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Emerging Automotive AI Technologies for Autonomous Driving and Infotainment


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Visteon presented at the Global AI and New Business Summit in Shanghai, China June 2018.

Published in: Automotive
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Emerging Automotive AI Technologies for Autonomous Driving and Infotainment

  1. 1. Emerging Automotive AI Technologies for Autonomous Driving and Infotainment June 14th, 2018 Vijay Nadkarni Global Head, Artificial Intelligence
  2. 2. Two Movements in Automotive -- Autonomous Driving & Infotainment 2 Autonomous Driving Infotainment Safety improvement Productivity or relaxation in the car Alleviation of traffic congestion Reduction of air pollution Transportation of goods Ride-sharing Driver engagement Driver productivity Superior in-vehicle experience App store in the vehicle Driver recommendations In-vehicle monetization
  3. 3. Autonomous Driving
  4. 4. The Building Blocks of Autonomous Driving AI AI Sensor fusion (cameras, LIDAR, RADAR, GPS, IMU, etc.) Object Detection & Classification Actuator Control (steering, throttle control, braking) Vehicular Maneuvers (aka Path Planning)
  5. 5. The Need for AI in Autonomous Driving Recognition of environment Convolutional neural networks (CNN) Deciding on and executing vehicular maneuvers Reinforcement learning (RL) Predicting uncertain road situations Recurrent neural networks (RNN)
  6. 6. Autonomous Driving: Object Detection and Classification • Objective: Detection of vehicles, drivable surfaces, pedestrians and traffic signs • Technology: Convolutional neural networks (CNNs) • Detection accuracy: 85% - 99.99% typical for a well-trained CNN • Silicon: GPUs and FPGAs are the most common • Prominent silicon vendors: nVIDIA, Qualcomm, Intel/Mobileye, NXP, Renesas… Vehicles Traffic signs Pedestrians Drivable surfaces 6
  7. 7. Reinforcement Learning – Used for Vehicular Maneuvers • Agent observes state of environment • Agent takes an action to achieve a benefit • Receives a reward based on outcome • Learns optimal behaviour to maximize reward • Sample simulation for Adaptive Cruise Control: • Ego vehicle collides with lead vehicle • Ego vehicle maintains safe distance • Ego vehicle maintains unsafe distance • Ego vehicle closes in and backs of continuously
  8. 8. Example: Overtaking Maneuver using Reinforcement Learning
  9. 9. Some Vehicular Maneuvers that Can be Handled by RL • Adaptive cruise control • Overtaking with lane change • Traffic congestion (stop and go traffic) • Merge onto highway from entrance ramp • Merge off highway onto exit ramp • Narrowing of lanes • Passing a construction zone • Passing an accident site • Stopping at a traffic light • Stopping at a stop sign • Left or right turn at intersection • Merging into roundabout
  10. 10. Infotainment
  11. 11. AI-based Automotive Use Cases for Infotainment Driver Productivity Take the office into the car Email read-backTake appointments from car Semantic personal assistant Recommendations Learn the driver’s profile and suggest contextual recommendations Observe drivers points of interest Recommendations Learn the driver’s actions and make contextual recommendations Nearby conveniences (bank, gas, fast-food…) careful driver likes Coldplay prefers Shell gas Learn driver’s profile, In and out of car
  12. 12. Driver Productivity – Calendar Integration with One-touch Phone Call Platform reads calendar & notifies driver at time of appt You have a phone appointment with John Howard. Shall I place the call? Yes No Platform extracts phone number using AI Create a calendar appointment Driver enters or speaks “Yes” Platform places call automatically
  13. 13. Recommendations Make contextual suggestions to driver based on the learnt profile Enable OEMs to do anonymized targeted marketing to drivers Use AI to learn the driver’s profile (preferences & brands) Correlate each stopping location with point(s) of interest there Track car’s stopping locations and note GPS at each point
  14. 14. Personal Assistant: Recurrent Neural Networks for NLP • Lexical Analysis: Spelling of words • Syntactic Analysis: Grammatical structure and arrangement of words • Semantic Analysis: Literal meaning of sentences The dog fetched the ball Lexical analysis Syntactic analysis Semantic analysis The cloud ate the ship Lexical analysis Syntactic analysis Semantic analysis X The boi flew the kyte Lexical analysis Syntactic analysis Semantic analysis X
  15. 15. What’s Next in The Automotive Industry Some amazing AI technologies are for autonomous driving & infotainment Adversarial reinforcement learning Capsule networksLeanandaugmenteddatalearning GANs Level 4 autonomous applications will be commercial in 3-4 years… Comprehensive safety regulations and DMV certifications will emerge