automated Vehicles Through Precision Localization," a Presentation from VSI Labs

  1. 1. © 2019 VSI Labs Improving the Safety & Performance of Automated Vehicles Through Precision Localization Phil Magney VSI Labs May 2019
  2. 2. © 2019 VSI Labs VSI’s Range of Research 2 Industry Research & Advisory Testing & Demonstration Engineering Services • Applied research on active safety and automated driving since 2014 • Supporting R&D & planning for automotive, suppliers and technology industry • VSI Offers various research portals
  3. 3. © 2019 VSI Labs Automated Vehicle Trajectories Series Production Low Speed Shuttles Robo-Taxis 3
  4. 4. © 2019 VSI Labs Automated Driving Today – Series Production Cars Automated driving is born from active safety systems (active lane keeping, adaptive cruise control, etc.) Most L2 vehicles use camera and radar Lane centering, no organic path planning Localization – most L2 vehicles rely on SLAM methods 4
  5. 5. © 2019 VSI Labs Automated Driving Today -- Shuttles Driverless Shuttles – low speed vehicles being developed as fully automated people movers Shuttles operate on a “virtual rail” Shuttles will also use various other sensors for collision avoidance Localization -- Shuttles use correction services to localization against a pre- mapped course 5
  6. 6. © 2019 VSI Labs Automated Driving Today -- Robo Taxis Many companies are developing L4 “robo-taxis” for deployment in metro areas under a ride sharing model Robo-taxis use many sensors including redundant camera, Lidar, Radar, HD maps, etc. Localization -- Precision localization against HD maps, mainly using lidar 6
  7. 7. © 2019 VSI Labs The Tech Behind Automated Vehicle Systems Sensors Processors Software 7
  8. 8. © 2019 VSI Labs Anatomy of an Automated Vehicle 8
  9. 9. © 2019 VSI Labs The Automation Pipeline 9
  10. 10. © 2019 VSI Labs Sensor Configurations L1 Active Safety Features L2 Lane Level Automation L2+ Multi Lane Automation L3 Traffic Jam Pilot and Hwy Pilot L4/L5 Robo-taxi / MaaS Camera Front facing Rear facing (passive) Front facing Rear facing (passive) Front facing x 2 Side facing x 2 Rear facing Driver facing Front facing x 2 Side facing x 2 Rear facing Driver facing Front facing x 3 Side facing x 4 Rear facing x 2 Thermal x 2 Driver facing Radar Med-long range front Short range side x 2 Med-long range front Short range side x 2 Med-long range front Short range side x 2 Short range front Med-long range Short range side x 2 Short range front Med-long range Short range side x 2 Imaging radar x 2 Ultrasonic 360 degree 360 degree 360 degree 360 degree 360 degree LiDAR Solid state Solid state x 2 Solid state x 2 360 degree x 2 HD Maps ADAS model ADAS model Lane model Localization model ADAS model Lane model Localization model ADAS model Lane model Localization model Inertial IMU IMU IMU IMU IMU GNSS + Correction RTK RTK RTK Total Sensors 7 8 16 18 28 Notes: This are proxy estimates based on OEM and fleets planned announcements. Sensor distribution may vary greatly among OEMs. Maps are counted as a sensor as well as IMU, GNSS devices. Surround view applications are not included in this chart. Ultra sonic is counted as one rather than discrete units. L3 – L5 are largely development sensor configurations, final production may differ. Automotive Sensor Proxy 10
  11. 11. © 2019 VSI Labs Improving Safety Through Precision Maps 11
  12. 12. © 2019 VSI Labs Limitations of Vision-based Automation (basic L2) 12
  13. 13. © 2019 VSI Labs Limitations of AVs in Poor Weather? 13 Sensors Fail • Camera & Lidar fail quickly • Radar still robust Infrastructure Fails • Lanes lines covered • Localization assets hard to recognize if they are covered in snow What Does it Take? • Precision localization – through correction services • V2I – through embedded cellular, DSRC • Virtual Infrastructure – Precision Maps (lanes models, intersections, and other metadata) • Low Grip Algorithm
  14. 14. © 2019 VSI Labs Absolute Localization (Ground Truth) 14 • GPS based accuracy is not enough and suffers from outages • Heighten the fixes with correction (RTK services) to w/I a few centimeters • Once base accuracy is enough, then you need to localized against it between fixes • Wheel odometry uses a starting position and wheel speed to estimate a position from a fixed point • Inertial measurement unit (IMU) senses the movement of the vehicle in time via yaw, pitch, roll axis and magnetic • Visual odometry estimates vehicle motion from a sequence of camera frames
  15. 15. © 2019 VSI Labs Relative Localization With Landmarks • Landmark localization - matches landmarks and objects (signs, poles, and other permanent objects • In landmark-based approaches the objects are classified • The challenge is the creation and maintenance of these maps, requires human annotation 15 Source: Towards Data Science
  16. 16. © 2019 VSI Labs Relative Localization With Lane Models • Lane models provide virtual lane and centerline trajectories from which to localize against • This is particularly important when lanes lines are obstructed (i.e. covered in snow) • Lane models is vital to multilane highways and intersection traversals • The challenge is the creation and maintenance of these maps 16
  17. 17. © 2019 VSI Labs What are Precision Maps & Who Supplies Them • Road model (ADAS Map) • Topology • Routing • Speed attributes, etc.) • Lane Models • Lane geometry • Polylines • Trajectories • Localization Layer • Landmarks • Signs, barriers, poles etc. • Edges and boundaries • Voxels • Confidence Index 17
  18. 18. © 2019 VSI Labs How to Keep the Data Fresh • Mapping data are always changing • Temporary lane closures or detours • Changes to the infrastructure (bridges, landmarks, poles, lights) • How to detect changes, collect, then validate those changes • Requires fleets to crowd source • Requires connected vehicles • Requires standardization of message sets 18
  19. 19. © 2019 VSI Labs Elements of Mapping Assets 19 Trajectories • Geocoded Paths Lane Models • Lane Geometry ADAS Map • Speeds/Slopes/Curves Localization Layers Raw Images Raw Point Clouds Vixels (Voxels + RGB data) Voxels (clusters of points) Compressed Point Cloud Object List (landmarks) VolumeofData Sense and React Sense and Align Sense and Position HERE HD Live Maps
  20. 20. © 2019 VSI Labs Closing Remarks • Automation in series production is an extension of ADAS. • Couple camera and radar and you have L2 capabilities. • Adding precision maps substantially improves the performance and safety of automated vehicles: • Mapping assets to improve localization • Lane models tell you where you can and cannot operate! • ADAS attributes add speeds, curve, slope and other important metadata • Shuttles are a pragmatic approach to automation • No path planning as you are using a recorded path to create a virtual rail • Robo-taxis will apply everything we have talked about times three • They will be highly constrained (largely because of SOTIF – Safety Of the Intended Function) 20
  21. 21. © 2019 VSI Labs Contact 21 Resources www.VSL-Labs.com Sign Up for Free White Papers Request Services Agreement Client Portal Registration Page Contact Phil Magney +1-952-215-1797

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