Cooperative ITS 2020 andthe Experimental Evaluation
Manabu Tsukada
The University of Tokyo
2015-02-24

at I-REF building, UTokyo

Cooperative ITS
Cooperative Systems
Systems where multiple entities
share information and tasks toachieve common objectives.

Intelligent Transportation Systems
Systems that realize safe, efficientand comfortable human mobility.

• Cooperative Intelligent Transportation Systems– Systems where multiple ITS Stations share information toachieve better road safety, traffic efficiency and comfort

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Cooperative ITS 2020 and the Experimental Evaluation

2Issues on Road Traffic• Accident※Road Traffic Death by WHO (2010)– 1.2 million dead, 50 million injury, every year
– 95% are human error• Energy※Energy White paper 2013

– 95 % of the consumed energy in the road is from oil
product in Japan• Global warming※ Greenhouse effect gas emission 2012
（Ministry of the Environment）– 95% of CO2 emission is from the vehicle in Transport
sector.

• Traffic jam※ Council for Social capital improvement 2012

– Japan loses 2.8 million man power (=5 billion hour)
every year
– Corresponds 4.3% of Japanese working population(64 million)
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Cooperative ITS 2020
Before 2020

Probe Information
systems• Center generates valuable transport
information– Accident, traffic jam, road information,
weather information– However, it does not reduce human error• Computer can replace driver’s task
After 2020

Automated driving？？？
systems– Computer have quick response– Computer is tireless– Computer is careful– Computer can process in parallel– Progress of Artificial Intelligent (AI)• Chess world champion• Shogi professional group has lost 2013

– Progress of Censor technology– Progress of Computer vision
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Prospects of Automated Driving• Government ITS Roadmap（2014.3）– World most advanced ITS in 2020
– Showcase of Automated Driving in Tokyo Olympics games
2020– After 2020, Japan becomes a center of innovation abouttransportation systems– Autonomous type and Cooperative type are assumed– Cooperative modules are integrated (added) into
Autonomous type

Level

Items on driving

Time

Level 1

Any of acceleration, steering, brake

In market

Level２

Some of acceleration, steering, brake2017

Level３All of acceleration, steering, brake(Manual driving is emergency only)

2020s

Level４

All of acceleration, steering, brake(No manual driving)2030

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Cooperative ITS 2020 and the Experimental Evaluation

SystemSafety support
Partially
Automated
Driving
Automated
Driving5Classification• 3 Type classified by information area
• Cooperative ITS can get wide area information inreal-time
プローブ型（広域情報）
Probe (WIDE area)
Cooperative (Neighbor)
Autonomous (vision)※Government ITS Roadmap

Probe (WIDE area)
Point

Wide area but not
realtimeIssue••••CloudBig data
Open Data
Data Anonymity

WIDE is working
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Cooperative (Neighbor)

Autonomous (vision)

Wide area and real time

Recognition by computer
vision and Real time

Real-time communication inhighly dynamic network
topology with relative speedof 200km/h.

••••

WIDE should work

(WIDE should collaborate)

Computer vision
AISensor
Parallel computing

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Issues in Cooperative ITS
Probe Information Systems•
•
•
•

Cloud

CloudBig data
Open Data
Data Anonymity

Intelligence

ProbeSending local info
Receiving global info

Automated Driving SystemsPlus• Distributed Intelligence networkarchitecture for combination
with Autonomous nodeIntelligence

Real-time Communications• In Highly dynamic network withrelative speed of 200km/h• Sub millisecond
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Cooperative ITS in Japan• Difference in the architecture between Japan and ISOJapan

Application

Safety（Smartway, DSSS, ASV）

Transport& Network
Access（plan）
5.9GHz

ISO／ETSI（ITS Station architecture）
SafetyNon safety

ITS Transport

TCP/UDPITS Network

IPv6700MHz

5.9GHzLTE, 3G, 2.4GHz, ...

• GoalApplication

Smartway, DSSS,ASVTransport& Network

Transport& NetworkAccess

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Non safety

5.9GHz700MHzLTE, 3G,

2.4GHz,...

Network Management
for cooperative ITSin JapanSensor
MapGPSStatistics
otherFlexible database for future use

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Methodology for experimental
evaluation of vehicular networks
1.

Manabu Tsukada, José Santa, Satoshi Matsuura, Thierry Ernst and Kazutoshi Fujikawa, "On the Experimental
Evaluation of Vehicular Networks: Issues, Requirements and Methodology Applied to a Real Use Case", EAI Endorsed
Transactions on Industrial Networks and Intelligent Systems, December 2014.

2.

Manabu Tsukada, José Santa, Satoshi Matsuura, Thierry Ernst and Kazutoshi Fujikawa, "AnaVANET: an experiment andvisualization tool for vehicular networks", 9th International Conference on Testbeds and Research Infrastructures forthe Development of Networks & Communities (TRIDENTCOM 2014), Guangzhou, China, May 20142015-02-24

Cooperative ITS 2020 and the Experimental Evaluation

9Outline
Background
Issue and requirements for VANET evaluation
Evaluation methodology
AnaVANET
Evaluation of NEMO over IPv6 GeoNetworking

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Network Layer Protocols for vehicular Networking
Vehicle to Vehicle (V2V)
wireless communications in dynamic topologies without any
infrastructure
VANET or MANET

Vehicle to Infrastructure (V2I)
global connectivity of nodes to the Internet
Internet Mobility (Mobile IPv6, NEMO)

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Issues on vehicular networks evaluation
Common end-to-end evaluation tools such as
ping6 and iperf
1. Unawareness of communication path
2. Unawareness of per-hop network
performance in the communication path
3. Unawareness of vehicles’ movement

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Requirements on vehicular network evaluation
1. Path detection
2. Communication performance in links
3. Geographical awareness4. Intuitive visualization
5. Independence from network protocols
6. Independence from devices
7. Adaptation to various scenarios
8. Easiness for data collection

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Overview of Evaluation Methodology
The evaluation goals are to analyze which
testing conditions affect which
performance indicators using
target network protocols.

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Testbed platform
V2V and V2I network
ARs and MRs

NEMO is supported
GeoNetworking is supported

OS: Linux 2.6.29.6
Wireless configuration• frequency 2.422Ghz• Data rate 6 Mbits/s

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Testing Scenarios
Location Scenarios
INRIA Paris-Rocquencourt installations

Number of vehicles
Up to four vehicles are considered in our case

Mobility Scenarios

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Data Flows and Performance Indicators
Data Flows

Geographic metric

UDPTCP
ICMPv6

Performance Indicators

Hop countSpeed
Position of cars
Distance between cars

Packet Delivery Ratio
Round Trip Time
BandwidthJitter

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Overview of the AnaVANET system
Generate packets with UDP, TCP and ICMPv6
Capture packets in all the nodes
Trace them packet by packet

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Output examples
Google maps shows theperformance indicators withMovement
Distance
Obstacles

Graphs generated by gnuplot
shows as follow
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IPv6 GeoNetworking using NEMO (ICMPv6)
64 bytes in 0.5s interval
RTT is about 5ms
Binding registrations are lostin the south road
Handoverwithout packet loss
120m from AR2, 50m from AR1

http://anavanet.net/demo-vienna/?analysis=1296754401
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IPv6 GeoNetworking using NEMO (UDP)
1250 Bytes packet with 1 Mbps
rate
UDP packets are lost during 4
seconds in handover
Disconnection time after thebinding registration failure wasseven seconds (= 1 + 2 + 4)

http://anavanet.net/demo-vienna/?analysis=1296759090
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Summary• Cooperative ITS 2020

– Automated Driving is necessary– Distributed Intelligence network architecture forcombination with Autonomous node

• AnaVANET: tools for experimental evaluation
of VANET– Analysis of evaluation of vehicular networks– Evaluation methodology– Analysis of metric impacts the network
performance– Performance indicators– Design and Implementation of AnaVANET– Evaluation of IPv6 GeoNetworking using NEMO

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Thanks
Manabu Tsukada
tsukada@hongo.wide.ad.jp
http://www.hongo.wide.ad.jp/~tsukada/