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MD9 electriCITYAutonomous City Bus

Obe

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-Burak Onur – TEMSA - Vision, Vehicle, Equipment, Use Cases-Ali Ufuk Peker - INFOTECH - Mapping-Tankut Acarman - GALATASARAY University - Algorithms

An Autonomous Full Electric Bus Development Project

Start : April 2017End: April 2020

-LOCK ON THE OBJECTObject Recognition, Detection Sampling Period <50ms

-ACT FASTCrash Avoidance and Response Time <100ms

-SAVE ENERGYElectric Consumption Average %10 less than SORT1 Driver

-DRIVE PEACEFULLY3 Axis Acceleration Detection Average %20 better than Driver

-ON DIETSensor, Computer, Steering, Cable weight not to exceed 200kg and 12kW Electric consumption

KPI’s

TEMSA |

Sabancı Holding

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TEMSA is under the umbrella of Sabancı Holding, one of the two largest conglomerates in Turkey.

13Countries

HQ: Istanbul, Turkey

Majority Shareholder: Sabancı Family (53.9%)

Listed: Istanbul StockExchange (BIST)

Free Float: 42.8%

2017 Revenues: TL 66 bn

2017 EBITDA: TL 15 bn

2017 EBITDA Margin: 22,9%

~63.000Employees

12Companies Listed in

Istanbul Stock Exchange

TEMSA |

TEMSA Adana Plant (Capacity & Location)

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2,500 Motor Coach,

2,000 Midibus,

6,000 Light Trucks

10,500 vehicles:

510 km2Total Area

115 km2Covered Area

Annual Production Capacity(in a single shift)

TEMSA |

TEMSA Product Range

5Electric Bus

Product Range

TS30TS45TS35E

HD RHDMD9 RHD

ENGLAND

USA

TURKEYMD9 electriCITY

Avenue EV

Avenue Electron MD9

electriCITYAvenue

EV

Avenue Electron

Opalin

Avenue Plus

OpalinCity

MD9 HD MaratonLD SBPlus

MD9 LE

MD7 /MD7 Plus

LD

ROEMD9

electriCITYAvenue

EV

Avenue Electron

MD9 HDLD MaratonMD9

LELF12MD7 /

MD7 Plus

LD SBPlusMaratonPrestij

SX City

Safir PlusPrestij

SX

MD9 LE

Avenue Plus CNG

FRANCE, BENELUX

TEMSA |

Product Line-up by Segment

COACH INTERCITY CITY

MARATON / VIP

LD

HD SAFIR PLUS / VIP

MD9

MD7 / OPALIN

PRESTIJ SX

TS45

TS35E

TS30

HD RHD

MD9 RHD

LD SB

MD9 LE

LF 18AVENUE PLUS

LF 12AVENUE PLUS

LF 12 CNGAVENUE PLUS

MD9 LE

MD7 CITY /OPALIN CITY

PRESTIJ SX CITY

LF 12AVENUE IBUS

LF EVAVENUE EV

MD9 electriCITY

TOURMALIN

Boasting a product line-up that addresses all segments, TEMSA has a service capacity that can be adapted to changing conditions andcustomer demands.

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TEMSA | 7

MD9 electriCITY Avenue EV Avenue Electron

Electric Vehicles

9 meters, Night Charge 12 meters, Flash Charge 12 meters, Night Charge

At TEMSA, technology is the foundation of growth. With the emphasis on R&D and innovation, TEMSA continues to manufacture bothtoday's and future's vehicles at full throttle.

TEMSA |

Autonomy

Vision

8TEMSA |

TEMSA |

Milestones

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FEET OFF

Cruise Control

*Drive by wire, software

FEET OFF

Adaptive Cruise Control

KEEP YOUR EYES ON THE ROAD

Lane Departure Warning

Frontal Crash Warning

Emergency Braking System

Fatigue Detection

*Radar & Camera

Coach & City2001

2003 2016

2020

2024

2030

EuroSafari CoachSafari HD, RD

FEET OFF

BremsoMat

Braking Downhill

*Valve Control, software

Maraton, Safir, MD

Coach & City

EYES SHUT

Steering Assist

Auto Braking

Lane Keeping

*Radar, Lidar, Camera

HANDS OFF

Auto Steer on Highway

*Radar,Lidar, Camera, Ultrsonic Sensors

Coach & City

MIND OFF

Laws

Remote Driving Assistance

Developing a broad range of products, Temsa has experienced rapid growth since its establishment.

TODAY

Throttle pedalis yours

You can have also the brake pedal

Follow the vehicle

TEMSA |

MD9 electriCITY

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Dimensions (m):

L: 9.495

W: 2.400

H: 3.132

Seating Capacity:

26 seats / 56 total

City Bus (Short Trip Distance / Class I)

For all your city needs in class I

TEMSA |

TEMSA |

Layout

11TEMSA |

Velodyne Vlp-32 Lidar

SICK LSM1111-11100 SICK LSM1111-11100

Sekonix GMSL Camera

Sekonix GMSL Camera

Sekonix GMSL Camera

Sekonix GMSL Camera

Continental ar408 radar

Ptgrey Bumblebeexb3 Stereo Camera

Xsens mit-g-710 imu NovAtel

pwrpack-7 dgps

NVIDIA DRIVE PX2

TEMSA |

Layout

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TEMSA |

TEMSA |

Use Cases

13TEMSA |

1- Cruise

BUS

STOP

BUS

STOP

BUS

STOP

2- Approach 3 - Access 4 - Depart

TEMSA |

Where to start ?

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Step 1- Protected Bus LanesStep 2- Marked Bus LanesStep 3- Campuses

TEMSA |

15TEMSA |

HD MAPS AND AI TRAINING WITH SIMULATION

FOR AUTONOMOUS DRIVE

INFOTECH

DR. ALI UFUK PEKER

INFOTECH

INFOTECH

Location Intelligence Company

• Geographic Information Systems (GIS) : 100+ Customers, Private Sector Focus

30 Finance Customers

• Mobile Location Based Services : 7 Operators, 4Countries, 60 Services

• Intelligent Vehicle Services : %80 Local Market, International Automotive

References

• Tracking & Telemetry : 1000+ Customers

• Global Partnerships :

INFOTECH

Solutions

Spatial Analytics Location Based Advertising Site SelectionAsset Tracking

Workforce Management

Location Based Services Fleet Management / Telemetry Indoor Positioning Risk Management

INFOTECH

Subjects worked on in our R&D projects

Automotive / Intelligent Vehicles /ADAS

◦ V2V Communications

◦ Enhanced Positioning

◦ HD Mapping

◦ Electronic Horizon

◦ Driver Evaluation

◦ Safe/Eco Driving

◦ Platooning

◦ Autonomous Driving

R&D

HD MAPS

ROLE OF SD/HD MAP IN AUTONOMOUS DRIVE

MAP DATA LAYOUT

MAP DATA LAYOUT

DATA EXCHANGE FORMAT

PRODUCTION PROCESS

Source Material Production Test&Validation Delivery

TESTING AREA

ADANA

SAMPLE MAP PRODUCTIONTEMSA

TEMSA FIELD TESTS

HD MAPS IN DATA FUSION

AI TRAINING WITH PHOTO REAL SIMULATION

TRAINING SET CREATIONROAD SURFACE

TRAINING SET CREATIONOBJECTS

TRAINING SET CREATIONSAMPLE TRAINING VIDEO

CONFIGURATION AND AUTONOMOUS DRIVE PLATFORM

CONFIGURATIONCONFIGURATION

ARCHITECHTURE

NVIDIA H/W

NVIDIA DRIVEWORKS

DRIVE TESTS

Dr. Ali Ufuk Peker

CEO

Infotech

Phone: +90 216 362 05 00

E-mail: upeker@infotech.com.tr

www.infotech.com.tr

EFFICIENT MULTI-OBJECT TRACKING BY STRONGASSOCIATIONS ON TEMPORAL WINDOW FOR AUTONOMOUS DRIVING

Prof.Dr. Tankut ACARMAN

Galatasaray UniversityComputer Engineering Department

Contents

• Multiple-object vehicle tracking system

• Tracklets of detected objects in consecutive frames by solving the min-cost flow problem of the temporal windows’ affinity measurements.

• We introduce a light and accurate method to combine bounding boxes extracted from multiple CNN-based detections as an alternative to detection confidence based methods such as Non-Maximum Suppression or clustering approaches predicting a single bounding-box.

Methodology

• We present a method combining bounding box from multiple CNN detections as an alternative to confidence based detection methods like NMS and clustering approaches.

• Our method leverages matching bounding boxes as proposed by CNNs subject to a detection confidence.

The Architecture

Block diagram of the proposed MOT method.

Top row shows all ’Car’ classdetections with confidencehigher than 0.05 from CNNensemble. Middle rowrepresents the ground-truthbounding boxes provided bythe KITTI dataset. Third row isthe resulting object boundingboxes from the proposedmethod.

Benchmarking results of a confidence-based CNN ensemble

Methodology

The optimal number of clusters relating toSilhoutte score.

Cluster analysis using Silhouette Score for the same frame. Optimal number of

clusters is 18 and our method produces 16 proposals with less computation.

Affinity Measurements

• Data association of detections from frame t at time index t and frame t+1 at t+ 1 is established using affinity measurements extracted from

– bounding box geometry,

– appearance comparison, and

– changing scene properties.

First row shows the detected’Car’ class objects denoted ’A’ to’L’ for the first frame. Secondrow are the next framesdetections with objects ’M’ to’X’. Third row represents DSBB ,DSA , DSCS dissimilaritymatrices and the final affinitycost matrix between objects.

Affinity measures for KITTI training sequence 0020, frames 0000 and 0001.

Affinity Measurements

Min-Cost Network Flow

• Data association is performed by solving the min-cost flow problem of the temporal windows’ affinity network.

• In general, Min-Cost flow computation requires a batch setting in order to achieve the globally optimal solution. Instead, a sliding temporal window of fixed length removing ambiguities resulting from bipartite matching, i.e. data association between only two frames to perform the online MOT method.

• When dissimilarity matrices of detected objects are created for three consecutive frames at time indexes t-2, t-1 and t , an affinity network is initiated in order to associate the objects detected at t .

• Decision is made whether an existing tracklet is sustained, terminated or a new tracklet is started.

Network Diagram

Network diagram

Example diagram for tracklet generation

Example diagram for tracklet generation

Experimental Evaluation

• The proposed MOT method is evaluated with KITTI Object Tracking Evaluation 2012 dataset for the ‘Car’ class. KITTI Object Tracking dataset consists of 21 training sequences with 8.008 frames and 29 testing sequences with 11.095 frames.

• Frames were recorded at 10 FPS from a camera mounted on the ego vehicle. All sequences have a varying number of objects and

lengths in their unique driving scenarios.

• Differential Evolution is used to optimize hyper-parameter values. A population size of 50 is created using latin-hypercube sampling.

Results

MOTA, MT, Recall and F1 metrics evaluated by the combination of affinity measures.

Benchmarking Results

MOTA, MT, Recall and F1 metrics evaluated by the combination of affinity measures.

Conclusions

• A computationally light and accurate MOT system tracking multiple objects belonging to a car class from a stream of data, i.e. an online method is developed.

• Data is associated using affinity measurements extracted from bounding box geometry, appearance comparison and changing scene properties.

• Each affinity property provides useful information in crowded and most probably occluded driving scenes for multiple car detection and tracking purposes by checking agreements of three CNN proposals for each detected object.

• The runtime is 6 times faster, which is 0.005 seconds.

Prof.Dr.Tankut Acarman

Senior R&D Consultant, researcher

Galatasaray University

Phone: +90 535 312 3145

E-mail: tacarman@gsu.edu.tr

www.gsu.edu.trTA.jpg