Stmicroelectronics Nv · EURONEXT:STMPA
Stmicroelectronics Nv — Technical Presentation (2019)
41 slides from Stmicroelectronics Nv's technical presentation, 2019
Slide 1: Automotive ADAS Systems
Automotive ADAS Systems Overall Automotive ADAS System
Slide 2: Table of Contents
Table of Contents 2 • ADAS overview • ADAS Vehicle Architectures • ADAS Technologies/Sensors • Vision(Cameras) System • LiDAR System • Radar System • GNSS/IMU System • V2X System • Sensor Fusion Example ST Confidential
Slide 3: ADAS Overview
Automotive ADAS Systems ADAS Overview
Slide 4: Overview of ADAS Technologies
Overview of ADAS Technologies 4 Targets Power Supply/Management
Slide 5: ADAS Sensors - Needed for Perception
ADAS Sensors - Needed for Perception 5 LIDAR Cameras GNSS antenna Radar Central Computer Ultra-Sonics Wheel Odometry
Slide 6: The Levels of Vehicle Automation
The 5 Levels of Vehicle Automation 6 Adding Senses Learning to Drive • Systems Networking • Accelerometers and Gyro • Sensor Fusion • Steering Wheel Angle • Distance Measurement • Ultrasonic sensors • Traffic Sign Recognition • Front Radar Sensor • Lane Reconstruction • Blind Spot sensor • Rear View
Slide 7: Sensor Fusion is Key to Autonomous
Sensor Fusion is Key to Autonomous 7 Source: Woodside Capital Partners (WCP), “Beyond the Headlights: ADAS and Autonomous Sensing”, September 2016
Slide 8: ADAS Vehicle Architectures
Automotive ADAS Systems ADAS Vehicle Architectures
Slide 9: Distributed vs Centralized Processing
Distributed vs Centralized Processing 9 Distributed Processing with Object Level Fusion Centralized Processing with Raw Data Fusion Intelligent Raw Data Sense Edge Sense Capture Think! Think! ACT ACT Processing (I/Q) Ultrasonic Processor Ultrasonic No Processing Object data · Raw Data · Breaking Ear
Slide 10: Distributed vs Centralized Processing
Distributed vs Centralized Processing 10 Source: 2018 IHS Markit – “Autonomous Driving-The Changes to come” Source: ADI • What are the Data rates requirements for each sensor? • Centralized (i.e. SERDES?) vs Distributed (i.e. ETH?) • Example: 4-5 Corner Radars are utilized in high end/premium vehicl
Slide 11: Vision (Cameras) System
Automotive ADAS Systems Vision (Cameras) System
Slide 12: Camera
Camera 12 • Essential for correctly perceiving environment • Richest source of raw data about the scene - only sensor that can reflect the true complexity of the scene. • The lowest cost sensor as of today • Comparison metrics: • Resolution • Field of view (FOV) • Dynamic range • Trade-off between r
Slide 13: Camera-Stereo
Camera-Stereo 13 • Enables depth estimation from image data All points on projective line to P map to p Left and right images One camera Add a second camera Find a point in 3D by triangulation! Source: Sanja Fidler, CSC420: Intro to Image Understanding
Slide 14: The Next Phase for Vision Technology
The Next Phase for Vision Technology 14 3 30° • From sensing to comprehensive perception • Machine learning used already for object sensing 50° 2 • Autonomous driving needs • Path planning based on holistic cues • Dynamic following of the drivable area 1 150° • Deep learning is now being applied
Slide 15: Machine Vision: ST & Mobileye
Machine Vision: ST & Mobileye 15 EyeQ3™ 3rd Generation Productvision processor EyeQ4™ 4th Generation enables Product • Detection of driving lanes • Detection of more objects, more precisely • Recognition of traffic signs • More features required for automated driving • Detection of pedestrians and c
Slide 16: LiDAR System
Automotive ADAS Systems LiDAR System
Slide 17: LiDAR Technology Overview
LiDAR Technology Overview 17 • LiDAR (light detecting and ranging, or “light radar”) sensors send one or more laser beams at a high frequency and use the Time-of- Flight principle to measure distances. LiDAR capture a high- resolution point cloud of the environment. • Can be used for object detectio
Slide 18: LiDAR Techniques
LiDAR Techniques 18 • There are multiple techniques currently under evaluation for LiDAR including rotating assembly, rotating mirrors, Flash (single Tx source, array Rx), scanning MEMS micro-mirrors, optical phased array. • From a transmitter/receiver (Tx/Rx) perspective the following technologies
Slide 19: LiDAR Summary
LiDAR Summary 19 • Autonomous vehicles have been around for quite some time but only now the technologies are available for practical implementations • No single sensor solution exists to cover all aspects – range, accuracy, environmental conditions, color discrimination, latency etc. • Multi-sensor
Slide 20: Radar Systems
Automotive ADAS Systems Radar Systems
Slide 21: RADAR Technology Overview
RADAR Technology Overview 21 • RADAR (RAdio Detection and Ranging) is one necessary sensor for ADAS (Advanced Driver Assistance System) systems for the detection and location of objects in the presence of interference; i.e., noise, clutter, and jamming. • Robust Object Detection and Relative Speed E
Slide 22: RADAR Techniques
RADAR Techniques 22 • Comparison metrics: • Range • Field of view • Position and speed accuracy • Configurations: • Wide-FOV: Short Range • Narrow-FOV: Long Range Source: Strategy Analytics Lunch & Learn the Market Session European Microwave Week 2013
Slide 23: Automotive Radar Vs. Automation Levels
Source: Rodhe & Schwarz - Automotive radar technology, market and test requirements, White paper – Oct 2018 (Salvo S. presentation) Automotive Radar Vs. Automation Levels 23 < 2014 2016 2018 2019 / 2020 > 2028 Level 1 Level 2 Level 3 Level 4 Level 5 Driver Assistance Partial Automation Conditional A
Slide 24: GNSS/IMU System
Automotive ADAS Systems GNSS/IMU System
Slide 25: GNSS/IMU Positioning
GNSS/IMU Positioning 25 • Global Navigation Satellite Systems and Inertial Measurement Units GNSS/IMU • Direct measure of vehicle states • Positioning, velocity, and time (GNSS) • Varying accuracies: Real-time Kinematic (RTK- short base line), Precise Point Positioning (PPP), Differential Global Pos
Slide 26: GNSS/IMU Positioning
GNSS/IMU Positioning 26 More Precision Enables More Safety Features Precise Positioning: Towards Autonomous Driving Precise Positioning to enable < 30cm precision GPS • Lane detection GLONASS BeiDou • Positioning data for V2X sharing Galileo QZSS SBAS • Collision avoidance Carrier Phase 0 RTK <30cm
Slide 27: Precise GNSS is a Critical ADAS Sensor
Precise GNSS is a Critical ADAS Sensor 27 Higher integrity requirements across safety-critical applications • Semi- and Autonomous driving safety-related applications requirements increase HPL – Horizontal Protection Level • Higher safety levels VPL – Vertical Protection Level Bad Solution • Added r
Slide 28: Precise GNSS is a Critical ADAS Sensor
Precise GNSS is a Critical ADAS Sensor 28 GNSS Accuracy in Automotive Environment (using PPP – Precise Point Positioning) Single Frequency (i.e. L1) multi- constellation/code- phase(1msec modulation signal) Multi Frequency (i.e. L1, L2) multi- constellation/carrier- phase APP: ASIL Precise Positioni
Slide 29: Precise GNSS is a Critical ADAS Sensor
Precise GNSS is a Critical ADAS Sensor 29 GNSS Integrity – Protection Levels
Slide 30: V2X System
Automotive ADAS Systems V2X System
Slide 31: Vehicle-to-Everything (V2X)
Vehicle-to-Everything (V2X) 31 V2X V2V V2I V2M V2D V2P Vehicle-to- Vehicle-to- Vehicle-to- Vehicle-to- Vehicle-to- Vehicle Infrastructure Motorcycle Device/object Pedestrian
Slide 32: FCC Spectrum Allocation for DSRC of ITS
FCC Spectrum Allocation for DSRC of ITS 3232 44.8 Gov’t Only EIRP [dBm] (not to scale) 40.0 use Limit 33.0 Dedicated Public Collision Avoidance Channel 175 Channel 181 Services on other (V2V) & Safety of Control Channel Channel 184 23.0 Shared Public Shared Public Safety/Private Safety/Private Chann
Slide 33: DSRC
DSRC 33 • Wireless Access in Vehicular Environments (WAVE) • Amendment to IEEE 802.11-2012 to support WAVE/DSRC • no authentication, no access point/no association • 5.8 – 5.9 GHz OFDM NLOS • Broadcasts BSMs 10 times per second • Fast Network Acquisition & low latency (<50msec) • Transmit power are
Slide 34: C-V2X Basics
C-V2X Basics 34 • C-V2X is a V2X radio layer: • C-V2X is Device-to-Device (D2D) communication service added to the LTE Public Safety ProSe (Proximity Services) Services • C-V2X makes use of the D2D interface – PC5 (aka Side Link) for direct Vehicle-to-Everything communication • C-V2X takes the place
Slide 35: C-V2X Basics
C-V2X Basics 35 • C-V2X Transmission Mode 4: • Mode 4 – Stand alone, distributed • Uses GNSS for location and time for synchronization Transmission Mode 4 PC5
Slide 36: C-V2X Basics
C-V2X Basics 36 • Transmission Mode 4: • Out of Coverage operation: The transmitting Transmission Mode 4 vehicle is not connected to the network • No SIM card or inter-operator collaboration is PC5 required • Each vehicle performs its own scheduling and PC5 PC5 allocation • No dependency on inter-ve
Slide 37: C-V2X Air Interface
C-V2X Air Interface 37 • C-V2X is based on LTE (4G) uplink transmission - SC-FDMA (Single Carrier Frequency Division Multiple Access) signal: • A single carrier multiple access technique which has similar structure and performance to OFDMA • Utilizes single carrier modulation and orthogonal frequenc
Slide 38: Both Technologies will do the JOB!
In Summary 38 Both Technologies will do the JOB! But: • Industry is waiting for regulatory certainty, Government Mandate is preferred! • C-V2X has to reach automotive production maturity • Implementation and deployment will depend on OEM system architecture • The market will demand standalone V2X mo
Slide 39: Sensor Fusion Example
Automotive ADAS Systems Sensor Fusion Example
Slide 40: Multi-sensor Fusion for State Estimation
Multi-sensor Fusion for State Estimation 40 Source: “State Estimation and Localization for Self-Driving Cars”, Coursera by University of Toronto
Slide 41: Thank You!
Thank You! Q&A