the importance of timing to autonomous vehicle navigation
TRANSCRIPT
2 January 2016
Spectracom: Precise, Secure, Synchronized
Bringing Technology to:
Military, Aerospace
UAV’s
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C4ISR
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Datacenters Robotics/Telematics IDM GIS Data Mining
Spectracom simplifies Position, Navigation, and Timing integration into our customer’s systems.
3 January 2016
Global Organization
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• 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
• 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
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
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
Map Matching Database must be constantly updated to be current
IMUs Self contained but not accurate over 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 open 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.
Autopilot Systems
from Guided Missiles and Spacecraft
to UAVs and Driverless Car
over 50 years of Technology Advancement
9 January 2016
10 January 2016
Autopilot Example – Cruise Control
Automatically maintain a set speed
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
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
• 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
The Connected Car the network as part of the autopilot navigation system
14 January 2016
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
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
• 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
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
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
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
• 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
• Hiro Sasaki – Director, Architected Solutions • [email protected]
• Lisa Perdue – GNSS Systems • [email protected]
• Gilles Boime – Senior Scientist • [email protected]
• Emmanuel Sicsik-Pare -- Strategic Product Mgr • [email protected]
• John Fischer - CTO • [email protected]
Acknowledgements
22 January 2016