In this Smart Road: Intelligent Roadside Perception Industry Report, 2020 analyzes policies, industry chain and technologies, market size, business model and suppliers in the intelligent roadside perception industry (including RSU, roadside sensor, MEC and cloud control platform).
With policy support, China's intelligent roadside perception market (including radar, camera, etc.) is proliferating fast and is expected to be worth RMB40 billion by 2025. In the market, LiDAR costs the most in deployment, with a single set even priced up to tens of thousands of yuan. Yet LiDAR performs much better in roadside scenarios, notably complex intersections, for it could recognize target attributes precisely and offer far more accurate, reliable data combining with cameras. LiDAR is still at the start of application in roadside end and has yet to be used massively. If deployed on a large scale, the price of LiDAR will take a nosedive.
Intelligent roadside facilities: enormous social benefits fascinate government to invest
Core sensors of an autonomous vehicle that offer limited detection range, is far from meeting the needs for non-line-of-sight perception of surroundings, e.g., ultra long distance, intersections and blind spots. Intelligent roadside perception is thus needed to broaden the detection range around a vehicle and make autonomous driving safer and more reliable.
In current stage, roadside perception devices are led by camera, LiDAR and radar. Players like VanJee Technology Co., Ltd., China TransInfo Technology Co., Ltd. and Changsha Intelligent Driving Institute Ltd. have made sound deployments in roadside perception system, with product lines covering three systems (perception, transmission and computing). During the layout, roadside perception devices can be directly mounted on traffic light poles, intelligent poles and other facilities.
For single perception devices have some limitations, Chinese vendors are vigorously developing multi-sensor fusion solutions which perceive richer road environment information more accurately around the clock. Examples include VanJee Technology Co., Ltd. who already begins to work on data fusion between roadside 3D LiDARs and cameras to cover the drawbacks of LiDAR for greater ability to perceive.
Through the lens of commercial use, pilot roadside infrastructures constructed for demonstration nationwide have generated a mass of data, which is a solution to some technological problems. For example, the First Section (radars and cameras deployed every 250 meters) of Shanghai-Hangzhou-Ningbo Intelligent Expressway has made its pilot run for half a year, bringing about 8% faster average speed, a 20% increase in traffic capacity, a 10% reduction in congestion time, 90% accuracy in travel time forecast, a 10% fall in driving accidents, and a 10% cut in rescue time. It follows that intelligent roadside equipment could produce so obvious social benefits that government is more willing to construct for commercial use on large scale.
Business model: Application in specific scenarios will go first
After exploration, it is found that application in scenarios is an efficient model to develop intelligent roadside perception equipment. In the long-cycle market, pilot application first in specific commercial scenarios remains more efficient. Demonstration products such as intelligent perception and integrated smart traffic poles on intelligent highways and at city crossroads have been introduced successively.
In July 2020, Ezhou Airport Expressway, Hubei's first intelligent highway invested and constructed by Hubei Provincial Communications Investment Group Co., Ltd., started construction, with a video detector installed every 150 meters on both sides of the road.
In July 2020, China's first 5G multifunctional demonstration highway pole (Pole No.: K72+455) was installed on the Shenzhen Section of Guangshen Expressway. Every pole can carry such intelligent hardware as 5G base station, intelligent light, video surveillance system, emergency broadcaster and meteorological monitor.
IT giants race to enter
Evolvement of cooperative vehicle infrastructure system (CVIS) is decided by use of roadside equipment. Amid the rising penetration of intelligent roadside perception systems, the intelligent transportation network will be built up to further improve operational efficiency of cities and roads with technologies from edge computing to cloud control. Intelligent roadside perception system is a foundation for development of intelligent transportation.
Additionally, widespread layout of intelligent roadside equipment will drag down installation cost of vehicle V2X devices and push up their penetration. Only sound development of CVIS may make L4/L5 automated driving a reality.
IT tycoons like Huawei and BAT (Baidu, Alibaba and Tencent) who are bullish about the prospect of intelligent roadside equipment, scramble to march into the market.
Baidu focuses on research and development of roadside perception capabilities that meet needs of autonomous driving scenarios and fuse with V2X roadside perception information offered by the vehicle autonomous driving system. In the first half of 2020, Baidu has won the biddings for intelligent transportation projects in Nanjing, Hefei, Yangquan, Chongqing, to name a few.
Alibaba remains superior in infrastructure end, with Cainiao Alliance and ET City Brain. The giant will develop vehicle-infrastructure cooperation using technologies such as Alibaba Cloud Control Platform, AliOS and intelligent roadside facilities.
Tencent has a plan to expedite edge computing equipment installations and to build an open source platform for edge computing together with several partners.
Huawei makes concurrent efforts on vehicle and infrastructure ends, having rolled out products like roadside unit, EI-based Traffic Intelligent Twins, and OceanConnect intelligent transportation platform.
Key Topics Covered:
1 National Guiding Policies and Path Planning 1.1 Intelligent Roadside Perception Policy 1.1.1 National Strategic Guiding Policies for Intelligent Highway 1.1.2 Level of Intelligent Technology for Intelligent Highway 1.1.3 Correspondence of Level between Intelligent Highway Technology and Autonomous Driving 1.1.4 Evaluation Contents and Standards of Intelligent Highway Pilot Projects 1.1.5 Standards for Intelligent Highway Infrastructure Projects Get Improved Gradually 1.1.6 Building of Intelligent Roadside Standard System 1.2 Development Plan for Intelligent Roadside Perception 1.2.1 Vehicle-infrastructure Cooperation Promotes Intelligent Transportation 1.2.2 Development Plan for Intelligent Highway 1.2.3 Construction of 5G Intelligent Highway 1.2.4 Requirements on Deployment of Intelligent Roadside Facilities in Intelligent Highway Pilot Projects 1.2.5 Path Planning of Industrialization of Intelligent Roadside Perception under the Internet of Vehicles (IOV) Framework (2019-2025)
2 Key Technologies and Industry Chain of Roadside Perception 2.1 Key Roadside Perception Technology 2.1.1 Role of Intelligent Roadside Perception Equipment 2.1.2 Intelligent Roadside Perception Solution 2.1.3 Intelligent Roadside Perception System 2.1.4 Perception Technology 2.1.5 Transmission Technology 2.1.6 Control Technology 2.1.7 Computing Technology 2.1.8 System Integration Technology 2.1.9 Technology of Dynamic Analysis and Security Decision 2.1.10 Key Technology for Roadside Perception-Multi-sensor Fusion 2.1.11 Technological Challenges to Roadside Perception 2.1.12 Roadside Perception Technological Solutions of Major Suppliers 2.1.13 Roadside Perception Product Layout of Major Suppliers 2.2 Intelligent Roadside Perception Industry Chain 2.2.1 New Intelligent Transportation System with Vehicle-Infrastructure-Cloud Collaboration 2.2.2 Role of Vehicle-Infrastructure Cooperation in Intelligent Transportation 2.2.3 Basic Application Scenarios of Intelligent Roadside Perception 2.2.4 Intelligent Roadside Perception Industry Chain 2.2.5 Industrial Ecosystem of CVIS-based Roadside Perception 2.2.6 Roles that All Parties on the Industry Chain Play in the CVIS-based Intelligent Transportation System 2.2.7 Obstacles to Industrialization of C-V2X Roadside Facilities in China
3 Roadside Perception Market Size and Trend 3.1 Intelligent Roadside Perception Market Size 3.1.1 Market Size - Estimated Data and Assumption 3.1.2 Market Size Estimation - Intelligent Roadside Perception Equipment (RSU + Perception) 3.1.3 Market Size Estimation - Cloud Control Platform 3.1.4 Market Size Estimation - Summary (RSU + Cloud Platform + MEC) 3.1.5 Trend of Market Size 3.1.6 Development Trend of Intelligent Roadside Perception 3.2 Patents of Intelligent Roadside Perception Equipment 3.2.1 RSU Patents 3.2.2 MEC Patents
4 Roadside Perception Business Models and Cases 4.1 Roadside Perception Business Models 4.1.1 Market 4.1.2 Prerequisites for Mature Business Models 4.1.3 How to Choose Application Scenarios in Early Industrialization 4.1.4 The Best Application Scenarios in the Early Stage 4.1.5 Exploration of Scenario-based Application Models 4.1.6 IOV Business Model 4.1.7 Challenges to IOV Business Model 4.1.8 Roadside Perception Equipment Investment and Business Model 4.1.9 Commercial Deployments in Intelligent Roadside Equipment 4.1.10 Exploration of Business Model for Intelligent Roadside Equipment 4.1.11 MEC Commercial Deployment Strategy 4.1.12 Deployment of MEC in IOV Scenarios 4.1.13 MEC Business Model 4.1.14 Scenario-based Application Case 1: Platooning on Highway 4.1.15 Scenario-based Application Case 2: Connected Autonomous Driving for Smart Mines 4.1.16 Scenario-based Application Case 2: Connected Autonomous Driving for Smart Mines (Analysis) 4.2 Intelligent Roadside Perception Application Cases 4.2.1 Intelligent Highway 4.2.2 Urban Area 4.2.3 Enclosed or Semi-enclosed Area 4.2.4 Intelligent Pole 4.2.5 Intelligent Pole Architecture
