M2SMART PROJECT

1M2SmartNewsLetter

Vol.3May 2019

REASEARCH MEETING AT NIHON UNIVERSITY (APRIL 2019)

JCC IN TOKYO

3rd Joint Coordination Committee (JCC)by Haruka Katarao, M2Smart Resident Coordinator

Fruitful discussion in the fully
bloomed cherry blossoms in Tokyo!
The 3rd Joint Coordination Committee(JCC) was conducted at Nihon
University on 5 April 2019. Led by Prof.
U.B. Desai, Director of Indian Instituteof Technology Hyderabad (IITH), 5IITH researchers visited Tokyo andexchanged opinions with theresearchers from Nihon University,Tokyo Institute of Technology andNagoya Electric Works. We also had aparticipation from Japan Science andTechnology Agency (JST).

Prior to the JCC meeting, theresearchers gathered at Nihon
University Funabashi Campus forgroup meetings.
Welcomed by the fully bloomed cherry
blossoms in the campus, it was a good
opportunity for each group’s
researchers to discuss directly theirresearch progress.

JCC meeting in Nihon University

M2SMART NEWSLETTER | VOL.3

On Estimation and Reduction of CO2 Emissions from Vehicles
By Bheemarjuna Reddy Tamma, M2Smart Co-Project Manager
The 3rd Joint Coordination Committee (JCC) meeting took place at Nihon University campus on 5th April 2019.
Researchers from IIT Hyderabad, Nihon University, Tokyo Institute of Technology, and Nagoya Electric Works had
presented progress made since the last JCC meeting in terms of testbed setups at IITH campus and Ahmedabad city,
joint research works carried out, and dissemination of the results as conference/workshop articles and demos. For
efficient detection/classification/tracking of heterogeneous vehicles present on Indian roads using CCTV videos, JCC
recommended Group-1 to develop an open dataset for vehicles plying on Indian roads. Since CO2 emission reduction isthe primary goal of the project, JCC brainstormed on various methodologies to be adopted for CO2 emission estimation
on Indian roads. Subsequently, the researchers would employ the developed methodologies for estimation of energy
and CO2 emission reduction by route choice and mode choice provided using IoT based traffic management system i.e.,
VMS displays and Multimodal App (MMA) for Smartphones. Since testbeds are ready, the focus now is on carrying out
R&D activities in order to meet the project goals.

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3

Group meeting at Nihon Universityby Satoshi Takahashi, Group 1 Co-Leader

On April 4, the day before the JCC meeting, research members from Japan and India
had a meeting at the Nihon University Funabashi Campus. At first, Dr. Tsuboi, theproject leader, confirmed the research theme, research plan, and the future schedule.
Then, the leaders of each research group introduced about the progress of researchand exchanged opinions among all members.
•Group 1 [Sensing]: Vehicle recognition by AI image processing, motorcycle
recognition by 3D sensor and LIDAR.
•Group 2 [Big Data Analysis]: Examination of PM2.5 generation measurement
method, analysis of the operating trajectory data of BRT in Ahmedabad City, andcollecting traffic information from news and social media.
•Group 3 [Multimodal Application]: Construction of traffic micro simulator, analysis ofquestionnaire results on public transportation usage in Ahmedabad.
•Group 4 [CO2 Reduction]: Research on approach of CO2 reduction evaluation bymodal shift.
Furthermore, Dr. Digvijay Pawar reported the status of upcoming IITH testbed.
Lastly, the researchers were divided into the 4 groups to have focused discussions andchalk out next steps. Through the face to face discussions, the relationship of trust
between researchers became strong, and it became a very meaningful meeting.

Group 1

Group 2

Group 3

Group 4

Meeting in Nihon University

M2SMART NEWSLETTER | VOL.34Vehicle Detector using Laser Ranging Image Sensor
by Lin Teng, Group 1

3D
TOF(Time-of-flight)
Laser
Ranging
Image Sensor
Sens
ProblemorObjectExtract
objectBackground subtraction

TargetData lossof subtraction
Low Reflectivity ofblack car• By Laser Ranging
Solutionof Image Sensor, the distancevalue of black is detected as infinity(4095).Black
cause much data loss of subtraction.
blackcarcar•We use the absolute value of the difference fromthe background to extract black car.

Background

Vehicle Detection and Tracking using Deep Learning
by Debaditya Roy, Group 2Intersections are a common source of idling vehicles that produce
greenhouse gases. Hence, estimating the density of traffic atintersections in crucial in understanding which intersections contributemost in terms of pollution. Further, by analyzing the capacity of traffic atintersections we can determine the composition of traffic (number andtype of different vehicles) and the average waiting time at an intersection.
Our preliminary analysis is based on the detection of the various vehicle
types at five intersections at Ahmedabad, namely, Paldi, Shivranjini,Nehru Flyover, Swami Vivekananda Bridge, and APMC market. We
employed a convolutional neural network known as Single-Shot Detector(SSD) to achieve 75% accuracy in detection of motorbikes, autorickshaws,cars, buses, and trucks from aerial videos of the intersections. Then aMarkov Decision Process (MDP) based tracking system was utilized totrack the various detected vehicles across the intersection. Our methodcan accurately track and count the vehicles at all these intersections thatcan be utilized for capacity analysis and greenhouse emission studies.
JCC Meeting

M2SmartNewsLetterVol.3
May 2019

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