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

The Importance of Timing to Autonomous Vehicle Navigation John Fischer, CTO jfischer@spectracom.com

Spectracom: Precise, Secure, Synchronized Spectracom simplifies Position, Navigation, and Timing integration into our customer’s systems. Bringing Technology to:  Military, Aerospace  UAV’s  Electronic Warfare  C4ISR  High-End Commercial Apps  Datacenters  Robotics/Telematics  IDM  GIS Data Mining 2 January 2016

Global Organization Russia UK China USA France Brazil Multi-domestic strategy 100+ Employees in 6 countries High reliability and superior service All sites ISO 9001 Registered 3 3 January 2016

Advanced Driver Assisted Systems • 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 4 January 2016

ADAS -> Driverless Car • 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

Safe and Secure Navigation • GNSS by itself is insufficient Weak signal / interference • Not always available • Tunnels • Parking garages • Urban canyons GNSS • City skyscrapers 6 January 2016

Safe and Secure Navigation • GNSS by itself is insufficient Weak signal / interference • Not always available • Tunnels • Parking garages • Urban canyons GNSS • City skyscrapers Vision Road / Map Systems Matching Hybrid system must: • be safer than a human driver • have high reliability and integrity • utilize many sensors • including real time networks Real Time Radars / Data Proximity Sensors Networking Inertial Measurement 7 January 2016

Alternative PNT in Autonomous Unmanned Systems DSRC Spotty coverage, Dedicated Short inaccurate; Skyhook GPS Cellular Range Comm – real + E911 Weak signal but Ubiquitous time networking for requirements ubiquitous in but inaccurate V2V and V2X links Automotive open sky, most accurate Active Tx autonomous Signals of Opportunity Radar, Not necessarily designed for Lidar, navigation is navigation, but useful for Sonar determining range or bearing part of a larger Vision Systems Reference Nav Inhibited by Determine subject of smoke, fog position in precipitation relation to other robotic reference points navigation in Autonomous Nav No interference or the absence of spoofing possible GPS. RFID Low cost, place IMUs Crowd-Sourced sensors where Self contained Via a network, location and needed – but not accurate proximity data is shared warehouse, over the long Map Matching controlled space term Database must be constantly updated to be current 8 January 2016

Autopilot Systems from Guided Missiles and Spacecraft to UAVs and Driverless Car over 50 years of Technology Advancement 9 January 2016

Autopilot Example – Cruise Control Automatically maintain a set speed 10 January 2016

Dynamic Response and Stability Underdamped – fast but erratic Too much delay in feedback loop – Overdamped – smooth but slow instability and oscillation Critically damped – optimum Low delay – tracking 11 2 February 2016

Closed Loop Control – a Primer Error = Setpoint – Measured Output • Control a process via feedback • Accuracy determined primarily by the sensor • PID Controller – error value drives the system Proportional Integrative Differential 12 2 February 2016

Autopilot Navigation • Setpoint <= desired trajectory or waypoint • Measured output <= GNSS realtime position sensors • Error => steering Vision Road / Map Systems Matching commands • Same process as: • Guided missiles • Spacecraft • Aircraft Real Time Radars / Data • Robotics Proximity Sensors Networking Inertial Measurement 13 2 February 2016

The Connected Car the network as part of the autopilot navigation system 14 January 2016

V2X Communications V2X communications integrated with navigation system can increase safety greatly • V2V – Vehicle to Vehicle DSRC – Dedicated Short Range Communications • V2I – Vehicle to Infrastructure 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 15 January 2016

The Connected Car – Two Separate Networks DSRC cellular Real Time / Non-Real Time / Critical User Experience Connectivity Connectivity where every millisecond matters where a few seconds is ok Internet, Infotainment, Navigation Telematics, etc. 16 January 2016

Time Sensitive Network Issues • Low latency • Predictable latency IEEE Network Specs • Reduce worst case delays 802.11p – Wireless Vehicles • Priority scheduling/pre-emption [DSRC] • Instant switching to alternate 802.1AS – Time Sync paths • Ensure delivery 1588v2 – Precise Time Protocol • Reliable for critical operations 802.1Qac – Path Control • Under fading conditions 802.1Qbv – Scheduled Traffic • Congestion and Doppler 802.1Qbu – Pre-emption • Fault tolerance and redundancy 802.1Qca – Path Control • Security and Privacy • 802.1Qcb – Seamless Redundant Time delay implies distance • Regulatory compliance 802.11Qcc – Stream Reservation • Scalable to larger networks 802.11Qci – Filtering and Policing 802.11Qv – Time Mgmt Protocol 17 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 18 January 2016

Framework for Simulation and Test Visualization Instrumentation Traffic – Vehicles, Pedestrians Test equipment Vision Sensors Network Connections to monitor View into signal Navigation simulated points stimuli and responses Autopilot VUT Vehicle Under Test Enhanced Simulation Vehicle Dynamics and Motion Control Road, Hazards and Weather Conditions Traditional Test 19 January 2016

Simulation vs. Test • Fully simulated models in Matlab or similar tools Simulation • Target system, environment, test stimulus all simulated Model • SW models replaced by executable code for the real target • HW, environment, test stimulus all simulated SIL • Selected components replaced with target HW and SW -- ECUs • Mixed of simulation and test HIL • Real code and HW • Simulated environment with mix of some real stimuli Lab • Target system fully integrated • Controlled environment and stimuli – test track Field • Human in the loop – road test • ADAS – human in VUT; Driverless – humans in other cars User Live Testing 20 January 2016

Summary • 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 21 January 2016

Acknowledgements • 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 22 January 2016