A Whole New World of Mapping and Sensing: Uses from Asset to Management to In Vehicle Sensing for Collision Avoidance

Transcription

1 A Whole New World of Mapping and Sensing: Uses from Asset to Management to In Vehicle Sensing for Collision Avoidance Charles Toth, Dorota A Grejner-Brzezinska, Carla Bailo and Joanna Pinkerton Satellite Positioning and Inertial Navigation (SPIN) Laboratory Department of Civil, Environmental and Geodetic Engineering The Ohio State University toth.2@osu.edu 2017 OTEC Symposium Columbus October 10-11,

2 Driverless Vehicle Technology As vehicle technology is moving towards higher autonomy, the demand for highly accurate map data is rapidly increasing, as accurate maps have a huge potential to increasing safety. In particular, engineering scale 3D maps, including road topography and infrastructure as well as city models along the transportation corridor represent tremendous support for driverless vehicles. The quality of the 3D data, measured in accuracy and currency must be clearly far superior to any conventional map data, traditionally provided by federal and local governments, and also significantly richer in information than the typical 2D map data used in car navigation systems. In a Smart City environment, integrated data exchange can provide the link between the geospatial/gis database and the vehicles (V2I/V2X). Access to this database, which includes high definition 3D maps and the corresponding metadata, is essential for autonomous vehicles as it facilitates the sensor systems to accurately relate the vehicle s location/trajectory to the surrounding environment in any situation. 2

3 Driverless Vehicle Technology 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 Crowdsourcing will be the dominant data acquisition technology (Big Data, Big Geo Data) Full autonomy is still a long way 3

4 Motivation 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

5 Traffic in Cities 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 5

6 Smart Columbus 6

7 Olli at CAR 7

8 Collision Avoidance Dynamic object detection on road Object detection is the process of finding instances of real-world objects such as vehicles, bicycles in images or videos. Traditionally, Support Vector Machine (SVM) combined with the Histogram of Orientation (HOG) features have become the most efficient algorithm 8

9 Tracking 9

10 Tracking 10

11 OSU GPSVan General sensor platform with highly accurate georeferencing system Data acquisition capabilities to support Creating accurate high-definition maps Provide sensor data stream to support driverless vehicle technology research 11

12 Sensors and FOV 12

13 Navigating without Maps OSU data, SPIN Lab CDD/IMU/SLAM solution 13

14 Various Viewpoints Many people involved in driverless technologies approach the problem from different angles Navigation/positioning people focus on GPS/GNSS trying to push the envelop; accuracy matters but confidence in the data is more important Sensor manufacturers are divided by technologies Optical sensing Laserscanning Radar Navigation and mapping fields are converging Car manufacturers are learning, just recognizing the importance of geospatial/gis data Many companies are already acquiring high-definition maps, hoping to sell it manufacturers 14

15 Vehicle Localization Localization accuracy required by autonomous driving High accuracy: 3-10 cm Single frequency GPS is not enough accurate (2-5 m, no GPS) More complex GPS processing requires communication and special infrastructure (RTK, GNSS) Urban canyon effect is still a problem IMU has large drift error and navigation-grade IMUs are expensive Solution: Map matching algorithms provide reliable and accurate localization solution Map matching requires precise map a prior the drive 15

16 Navigating with Maps KITTI data, widely used benchmark, SPIN Lab CDD/IMU/SLAM solution 16

17 High Definition Maps How to obtain precise map? Mobile mapping (e.g. Google Street View; not accurate) First, the autonomous vehicle has to be driven manually along the route to create a map (SLAM); e.g., shuttles Crowdsourcing: Vehicles/platforms share information (vehicle to vehicle, V2V distributed, or vehicle to infrastructure, V2I, centralized), including Precise location information Redundant video and other sensor streams (bandwidth is an issue) Detailed representation of the scene (requires the processing of the sensor streams) 17

18 What is Mapped? iphone Android 18

19 MobileEye Implementation MobileEye s Road Experience Management (REM) The solution is comprised of three layers: harvesting agents (any camera-equipped vehicle), map aggregating server (cloud), and map-consuming agents (autonomous vehicle): The harvesting agents collect and transmit data about the driving path s geometry and stationary landmarks around it The cloud server aggregates and reconciles the continuous stream of Road Segment Data a process resulting in a highly accurate and low level Time to Reflect Reality map, called Roadbook (MobilEye GIS) The last link in the mapping chain is localization, in order for any map to be used by an autonomous vehicle, the vehicle must be able to localize itself within it, the Mobileye software running within the map-consuming agent (the autonomous vehicle) automatically localizes the vehicle within the Roadbook by realtime detection of all landmarks stored in it 19

20 City Model Demonstration Power of crowdsourcing: Building Rome in a day Researchers create 3D model of cities using images shared on Flickr (2009) Website: Videos: 80DB3AD6 20

21 Pilot Project Objectives Sensing Data for Autonomous Mobility: OSU Campus Benchmark Dataset Collection to Aid Autonomous Vehicle Testing in Smart Columbus The main objective is to acquire data streams from mobile platforms, including vehicles, bicycles, pedestrians, etc. These data are essential to testing vehicle sensing and maneuvering capabilities, and directly support research and development of autonomous vehicle technologies. A second objective is to create a high definition map of the test area that includes the mobile data collection and additional surveying of the area. The availability of such maps has a significant effect on autonomous driving by providing a detailed description of the object space of and around the transportation corridor, greatly improving the reliability of the vehicle s self localization and path planning capabilities. 21

22 High-precision Mapping OSU Sensors, Data 22

23 High-precision Mapping Capabilities Data acquisition components: Local reference GPS network (densified, if needed, by the OSU team) GPSVan, mobile mapping platform to collect data High-accuracy georeferencing system (permanently installed) GPS/GNSS data for platform positioning IMU data for platform positioning and orientation data Imaging sensors (configuration depends on project objectives) Various digital cameras; video and still imagery from various perspectives Mobile laserscanner data to generate dense point clouds Phase 1 data processing steps: Static georeferencing data processing Vehicle trajectory computation LiDAR and image point cloud processing 23

24 Sensor Configuration Sensor # of Storage FOV [ ] Sampling rate Data rate sensors Gbyte/hour Velodyne VLP x Hz 2 MB/s 7.2 Velodyne HDL x Hz 4 MB/s 14.4 GoPro Hz 60 MB/s 216 Nikon 1 45/65 1 Hz 15 MB/s 65 Point Grey Hz 120 MB/s 400 Sony Nex Hz 48 MB/s 172 Canon 2 45 Video mode 15 MB/s 54 Casio 2 45 Video mode 15 MB/s 54 MobileEye 1 MicroStrain IMU 1 N/A 200 Hz 100 kb/s 0.36 EPSON IMU 1 N/A 300 Hz 150 kb/s 0.54 H764G 2 N/A 256 Hz 200 MB/h 0.20 GPS 2 N/A 5Hz 0.02 Total: ~ 1 TB 24

25 Observation Space, Sensor FOV 25

26 Schedule 1. System configuration Sensor selection FOV optimization Sampling rate 2. System shake-up Laboratory testing CAR/road testing 3. System calibration, sensor orientation with respect to vehicle reference frame Target site construction at CAR (point and planar controls) Calibration measurements Processing, estimating calibration 4. Main data collections 1. Set 1, data collection around CAR 2. Set 2, data collection on Campus 3. Set 3, data collection including reference moving objects, including bicycles and pedestrians, equipped with GPS/IMU/UWB positioning and imaging sensors 26

27 SUMMER DATA ACQUISITION 27

28 Laser Data 28

29 Multiple Vehicle Configuration 29

30 Summary Driverless vehicle technologies greatly benefit from high resolution city model level geospatial data Mapping, data acquisition methods for transportation GIS systems are rapidly changing Need for high-definition, and highly accurate geospatial data is quickly growing Crowdsourcing is becoming mainstream; vehicles are becoming the prime source of geospatial data Communication is essential; Smart City environment provides the larger framework, integrated data exchange can provide the link between the geospatial/gis database and the vehicles (V2I/V2X) 30