![17
❑ Driving by human beings is found to be dangerous and has led
to countless deaths over the years. Worldwide, per the Global
Road Crash Data [1], traffic crashes are the major cause of
death and injuries, specifically estimated at 1.3 million
fatalities each year, on average 3,287 deaths per day.
❑ In the United States, there are over 37,000 deaths and an
additional 2.35 million injuries in road crashes each year. Of
these, 94% are caused by human error [4], reported by USA’s
National Highway Traffic Safety Administration (NHTSA)
research.
❑ The cost of traffic crashes is incredibly high, reaching USD $518
billion globally and $230.6 billion in United States. Unless
action is taken, traffic crashes are predicted to be the fifth
leading cause of death by 2030.
Motivation for Autonomous Driving (Self-Driving Cars)](https://image.slidesharecdn.com/2-241120164641-cc248094/85/2-3-Toth-pdfsdfsdfsdfsdfsdfsdfsdfsdfsdfsdfsdfs-17-320.jpg)
![23
10−3
10−2
10−1
100
101
102
103
100
101
102
103 104
Inter-
ferometry
Inter-
ferometry
Metrology
Metrology
Industrial
Photogrammetry
Industrial
Photogrammetry
Total Station
Total Station
UAS
UAS
GPS
GPS
Aerial
Photogrammetry
Aerial
Photogrammetry
Space-based
Remote Sensing
Space-based
Remote Sensing
Object / Area size [m]
Accuracy [mm]
Reproduction from http://www.igp-data.ethz.ch/berichte/blaue_Berichte_PDF/105.pdf
Terrestrial
LiDAR
Terrestrial
LiDAR
DGPS
DGPS
Geomatics Technologies](https://image.slidesharecdn.com/2-241120164641-cc248094/85/2-3-Toth-pdfsdfsdfsdfsdfsdfsdfsdfsdfsdfsdfsdfs-23-320.jpg)
2.3-Toth.pdfsdfsdfsdfsdfsdfsdfsdfsdfsdfsdfsdfs
- 1. SMART CITIES: THE MOBILITY COMPONENT Charles Toth Satellite Positioning and Inertial Navigation (SPIN) Laboratory Department of Civil, Environmental and Geodetic Engineering The Ohio State University Email: toth.2@osu.edu XXVI FIG Congress 2018, Istanbul, Turkey May 6-11, 2018
- 2. 2 ❑ Smart City ▪ Mobility → Smart Mobility ▪ Positioning and Navigation (PNT), GEOINT ▪ Autonomous Vehicle (AV) technologies ❑ Technology Trends ▪ Geomatics field ▪ Sensor technologies ❑ Crowdsourcing/Crowdsensing ▪ Smart devices ▪ Autonomous vehicles ❑ Conclusion
- 3. 3 The deployment of technology and data… …to improve people’s lives. Smart Cities: Connectivity Spatial/temporal context Big Data
- 4. 4 ❑ 26 smart cities are expected by 2025, 50% of which will be in Europe and North America ❑ At present: smart communities projects in many cities worldwide (global spending of $1.5 trillion by 2020) ❑ No smart city yet…Amsterdam, Barcelona, Dubai , NYC, London, Nice, Singapore Smart City
- 5. 5 Smart Columbus Overview Smart Columbus Overview
- 7. 7 ❑ Smart City ▪ Mobility → Smart Mobility ▪ Positioning and Navigation (PNT), GEOINT ▪ Autonomous Vehicle (AV) technologies ❑ Technology Trends ▪ Geomatics field ▪ Sensor technologies ❑ Crowdsourcing/Crowdsensing ▪ Smart devices ▪ Autonomous vehicles ❑ Conclusion
- 8. 8 Mobility: The movement of people and goods from place to place, job to job, or one social level to another (across bridges – physical or assumed).
- 9. 9 Smart Mobility: The movement of people and goods with… Smart Mobility: The movement of people and goods with… 0 Accidents and Fatalities 0 Carbon Footprint 0 Stress TRIPLE ZERO
- 10. 10 Smart Mobility: Smart Mobility: • Saves Lives • Improve Lives of Older Adults and People with Disabilities • Transportation in an Era of Urbanization • Environmental Sustainability • Economic Sustainability • Saves Lives • Improve Lives of Older Adults and People with Disabilities • Transportation in an Era of Urbanization • Environmental Sustainability • Economic Sustainability
- 11. 11 The Commute of the Future The Commute of the Future Challenges • Increasing VMT/VKT • Pollution • Urban Sprawl • Inequity • Segregated Roadways Challenges • Increasing VMT/VKT • Pollution • Urban Sprawl • Inequity • Segregated Roadways
- 12. 12 ❑ Smart City ▪ Mobility → Smart Mobility ▪ Positioning and Navigation (PNT), GEOINT ▪ Autonomous Vehicle (AV) technologies ❑ Technology Trends ▪ Geomatics field ▪ Sensor technologies ❑ Crowdsourcing/Crowdsensing ▪ Smart devices ▪ Autonomous vehicles ❑ Conclusion
- 13. 13 Position, Navigation, and Timing in an Autonomous Future Position, Navigation, and Timing in an Autonomous Future Courtesy of USDOT
- 14. 14 PNT Applications in Smart Cities PNT Applications in Smart Cities • V2V & V2I (V2X) Communication • Autonomous Navigation & Collision Avoidance • Location Based Services • Smart & Resilient Infrastructure • V2V & V2I (V2X) Communication • Autonomous Navigation & Collision Avoidance • Location Based Services • Smart & Resilient Infrastructure Courtesy of Prof. Dorota Brzezinska, OSU
- 15. 15 ❑ Location-based services ❑ Autonomous navigation and collision avoidance ▪ Connected vehicles – cooperative mobility; vehicle-2-vehicle (V2V) and vehicle-2- infrastructure (V2I) cooperation, and V2X ▪ Geodetic infrastructure needed (e.g., CORS) Multi-Sensor Positioning and Multi-Infrastructure Communication https://www.google.pl/#q=connected+vehicles Positioning and Communication
- 16. 16 ❑ Smart City ▪ Mobility → Smart Mobility ▪ Positioning and Navigation (PNT), GEOINT ▪ Autonomous Vehicle (AV) technologies ❑ Technology Trends ▪ Geomatics field ▪ Sensor technologies ❑ Crowdsourcing/Crowdsensing ▪ Smart devices ▪ Autonomous vehicles ❑ Conclusion
- 17. 17 ❑ Driving by human beings is found to be dangerous and has led to countless deaths over the years. Worldwide, per the Global Road Crash Data [1], traffic crashes are the major cause of death and injuries, specifically estimated at 1.3 million fatalities each year, on average 3,287 deaths per day. ❑ In the United States, there are over 37,000 deaths and an additional 2.35 million injuries in road crashes each year. Of these, 94% are caused by human error [4], reported by USA’s National Highway Traffic Safety Administration (NHTSA) research. ❑ The cost of traffic crashes is incredibly high, reaching USD $518 billion globally and $230.6 billion in United States. Unless action is taken, traffic crashes are predicted to be the fifth leading cause of death by 2030. Motivation for Autonomous Driving (Self-Driving Cars)
- 18. 18 ❑ Most of the accident happen close to our homes (urban areas) ❑ An average American driver spends nearly 300 hours on road each year ❑ Traffic congestion and parking are painful Picture credit: pixabay Traffic in Cities
- 19. 19 ❑ Driverless technology is rapidly evolving ❑ High-definition geospatial/GIS data is an enabling component to improve localization and, subsequently, safety ❑ Huge amount of GIS data is already available, the question is how to access it, and then the communication (organizing data, and V2X) ❑ Crowdsourcing will be the dominant data acquisition technology (Big Data, Big Geo Data) Autonomous Vehicles (AV)
- 20. 20 Google Driverless Car 2017 DARPA Urban Challenge 2007 Rapid AV Developments
- 21. 21 ❑ Smart City ▪ Mobility → Smart Mobility ▪ Positioning and Navigation (PNT), GEOINT ▪ Autonomous Vehicle (AV) technologies ❑ Technology Trends ▪ Geomatics field ▪ Sensor technologies ❑ Crowdsourcing/Crowdsensing ▪ Smart devices ▪ Autonomous vehicles ❑ Conclusion
- 22. 22 Surveying Airborne Surveying Mobile Mapping Autonomous Driving Smart Mobility → Autonomous Driving → Geomatics
- 23. 23 10−3 10−2 10−1 100 101 102 103 100 101 102 103 104 Inter- ferometry Inter- ferometry Metrology Metrology Industrial Photogrammetry Industrial Photogrammetry Total Station Total Station UAS UAS GPS GPS Aerial Photogrammetry Aerial Photogrammetry Space-based Remote Sensing Space-based Remote Sensing Object / Area size [m] Accuracy [mm] Reproduction from http://www.igp-data.ethz.ch/berichte/blaue_Berichte_PDF/105.pdf Terrestrial LiDAR Terrestrial LiDAR DGPS DGPS Geomatics Technologies
- 24. 24 Bay Bridge, San Francisco How Was the Bridge Surveyed?
- 25. 25 Platforms and Sensors GPS IMU Navigation sensors Passive imaging sensors Active imaging sensors LiDAR, SAR, SONAR Spaceborne Airborne • Fixed wing • Helicopter • UAV/UAS Land-based (indoor/outdoor) • Vehicle • Autonomous • Pushcart • Man-portable Sea-, under-water based • Ship • Autonomous, man-portable Receiving Array Sonar Transducer (Emitter) Tilt Sensor Power & Communication Barometer/pressure sensors Magnetometer/compass/inclinometer Odometer/step RF-based sensors UWB/PL/WiFi/RFID/etc.
- 26. 26 GPS Wi-Fi 4G/GPRS 3-axis accelerometer 3-axis gyro Microphone Ambient light sensor Bluetooth Proximity sensor FM radio Cameras 3-axis magnetometer Smart Devices
- 27. 27 ❑ Smart City ▪ Mobility → Smart Mobility ▪ Positioning and Navigation, GEOINT ▪ Autonomous Vehicle (AV) technologies ❑ Technology Trends ▪ Geomatics field ▪ Sensor technologies ❑ Crowdsourcing/Crowdsensing ▪ Smart devices ▪ Autonomous vehicles ❑ Conclusion
- 28. 28 Check what’s in your pocket…a powerful geospatial technology! ❑ People leave digital footprints wherever we go ❑ We’re continuously georeferenced, and provide other information too, including increasing volumes of imagery (voluntarily or involuntarily) Device Navigation Sensors Imaging Sensors Communication Capability Smartphone Smartwatch GPS/IMU/ Compass CMOS (2) (still and video) 3G/4G/WiFi/BT/etc. Digital camera GPS CMOS (still and video) WiFi Recreational GPS Wearable technology GPS/Compass/ IMU/HRM/etc. No WiFi, BT Car navigation GPS CMOS (rear, etc.) 3G/4G/BT/etc. Social networks (virtual) Access point location Webcam, etc. Internet Crowdsourcing and Crowdsensing
- 30. 30 30 Running Bicycling What is Mapped?
- 31. 31 UAS is a Flying Smart Sensor Power of crowdsourcing: Building Rome in a day (2009) ❖ Website: https://grail.cs.washington.edu/rome ❖ Videos: https://www.youtube.com/watch?v=qYaU1GeEiR8&list=PLDFDB5B8C80DB3AD6
- 32. 32 http://www.navipedia.net/index.php/GNSS_Market_Report ❑ By the end of 2017 there were 2.4 billion smartphones in use, 7+ billion by 2030; LBS – the primary driver! ❑ Fastest growing GNSS+ market with revenues expected to reach over $88 billion USD by 2020; market growth at a CAGR of ~21% over the period 2012-2017 ❑ IoT, Big Data, augmented reality, smart city, autonomous driving, multimodal logistics, mBanking, mHealth, asset mng’t, etc. Smartphone and GNSS Growth
- 33. 33 ❑ Smart City ▪ Mobility → Smart Mobility ▪ Positioning and Navigation (PNT), GEOINT ▪ Autonomous Vehicle (AV) technologies ❑ Technology Trends ▪ Geomatics field ▪ Sensor technologies ❑ Crowdsourcing/Crowdsensing ▪ Smart devices ▪ Autonomous vehicles ❑ Conclusion
- 34. 34 Model Cameras LiDAR Other Tesla M3 7 0 Cadillac ST6 8 0 HD map, RTK Waymo 1 1 Route info What Does AV See?
- 35. 35 35 Sensors: various data streams Image Streams
- 36. 36 Image-based Collaborative Navigation and Mapping OSU Campus, SPIN Lab CDD/SLAM solution (smartphone imagery)
- 37. 37 37 Central front LiDAR sensor, Velodyne HDL-32 All LiDAR sensor data combined Lidar Point Clouds
- 41. 41 KITTI data, widely used benchmark, SPIN Lab CDD/IMU/SLAM solution Creating Maps
- 42. 42 ❑ Smart Cities are relying on connectivity and sharing data/information with spatial/temporal context (every piece of information is geotagged) ❑ Handling the huge amount of sensor data requires new methods, Data Science, and within that discipline Data Analytics and Deep Learning (AI) ❑ Smart mobility is an essential part of Smart Cities, and driverless vehicles will play a growing role in the future ❑ Sensor proliferation will continue, seriously affecting both professional and crowdsourcing/crowdsensing based geospatial data acquisition and processing (accuracy and privacy are important questions) ❑ Autonomous vehicle technologies need high-definition and accurate 3D geospatial data to improve robustness and safety ❑ Autonomous vehicle technologies will likely be the prime provider of geospatial data along transpiration network in the future (mobile mapping platforms), and create a live transportation system (smart CAD/GIS) Conclusion