5G-based Driving Assistance for Autonomous Vehicles

CMCC ZENGFENG

2018.6

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R15 first version of the

5Gstandard

R14 Pre5G

Strive to achieve commercial use of 5G

in 2020

R16 complete 5G standard

Key Technology Verification

2013 2014 2015 2016 2017 2018 2019 2020

Large scale Trial & Pre-commercial Networks

Basic Needs and Objective

Key Technology Research

Establish Central and Regional Laboratory to Conduct Cross-industry Innovation

System Verification

Candidate Standard Scheme Study

Enhanced Technology Research

Technology Development Strategy

Technology Research

Strategy

Standard

Trial Verification

Ecological

Network Construction strategy

China Mobile 5G Innovation Center

China Mobile's 5G development layout and plan

CMCC 5G Trial Plan and Scale

5G Network -

5G Devices -

17 Cities 1100+ 5G eNBs

2000+ Terminals

Scale-up trials in 5 cities

Application showcase in 12 cities

Exploring converged innovation on 5G services

Launching 5G terminal forerunner plan

Speeding up 5G Development

2020~ 2019 2018

5G Pre-commercial Trial

5G E2E Commercialization

Scale-up Trial Application Showcase

Carry out 5G Application Demonstration and Study in 7 Areas

Major Projects in 2018

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Smart Transportation

Autonomous driving

AR/VR 4K Live Streaming

Video Fusion

HD Cloud Gaming Smart Factory Smart Grid

Cloud Robot AI

Mobile Remote Medical

Transportation

Video & VR/AR Entertainment Manufacturing Energy

Artificial Intelligence Healthcare

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Paving the path to more autonomous driving

Network Oriented

Intelligent Oriented

Driver Assistance Partial Automation Conditional Automation Full Automation

Informatio

n

interaction

Collaborat

ive

Sensing

Network

collaborati

ve decision

& control

5G

LTE-V

4G

The Roadmap of Evolution of Intelligent Connected Vehicle

The roadmap was released by

Ministry of Industry and

Information Technology of the

People’s Republic of China.

By 2025，China will establish a

ICV standard system that can

support full automatic driving.

The goal of “networked oriented

” is to support network

collaborative decision & control .

There are three stages of network

oriented ICV.

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5G Enhances Information Interaction

Autonomous vehicles will become a

mobile space, which can be used for

entertainment，work and telematics.

AR/VR、Game、Movie…

Onboard Information

Download local 3D HD map imme

diately

Model and analysis of dangerous

situation

Download HD Map

Autonomous vehicles of Level 3: The cars

can handle almost all the scenario by itself,

but if they meet some unsolvable problem ,

remote drivers will be informed and intervene.

Remote drivers will control several

unmanned vehicles remotely.

Enables Commercial Operation

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5G Enhances Collaborative Sensing

Perception is the key factor of autonomous driving. Sensors such as lidar，radar and camera can only ‘see’ the surrounding objects by processing echo signals , but processing results will be affected and limited by some extreme bad environment, such as foggy, rainy and sunlight. The network-assisted vehicles can communicate with surrounding vehicles and road side infrastructure directly:

To exchange information such as velocity，position ， acceleration and trajectory in future. To overcome the limit of perception range and environment. To evolve from intelligent car to intelligent connected car.

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5G Enhances Collaborative Sensing LTE-V2X defines 27 application scenarios for assisted driving, and demand of each scene is defined in terms of time delay, movement speed, reliability and other aspects.

5G-V2X defines a number of scenarios for autonomous driving requirements, and the requirements of each scene vary greatly. Different technical combinations are needed to meet different scene requirements.

Source：3GPP SA1 TS22.185 Source：3GPP SA1 TS22.186

peripheral message collection.

Traffic status alarms

Platoon

Remote control

maximum latency:5~10ms reliability : >99.999% End-to-End latency: 100ms

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5G Enhances Collaborative Control & Decision

Drawbacks of Intelligent vehicles without network： Computational complexity : Driverless car needs variety sensors

to adapt complex and variant environment, so it needs powerful

CPU/GPUs to process signals ;

Expensive: The price of intelligent vehicles is very high,

because they needs expensive sensors and processors；

Limitation of on board perception： From spatial dimension, on

board perception is so limited, and it can only perceive the area of

a certain range centered on the vehicle. In some special areas,

such as street corner and intersection, there will be certain blind

area.

Hierarchical decision architecture for connected car： The data is processed at different layers according to safety

requirements and latency requirements.

Perceptions of peripheral environment will be enhanced by road

side units which are composed of cameras or radars, and also by

collecting more road information.

Some data may be processed in MEC server , that will reduce

computational complexity on board.

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Hierarchical decision architecture of connected car

Source：3GPP SA1 TS22.185

……

Vehicle control

Get Message from

instant trends of

driving space

Decision on

board

Arbitration between

regional decision and

on-board decision

5G Gateway

Perception on

board

MEC Service(Storage、compute,..）

Vehicle state message

（position、velocity 、

acceleration …）

Road side

auxiliary

sensing

system

Original

sensor

message

Instant Trends of Driving Space

OEM A

Lane Level

planning，Splicing of

ITDS

Regional

Traffic

planning

Arbitration of

different

dispatchers

Regional decision Region Control

Regional Traffic planning

（Traffic light、

Lane、Traffic signal）

Vehicle control To

terminal

Vehicle control

results to RCL

Vehicle big

data

Climate

（Snowy 、rainy、

foggy ）

Traffic big data

Data perception

Vehicle data

management

High level decision

HD map

management

Macro control

Management of

MEC hand over

Traffic

dispersion

Traffic

planning

Path

planning

Regional Control Layer（RCL）

Terminal Layer

Road cooperative Control Layer

Perception Decision Control

Network architecture of hierarchical autonomous driving

Operator 1's Core Network

Operator 2's Core Network

Path Planning Transport Traffic Control

Road Side unit

High-definition Map Database

Road Side unitRoad Side unit

Cloud

Traffic Light Control

Lane Level Planning

Multi-sensor Data Fusion

High accuracy positioning

Operator 1's Access Network

MEC Host

Traffic Light Control

Lane Level Planning

Multi-sensor Data Fusion

High accuracy positioning

MEC Host

Traffic Light Control

Lane Level Planning

Multi-sensor Data Fusion

High accuracy positioning

MEC Host

Operator 1's Access Network Operator 2's Access Network

Regional Control Layer（RCL）

Terminal Layer

Road cooperative Control Layer

MEC host deployed on the

access network can operate

multiple autonomous driving

applications. To be specific, it

completes the analysis and

processing of the data from

connected vehicles and road

side sensors.

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Terminal Layer

Terminal Layer

Regional Control

Layer（RCL）

Road cooperative Control Layer

Upload： Integrated map from vehicles Original sensor message

Download Integrated regional map(Instant

Trends of Driving Space) Decisions from regional control layer

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Regional control layer

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OAI Trials in Several Cities

OAI Trials in Several Cities

Use OAI to control a car

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In summary

5G paves the path to more autonomous driving

5G enhances information interaction 5G enhances collaborative sensing 5G enhances collaborative control & decision

The architecture of hierarchical autonomous driving is established Terminal layer Regional control layer Road cooperative control layer

Thank You

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