intelligent transportation and autonomous vehicles• participating the design of national high...

2018/11/21

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• Chinese geodesist and educator;

• President of Duke Kunshan University from 2012.

• Academician of Chinese Academy of Engineering since 1999;

• Former president of Wuhan University from 2003 to 2008;

• Have developed the first GPS satellite positioning data processing system in China;

• Participating the design of National High Precision GPS Network;

Introduction to Pro. Liu Jiangnan

Intelligent Transportation and Autonomous Vehicles

Organizer:

2018/11/21

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Historic evolution of using maps in the car

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Foreword

Contents

1. Background and demands

2. Compared with traditional map

3. Main elements and layers

4. Outlook of technologies and standards

5. Summary and thoughts

2018/11/21

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1.1 Advancement of GNSS technology

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1.1 Advancement of GNSS technology

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1.2 Accurate and intelligent trends of traffic management

1.2 Accurate and intelligent trends of traffic management

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Intelligent High-Precision Map(IHPM) is the carrier of high-

precision environment perception and the foundation of real-time

road control for autonomous/unmanned driving

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1.3 Intelligent and Connected Vehicles and Autonomous Driving

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Contents






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2.1 Definition and classification of Intelligent High-Precision Map(IPHM)

� IPHM is an intelligent map that meet the following constraints:

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2.1 Definition and classification of Intelligent High-precision Maps(IPHM)

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�According to Application Scenarios，it can be divided into four

categories：high precision maps for vehicle, for control center, for

robots, and for cloud

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� Information load and expression(What is the information?)

2.2 Theory development of high-precision map for vehicles

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Traditional Maps

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� The use of information (How to use the information?)


� The use of information (How to use the information?)

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Contents






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1. Static layer

• Road model

• Traffic infrastructure

• Position information

3.1 Layers for Intelligent High-precision Maps

3. Dynamic layer

• Vehicles

• Pedestrian

4. User layer

• Vehicle configuration

• Scene information

• Behavior monitoring

• Cognitive characteristics

2. Real-time layer

• Road Construction

• Traffic jam

• Traffic management

• Weather prediction

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3.2 Examples of representative elements

Light pole

Lane

Lane line

Curbs

Road boundar

y line

Lane connection

Traffic jam

Scene

occupancy grid

Vehicle perception

Trajectory record

þ��Ð ä��Ï��×Ñ�� Reversible lanes

Road sign

Traffic management

PedestrianV2X

Traffic accident

Traffic sign

Contents






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Data collection and updating

• Centralized professional collection

• Crowdsourcing

• Integration of professional collection

and crowdsourcing

Dynamic data interaction

• Machine vision and target detection

• Multi-source sensor collaboration

• V2X

Computing Pattern

• Cloud computing

• Edge calculation (cloud and terminal

collaboration )

Application and usage

• Lane level map matching

• Map-assisted perception and position

• planning and obstacle avoidance

Autonomous intelligent control

• Accurate match between traffic signs and

dynamic position of vehicles

• Real-time driving control through

information in Internet of Vehicle

• Environmental and behavioral monitoring

• ��

Map production and data processing

• Multi-semantic segmentation

• Computer vision

• Geometric rendering and topology

4.1 Several technical problems that need to be solved

4.2 Standards development

� ISO TC204/WG3—Database construction and data exchange standards for intelligent traffic maps

� OpenDRIVE (standardize the logical road description to facilitate the data exchange between

different driving simulators)——OpenDRIVE® V 1.4 Format Specification, Revision H

� ADASIS(Define a interface specifications)——ADASIS v1�ADASIS v2�ADASIS v3

� ETSI——ETSI TR 102 863 V1.1.1 (2011-06)

� NDS Steering Committee put HD map on standardization agenda in 2013 by installing dedicated

Working Group 3,then in 2017 NDS standard is ready for 2020 autonomous cars

� Open AutoDrive Forum is the cross-domain platform driving standardizations in the area of

autonomous driving

� DMP (Dynamic Map Planning Co., Ltd. )——Study the methodologies of developing and

maintaining high-precision 3D map data for Automated Driving

� Lead by Wuhan University—— “Data Specifications of Road High-precision Electronic Navigation Map”

� Lead by Beijing University of Civil Engineering and Architecture—— “Production Technology Specifications of

Road High-precision Electronic Navigation Map”

� China ITS Industry Alliance(C-ITS) ——”Digital Map for Intelligent Vehicle Data Model & Exchange Format

Specification”

� CAICV HD MAP WG—— 3 subjects: ”Research on Automated Driving Map Standard System”(Lead by

NavInfo)�” Research on Map deflection and Communication between mapping management departments”(Lead by

Amap)�”Research on Autopilot Map Technology and Application Roadmap” (Lead by Wuhan KOTEI Informatics

Co., Ltd.�� Baidu—— Apollo OpenDrive specifcations

International standards Standards in China

� 2018-20190 Ministry of Natural Resources 1National Administration of Surveying, Mapping and Geoinformation of China2entrusts Wuhan

University, Tongji University, Beijing University of Civil Engineering and Architecture, etc. to set standard related to Surveying, Mapping and

Geoinformation Industry, and release the draft of “Data Specifications of Road High-precision Electronic Navigation Map "

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4.3 Roadmap

:��Roadmap of Intelligent high-precision maps � “The intelligent vehicle innovation and

development strategy” released by the national

development and reform commission of China

specifies the national intelligent vehicle

innovation and development strategy: the

framework of road network facilities,

regulations and standards for standard

intelligent vehicles will be basically formed by

2020, and be fully formed by 2025, by that time ,

"human-vehicle-road-cloud" could be highly

coordinated.

� The European Union plans to develop

intelligent transport from the two aspects of

Internet road environmental resource

aggregation and intelligent network vehicles,

and aims to achieve that goal by 2050.

� Japan conducted a static data survey of autono

mous driving in 2015; Conduct dynamic data re

search in 2016, and establish dynamic map pla-

tform to discuss dynamic map data use cases an

d element contents. Japan will achieve full cove

rage of high-precision maps by 2020

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Intelligent

high-

precision

maps

Dynamic

high-

precision

maps

Static

high-

precisio

n maps

� Establish basic map system for vehicles

covering whole country, complete the

standard and uniform basic maps for

vehicle.

� High precision maps realize autonomous

intelligent control, with self-adaptive, self-

evaluation and self-learning

� Optimize global control of the entire

network, provide comfortable, convenient, ,

green, civilized trip and dynamic, real-time,

intelligent, ubiquitous service

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� Complete the static data survey and

formulation of related standard

� Establish data acquisition system with high

precision and update ecological chain

� Carry out dynamic data survey and determine

types, contents, and definitions of dynamic map

data

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(,*(-

� High precision map related standard syst-

em will be formed in an all-round way

� ”human-vehicle-road-cloud

” will achieve highly collaborative

� Wireless communication network for

vehicles will basically meet the demand

(.

Contents






2018/11/21

13

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4.1 Several technical problems that need to be solved

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intelligent transportation and autonomous vehicles• participating the design of national high...

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