5G-based Driving Assistance for Autonomous Vehicles CMCC ZENGFENG

Transcription

1 5G-based Driving Assistance for Autonomous Vehicles CMCC ZENGFENG

2 China Mobile's 5G development layout and plan Strategy Basic Needs and Objective Technology Development Strategy Network Construction strategy Technology Research Key Technology Research Candidate Standard Scheme Study Enhanced Technology Research Standard R14 Pre5G R15 first version of the 5Gstandard R16 complete 5G standard Trial Verification Key Technology Verification System Verification Large scale Trial & Precommercial Networks Strive to achieve commercial use of 5G in 2020 Ecological China Mobile 5G Innovation Center Establish Central and Regional Laboratory to Conduct Cross-industry Innovation 2

3 CMCC 5G Trial Plan and Scale Speeding up 5G Development Scale-up Trial Application Showcase G Pre-commercial Trial G E2E Commercialization 2020~ Scale-up trials in 5 cities Application showcase in 12 cities 5G Network - 17 Cities G enbs 5G Devices Terminals Exploring converged innovation on 5G services Launching 5G terminal forerunner plan

4 Major Projects in 2018 Carry out 5G Application Demonstration and Study in 7 Areas Video & VR/AR Entertainment Manufacturing Energy AR/VR 4K Live Streaming Video Fusion HD Cloud Gaming Smart Factory Smart Grid Transportation Artificial Intelligence Healthcare Smart Transportation Autonomous driving Cloud Robot AI Mobile Remote Medical 4

5 Paving the path to more autonomous driving 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. 5G LTE-V 4G Network collaborati ve decision & control Collaborat ive Sensing Informatio n interaction Network Oriented Intelligent Oriented Driver Assistance Partial Automation Conditional Automation Full Automation The Roadmap of Evolution of Intelligent Connected Vehicle 5

6 5G Enhances Information Interaction Onboard Information Download HD Map Enables Commercial Operation Autonomous vehicles will become a mobile space, which can be used for entertainment,work and telematics. AR/VR Game Movie Download local 3D HD map imme diately Model and analysis of dangerous situation 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. 6

7 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. 7

8 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. Platoon peripheral message collection. End-to-End latency: 100ms Source:3GPP SA1 TS Remote control maximum latency:5~10ms reliability : >99.999% Traffic status alarms Source:3GPP SA1 TS

9 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. 9

10 Hierarchical decision architecture of connected car Perception Decision Control Road cooperative Control Layer Climate (Snowy rainy foggy ) Data perception Vehicle big data Traffic big data Vehicle data management High level decision HD map management Path planning Management of MEC hand over Macro control Traffic dispersion Traffic planning Regional Control Layer(RCL) Road side auxiliary sensing system Instant Trends of Driving Space Vehicle state message (position velocity acceleration ) Original sensor message Regional Traffic planning Regional decision OEM A Lane Level planning,splicing of ITDS Arbitration of different dispatchers MEC Service(Storage compute,..) Regional Traffic planning (Traffic light Lane Traffic signal) Region Control Vehicle control To terminal Terminal Layer Get Message from instant trends of driving space Perception on board Source:3GPP SA1 TS G Gateway Arbitration between regional decision and on-board decision Decision on board Vehicle control Vehicle control results to RCL 10

11 Network architecture of hierarchical autonomous driving Cloud Road cooperative Control Layer Path Planning Operator 1's Core Network High-definition Map Database Transport Traffic Control Operator 2's Core Network 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. Regional Control Layer(RCL) Traffic Light Control Lane Level Planning MEC Host Multi-sensor Data Fusion High accuracy positioning Lane Level Planning Traffic Light Control Multi-sensor Data Fusion MEC Host High accuracy positioning Traffic Light Control Lane Level Planning Multi-sensor Data Fusion MEC Host High accuracy positioning Operator 1's Access Network Operator 1's Access Network Operator 2's Access Network Terminal Layer Road Side unit Road Side unit Road Side unit

12 Terminal Layer Terminal Layer Upload: Integrated map from vehicles Original sensor message Download Integrated regional map(instant Trends of Driving Space) Decisions from regional control layer Regional Control Layer(RCL) Road cooperative Control Layer 12

13 Regional control layer 13

14 OAI Trials in Several Cities 14

15 Use OAI to control a car OAI Trials in Several Cities

16 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 16

17 Thank You 17