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CASPer 2015
A Sensing Coverage Analysis of a Route
Control Method for Vehicular Crowd
Sensing
Mar 27,2015
Osamu Masutani
Chief Engineer, Denso IT Laboratory, Inc.
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 1
Summary
Concept
Vehicular crowd sensing for city monitoring
Methodology
Sensing coverage of city monitoring
Route finding methods for crowd sensing
Evaluation
Conclusion & future work
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 2
Concept : Vehicular Crowd Sensing for a smart city
Major topics of smart city
Energy efficiency for sustainable economy
Cost effective and resilient infrastructure
Contribution of vehicles
Efficient traffic control
Crowd sensing by vehicles
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 4
Efficient traffic City monitoring
smart city
Transportation sector
Vehicle as a powerful sensor
A vehicle has huge potential for crowd sensing
Many kinds of in-vehicle sensors
Advanced environmental sensors
Stereo camera, laser rader, milliwave rader
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 5
Denso Technical Review
https://www.denso.co.jp/ja/aboutdenso/technology/dtr/v17/files/10.pdf
http://www.embedded.com/print/4011081Smart phones Vehicle
6th Gen iPhone 3rd Gen Prius
Sensors <10
Cameras, Accelerometer, Mic,
Proximity …
100
Physical, thermal, electric …
Processors 1 CPU(2 cores), 1 GPU(4 cores) 70 ECUs
Battery 6.7 Wh (1810 [email protected]) 1.3 kWh
http://www.car-electronics.jp/files/2012/10/CurrentStateOfIn-
vehicleMicrocomputer.pdf
Floating car to Vehicular crowd sensing
Floating car systems monitor these phenomena in a city :
Traffic monitoring (congestion, incident) : GPS tracking data
Road condition monitoring (ice) : ABS, road monitoring sensor
Weather monitoring (precipitation) : wiper
Vehicular crowd sensing (VCS)
Try to contribute “for a city” rather than “for a drivers“
Wider range of usage
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 6
Environment
(pollution, noise)
Facility
Maintenance
(bridge, tunnel)
City Mapping
(road, building)
Public Security
(crime, disaster)
City monitoring with VCS
Key performance indices for vehicular crowd sensing
Quality of data (Accuracy)
Quality of sensors
Quantity of data (Coverage)
Number of sensors
Boost the area simultaneously observed
Route of sensors
Track efficient route to visit sensing target
The routes should not be redundant among multiple sensors
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 7
Number of sensors
Route (orbit) of sensors
Coverage enhancement of vehicular crowd sensing
Number of sensors
Base traffic amount * Participating rate
Enhanced by penetration strategy (enforcement, incentive)
Route of sensors
Efficiently track sensing demand in a city
Enhanced via traffic control
Center based navigation
Fleet management
Managed self driving car
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 8
Number of sensors
Route of sensors
Definition of sensing demand in a city
Sensing demand in a city varies :
In space
In time
Three categories of demand :
Uniform : weather, road condition
Static : facility (bridges, tunnels)
Dynamic : crime, traffic
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 10
UNIFORM STATIC DYNAMIC
Evaluation index of sensing coverage
Sensing Demand
Defined on each road link
Binary demand (exist or not)
Fully satisfied when the sensing vehicle pass the link
Coverage : Demand Satisfaction
How much percentage the demand satisfied in space and time
Varies from 0 (fully satisfied) to 1 (not satisfied)
Travel Time
The time taken to destination
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 11
Link
Sensing Demand
dem
an
d level
0
1
Not satisfied
Satisfied
Travel time
Traffic control aware of sensing demand
Modification of shortest route in order to pass sensing demand
Make detour to satisfy sensing demand
Default route finding
Distance link cost or time cost
The cost aware of sensing demand
The link cost is decreased as much as sensing demand
The route is attracted to the sensing demand.
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 12
Sensing demand
Default route
New route
link cost
demand
Route reservation to avoid concentration of traffic
Traffic concentration to sensing demand
Redundant sensing when multiple vehicle visit at once
Solution : route reservation
Each vehicle reserves route before it arrives
Find optimal route according to number of reservations for each links.
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 13
RESERVED
RESERVED
Route Reservation
Reservation is managed in traffic management center
Each link has reservation slot
Reservation aware route finding is performed in traffic center
All of sensing vehicle follow the route
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 14
link cost
demand
Available on :
Evaluation environment
“Metro traffic simulator” – simple micro simulation workbench
Car following model
Shortest route search
Grid and OSM based maps
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 16
Result summary
Uniform sensing demand : previous work
Static sensing demand
For coarse sensing demand, simple sensing demand cost would work.
For higher traffic density, combination with route reservation would work
For longer route, reservation should be considered time slot
Dynamic sensing demand
Route reservation with time slot would work
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 17
0 Uniform sensing demand
Reservation has two appropriate effects
Coverage extension
Use alternative routes effectively
Reduction of traffic congestion
Avoid traffic concentration before jam occurs
These effects realize higher coverage without travel time extension
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 18
Distance cost
Travel time cost
Reservation cost
Link ID
tim
e
Previous work : Masutani, O. A proactive route search method for an efficient city surveillance. 21th World Congress on ITS, (2014).
Common setting
Map
10 * 10 grid (50m pitch)
10 origin to 10 destination (100 combination)
Updated once in 30 second
Sensing demand
Binary sensing demand
Random distribution
Simulation duration
20,000 sec
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 19
1-1 Static Sensing Demand
Sensitivity analysis on demand density
Three route finding methods
Distance
Travel time
Sensing demand aware
Result
For coarse demand, simple sensing demand cost would gain extra coverage.
For dense demand, distribution is similar to uniform case -> previous work
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 20
Advantage in coarse demand
Co
vera
ge
Density
Distance
Travel time
Demand aware
1-1 Analysis
De-tour occurred ?
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 21
coarse moderate dense
sensing HIGH
selective
LOW
bound by # of vehicles
LOW
bound by # of vehicles
travel
time
LOW
small detour occurred
HIGH
much detour occurred
LOW
don’t need to detour
Travel ti
me
Co
vera
ge
Density
Density
Demand aware
Demand aware
1-2 High traffic volume case - reservation
Reservation avoid concentration
Reservation technique can extend coverage even in higher traffic
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 22
Demand aware Demand aware
+ reservation
Illustrati
on
Demand aware
Excess demand aware
Reservation
Co
vera
ge
Traffic volume
1-2 Effect of reservation cost
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 23
Sensing Demand only Sensing Demand + Reservation
1
2
3
1
2
3
1
2
3
1
2
3
never visited visited
1-3 Longer route case – predictive reservation
Reservation deteriorate when map size is increased
Caused by excess reservations which is not actually necessary
“time slot” of reservation to avoid excess reservation
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 24
current reservation predictive reservation
Demand aware
Reservation
Reservation w/ time slot
Map size
Co
vera
ge
: sensing demand
1-3 Effect of predictive reservation
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 25
1
2
3
1
2
3
1
2
3
1
2
3
Sensing Demand +
Reservation
Sensing Demand +
Reservation
w/time slot
2 Dynamic demand
Predictive demand
Known demands on future
Time slot work
Only confirmed in reciprocal dynamic demand
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 26
PD PD with current reservation PD with predictive reservation
Predictive Demand aware
Reservation
Reservation w/ time slot
: sensing demand
Conclusion and Future work
Sensing demand and reservation aware route finding
Enhance coverage without extending much travel time
Detour is not zero : need some kind of incentive is needed.
Easily integrated to current center-based navigation
Future work
More realistic evaluation : real traffic, participation rate
Optimization technique to maximizing coverage
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 27
Optimization approaches
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 28
Navigation
System
Vehicular
crowd
sensing
Collaborative
routing
Fleet
Management
Traffic
Management
Small traffic / microscopic
Low penetration rate
Dedicated vehicles
Maintain quality of service
Large traffic / macroscopic
High penetration rate
General vehicles
Maintain user equilibrium
Thank you for your attention !
Any questions ?
Copyright (C) 2015 DENSO IT
LABORATORY,INC. All Rights Reserved. 29