[PPT] - The Importance of Timing to Autonomous Vehicle Navigation John PowerPoint Presentation

SLIDE 1

The Importance of Timing to Autonomous Vehicle Navigation

John Fischer, CTO jfischer@spectracom.com

SLIDE 2

2 January 2016

Spectracom: Precise, Secure, Synchronized

Bringing Technology to:

Military, Aerospace
UAV’s
Electronic Warfare
C4ISR
High-End Commercial Apps
Datacenters
Robotics/Telematics
IDM
GIS Data Mining

Spectracom simplifies Position, Navigation, and Timing integration into our customer’s systems.

SLIDE 3

3 January 2016

Global Organization

3

Multi-domestic strategy 100+ Employees in 6 countries High reliability and superior service All sites ISO 9001 Registered

USA Brazil UK France Russia China

SLIDE 4

ADAS already in luxury cars and moving to mainstream
Anti-lock brakes and anti-slip traction control
Lane departure warning system
Speed assistance and autonomous emergency braking
Automatic parking
Driver wake-up and attention control
Pedestrian and low-speed obstacle avoidance

Advanced Driver Assisted Systems

4 January 2016

SLIDE 5

Mix of correlated sensors for

navigation

Radar and proximity sensing
Optical, vision systems
GNSS
Accurate map matching
New regulations for safety
Four US states have laws for

autonomous vehicles on public roads

Google – over 1 million miles tested
UK -2013 – testing on public roads
France – 2015 – 2000 km roads for

testing – Peugeot-Citroen

Toyota Lexus GS autonomous car on

Tokyo expressways

Canada – starting 2016 testing

5 January 2016

ADAS -> Driverless Car

SLIDE 6

6 January 2016

Safe and Secure Navigation

GNSS

GNSS by itself is insufficient

Weak signal / interference
Not always available
Tunnels
Parking garages
Urban canyons
City skyscrapers

SLIDE 7

7 January 2016

Safe and Secure Navigation

GNSS Vision Systems Radars / Proximity Sensors Inertial Measurement Road / Map Matching Real Time Data Networking

GNSS by itself is insufficient Hybrid system must:

be safer than a human driver
have high reliability and integrity
utilize many sensors
including real time networks
Weak signal / interference
Not always available
Tunnels
Parking garages
Urban canyons
City skyscrapers

SLIDE 8

Map Matching Database must be constantly updated to be current IMUs Self contained but not accurate

ver the long

term Autonomous Nav No interference or spoofing possible Reference Nav Determine position in relation to other reference points GPS Weak signal but ubiquitous in

pen sky, most

accurate Vision Systems Inhibited by smoke, fog precipitation Spotty coverage, inaccurate; Skyhook + E911 requirements Cellular Ubiquitous but inaccurate RFID Low cost, place sensors where needed – warehouse, controlled space Active Tx Radar, Lidar, Sonar Crowd-Sourced Via a network, location and proximity data is shared Signals of Opportunity Not necessarily designed for navigation, but useful for determining range or bearing DSRC Dedicated Short Range Comm – real time networking for V2V and V2X links

8 January 2016

Alternative PNT in Autonomous Unmanned Systems

Automotive autonomous navigation is part of a larger subject of robotic navigation in the absence of GPS.

SLIDE 9

Autopilot Systems

from Guided Missiles and Spacecraft to UAVs and Driverless Car

ver 50 years of Technology Advancement

9 January 2016

SLIDE 10

10 January 2016

Autopilot Example – Cruise Control

Automatically maintain a set speed

SLIDE 11

Underdamped – fast but erratic Overdamped – smooth but slow Critically damped – optimum

11 2 February 2016

Dynamic Response and Stability

Too much delay in feedback loop – instability and oscillation Low delay – tracking

SLIDE 12

12 2 February 2016

Closed Loop Control – a Primer

Control a process via feedback
Accuracy determined primarily by the sensor
PID Controller – error value drives the system

Proportional Integrative Differential

Error = Setpoint – Measured Output

SLIDE 13

Setpoint <= desired

trajectory or waypoint

Measured output <=

realtime position sensors

Error => steering

commands

Same process as:
Guided missiles
Spacecraft
Aircraft
Robotics

13 2 February 2016

Autopilot Navigation

GNSS Vision Systems Radars / Proximity Sensors Inertial Measurement Road / Map Matching Real Time Data Networking

SLIDE 14

The Connected Car the network as part of the autopilot navigation system

14 January 2016

SLIDE 15

15 January 2016

V2X Communications

V2V – Vehicle to Vehicle
V2I – Vehicle to Infrastructure

V2X communications integrated with navigation system can increase safety greatly 1. Real time data network

Traffic lights
Emergency vehicles
Construction zones

2. Coordination / early warning

Advanced braking
Platooning

3. “Crowd-sourced” location

Shared location
Proximity detection

DSRC – Dedicated Short Range Communications

SLIDE 16

16 January 2016

The Connected Car –Two Separate Networks

Real Time / Critical Connectivity Non-Real Time / User Experience Connectivity where every millisecond matters where a few seconds is ok

Navigation Internet, Infotainment, Telematics, etc.

cellular DSRC

SLIDE 17

Low latency
Predictable latency
Reduce worst case delays
Priority scheduling/pre-emption
Instant switching to alternate

paths

Ensure delivery
Reliable for critical operations
Under fading conditions
Congestion and Doppler
Fault tolerance and redundancy
Security and Privacy
Time delay implies distance
Regulatory compliance
Scalable to larger networks

17 January 2016

Time Sensitive Network Issues

IEEE Network Specs 802.11p – Wireless Vehicles

[DSRC]

802.1AS – Time Sync 1588v2 – Precise Time Protocol 802.1Qac – Path Control 802.1Qbv – Scheduled Traffic 802.1Qbu – Pre-emption 802.1Qca – Path Control

802.1Qcb – Seamless Redundant

802.11Qcc – Stream Reservation 802.11Qci – Filtering and Policing 802.11Qv – Time Mgmt Protocol

SLIDE 18

18 January 2016

Time Sensitivity for Automotive Networks

Let’s do the numbers [Order of Magnitude]

60 mph => 100 km/hr

30 m/s => 3 cm/millisecond

System level response => msec

=> 1KHz update rates minimum

Subsystem responses =>

10 – 100 usec range

Network latency =>

< 100 usec over multiple hops [5-7]

alternate fault tolerant paths
all Bit Error Rate [BER] conditions

Latency is key if the network is part of the control loop:

Stability
Dynamic performance

SLIDE 19

19 January 2016

Framework for Simulation and Test

Visualization

View into simulated stimuli and responses Enhanced Simulation

Instrumentation

Test equipment to monitor signal points Traditional Test

Vision Sensors Network Connections Vehicle Dynamics and Motion Control Road, Hazards and Weather Conditions Autopilot VUT Navigation Traffic – Vehicles, Pedestrians Vehicle Under Test

SLIDE 20

20 January 2016

Simulation vs. Test

Model

Fully simulated models in Matlab or similar tools
Target system, environment, test stimulus all simulated

SIL

SW models replaced by executable code for the real target
HW, environment, test stimulus all simulated

HIL

Selected components replaced with target HW and SW -- ECUs
Mixed of simulation and test

Lab

Real code and HW
Simulated environment with mix of some real stimuli

Field

Target system fully integrated
Controlled environment and stimuli – test track

User

Human in the loop – road test
ADAS – human in VUT; Driverless – humans in other cars

Simulation Live Testing

SLIDE 21

Driver assisted and driverless cars are here today…
…requiring very complex navigation systems
Much more than just GNSS
INS, mapping, radars, vision systems, realtime networks
Simulation and Test must address interaction effects in

complex control loops

Traffic and road conditions, objects, weather
Wireless network latency a key factor in the control

system

Fault tolerance, route changes, re-transmission, multiple hops
Time Sensitive Networks

Summary

21 January 2016

SLIDE 22

Hiro Sasaki – Director, Architected Solutions
hsasaki@spectracom.com
Lisa Perdue – GNSS Systems
lperdue@spectracom.com
Gilles Boime – Senior Scientist
gboime@spectracom.com
Emmanuel Sicsik-Pare -- Strategic Product Mgr
Emmanuel.sicsik-pare@spectracom.orolia.com
John Fischer - CTO
jfischer@spectracom.com

Acknowledgements

22 January 2016