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Junli Gu at AI Frontiers: Autonomous Driving Revolution

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Autonomous driving has gain enormous attention and momentum in the past year, due to its potential huge impact on car industry. Junli Gu's talk summarizes the current trends and on-going efforts of driver-less cars. Her talk highlights the technical challenges and share some insights in how machine learning might lead us to the path.

Published in: Technology

Junli Gu at AI Frontiers: Autonomous Driving Revolution

  1. 1. Autonomous driving revolution: trends, challenges and machine learning Junli Gu 1
  2. 2. Outline • Vehicle revolution • Challenges: when Big Data meets Machine Learning in the car • Sensor System collects Big Data • Machine Learning perceives the real world • Car is a Digital Agent • Conclusions 2
  3. 3. Transportation evolution history 3 P1: Analog device P2: Digitalization P3: Intelligence 1900 2020 P0: Primitive
  4. 4. A revolution from analog to digital to intelligence 4 • All parts are digital controllable • Complex sensor systems • Driving through perception • Controlled by computers • A car will become an agent • With autonomous behavior
  5. 5. Big Data meets Machine Learning in the Car 5 World Perception GPS, mapping, localization Motion Control Path Planning • Real world is a Big Data problem • Driving in human world required intelligent perception of the world
  6. 6. Hybrid sensor system collects Big Data • A sensor system composed of Cameras, Radar, Ultrasound, Lidar • Sensor fusion technology is not mature yet • Different sensor data is mostly computed separately 6
  7. 7. Case study in combining sensors • Google self driving cars • Velodyne 64-beam laser + 4 radars+ 1 camera+ GPS • generates a 3D geometry map of the environment • Radar info is too thin to differentiate objects of same size, same speed • Cameras see rich semantics. What you see is what you get. But no depth 7 What radar sees What camera sees
  8. 8. Machine learning perceives the real world • Large scale of 2D object recognition is better than human • What objects are around • 3D scene understanding and modeling • Where is the object • Semantic segmentation • Extent of the obstacles • Reinforcement learning • Policy (reward or penalty based learning) • End to end learning • From raw data direct to behavior, like a robot 8
  9. 9. Deep learning based 2D image recognition 9 97% 2016 • Large scale object recognition 97% accuracy • Lane detection, pedestrian detection, vehicle detection • Animal detection, and road surface detection etc. • Case: Mobile eye from traditional algorithm => Deep learning DNN model Big Data Human 95%
  10. 10. 3D scene understanding • Real world is not flat. It is 3D. • Depth information is critical for driving. • 3D model is far more complicated. 10 Reference: Lin Yuanqing, “Building Blocks for Visual 3D Scene Understanding towards Autonomous Driving”
  11. 11. Semantic segmentation • Understand the extent of the object and each pixel of it • Advanced requirement based on recognition and detection • Eventually 3D world segmentation 11 Above: “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation” Right: “Object Scene Flow for Autonomous Vehicles”
  12. 12. Reinforcement learning • Goal: to win • Policy based learning • Learning task: next move at arbitrary states • Algorithm: reinforcement learning + DNN • Similar methodology can be applied to driving • Driving Policy: Given safety, spend least time to get to destination 12
  13. 13. End to end learning • Use machine learning as the only step from raw data to control • Without human interference noise in the middle steps • DaveNet: AI teaches the car how to drive • NVidia GTC 2016 “after 3000 miles of supervised driving, their car can navigate safely on freeways and country roads, even on rainy days.” “End to End Learning for Self-Driving Cars”, arxiv.org ; Google “Human-level control through deep reinforcement learning” 13
  14. 14. Other challenges in the Car • Embedded computers in the car • Cloud solution • Vehicle external connections (V2E) • Vehicle internal connection 14
  15. 15. Embedded computing systems • Real time computing for the sensor array’s Big Data input • Heterogeneous SoC + HPC level computing (Tera flops) • Reliability is another key 15 ARM DSP GPU accelerator General purpose Control friendly Customized processor machine learning Computing throughput Mobile eye Google TPU NVidia drive PX Qualcomm snapdragon
  16. 16. Cloud based solution • Agent+ system: cloud updates with traffic condition, bad weather, etc. (Toyota) • Connect smart home with car (Volks Wagen, with LG software) • Challenges: network reliability and real time response 16 cloud Collect traffic info broadcast info Collect smart home
  17. 17. V2E (vehicle to everything) network • Collectively become smarter: share learning on the network • Dephi: V2V(vehicle), V2B(building), V2P(pedestrain) • Tesla: fleet learning. Baidu: vehicle to traffic lights 17
  18. 18. High speed internal interconnect • All digital components communicate • High speed due to many sensor • Reliability and redundant design 18
  19. 19. Conclusions • Cars are transforming from analog to digitalization and intelligence • Future cars are a digital autonomous device, similar to Robots • When Big Data meets Machine learning in the car • Complex sensor system collects the Big Data info • Machine learning algorithms will be the key to perceive the real world • Computing in the car is another challenge • Cars will be connected to each other and the cloud • Whoever addresses the technical challenge will harvest the influence.

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