This series introduces the solutions, commercial value and typical cases of 5G Internet of Vehicles empowering self-driving sanitation vehicles, mainline logistics, terminal logistics, mining trucks, port automatic driving, automatic shuttle vehicles, Robotaxi, buses, etc. The previous article introduced 5G Internet of Vehicles empowering self-driving sanitation vehicles . This article introduces 5G Internet of Vehicles empowering trunk logistics. Please look forward to the next article 5G Internet of Vehicles empowering terminal logistics.
Text | Wu Dongsheng
The full text is 7900 words, and it is expected to read for 20 minutes
(one)
Overview of Trunk Logistics Industry
Logistics consists of links such as transportation, distribution, warehousing, packaging, handling, distribution and processing of goods, and related logistics information. Traditional logistics is more about the flow of goods in the physical sense, with the purpose of bringing operational benefits to enterprises. At the beginning, each manufacturer established its own logistics line for warehousing, dispatching and transportation of its own products, and gradually evolved the relationship between production suppliers and distributors, and then some independent logistics teams began to undertake transportation tasks. gradually separated from logistics.
Immediately afterwards, third-party logistics companies sprung up like mushrooms after rain, and the transportation channels became diversified, with logistics lines distributed across land, sea and air. Corresponding to the logistics industry in this period is the offline retail business. Logistics technology not only focuses on improving the production efficiency of production suppliers, but also focuses on accelerating the efficiency of product transportation. Therefore, traditional logistics technology is closely related to the transportation industry, and many transportation-related hot technologies have been integrated into the logistics industry, such as the construction of logistics networks, intelligent selection of logistics routes, storage location selection, and logistics route planning.
In today's e-commerce era, the global logistics industry has a new development trend. The core goal of modern logistics service is to meet the needs of customers with the minimum comprehensive cost in the whole process of logistics. With the emergence of a large number of e-commerce companies, the transportation of goods is no longer limited to manufacturers and distributors, but has developed to the point that everyone has become a node in the logistics network. Netizens place an order on an e-commerce website, All need a logistics team to provide logistics and transportation services.
Nowadays, the logistics industry urgently needs a revolution in order to keep pace with the development of the retail industry and create a new era of unbounded retail industry together. Therefore, a new generation of logistics technology is about to emerge, and autonomous driving technology is one of them[1 ] .
The rapid development of economy and trade has driven the growth of freight demand, and the logistics industry has become a pillar industry of China's national economy and an important modern service industry. In 2020, the total cost of social logistics in China will be 14.9 trillion yuan, accounting for 14.67% of GDP. In China's logistics and transportation structure, road freight occupies an absolute dominant position, and road freight accounts for more than 70% of the total social freight volume for a long time.
At present, there are 5 million large trucks in our country for trunk line transportation with a radius of 500 kilometers; 10 million trucks are used for regional transportation with a radius of 50 kilometers; E-commerce logistics and food delivery market services. At the same time, in the closed scene, there are many ports in China, and a large amount of cargo is handled every year. In addition, China's mining areas are rich in mineral resources, with an annual output of hundreds of millions of tons. The demand for transportation vehicles and drivers is increasing day by day. Among them, trunk logistics refers to the line transportation that plays a backbone role in the road transportation network. The transportation distance is long (more than 500 kilometers for inter-provincial transportation, and 200-500 kilometers for inter-provincial transportation). Heavy-duty trucks based on trucks and tractors.
The road freight logistics industry faces many challenges.
(1) Shortage of truck drivers and low job satisfaction
At present, road transportation in the logistics industry is facing a serious shortage of truck drivers, and the gap in China alone has reached 10 million.
Factors such as heavy work intensity, high safety risks, and difficulty in securing social status and welfare have resulted in low job satisfaction for truck drivers. This situation is more obvious in individual truck drivers. According to statistics, the proportion of individual truck drivers in my country has reached more than 70%, and more than 60% of the drivers do not have corresponding insurance.
More than 70% of truck drivers work more than 10 hours a day, and 40% of truck drivers work more than 12 hours a day. In addition, freight drivers may encounter various fines, as well as lost oil, lost tires, and lost goods, which may constitute more than 10% of the fixed cost expenditure.
At the same time, truck drivers are difficult to recruit and tend to be "aging". In 2020, 53.40% of truck drivers in China will be over 40 years old, and the phenomenon of aging population is obvious.
(2) The road freight market is highly fragmented, causing disorderly competition
Although China's road freight market has a trillion-level scale, 60% of its transport capacity is in the hands of small fleets and individual retail investors. In a highly fragmented market, logistics companies can only win more orders through disorderly competition at low prices, reducing their bargaining power.
In addition, logistics companies generally have the problem of difficulty in recruiting and managing drivers, and the cost and pressure of personnel management and training have increased.
(3) Road freight accidents occur frequently, causing huge losses
In 2019, the number of road freight accidents per million kilometers in China was 3.7, while in the United States it was 1.3, nearly three times higher in China. Among the approximately 7 million intercity medium and heavy trucks in my country, an average of 50,700 traffic accidents occur every year, and almost one fatal accident occurs for every 1,000 vehicles every year. The average annual accident insurance compensation of road freight companies is about 30,000 yuan per vehicle, and a single accident will also bring an average of 30,000 to 40,000 yuan in downtime losses. Accident risks will also affect the economic benefits of the upstream and downstream industries, especially insurance companies.
In China, the risk of freight transportation in the bulk industry is the highest, followed by express delivery, and the transportation of hazardous chemicals is relatively the safest. Freight risks: bulk industry > express delivery > hazardous chemical industry structure due to various factors, such as the low timeliness requirements of hazardous chemical transportation itself, fatigue driving rarely occurs, and most of them are short-distance fixed routes with low safety risks. Although most of the bulk industries are short-distance fixed routes, due to cost and route constraints, most of them choose non-high speed, the intersection risk is extremely high, and the phenomenon of fatigue driving in the bulk transportation industry is extremely serious[2 ] .
The direct causes of highway freight traffic accidents mainly include driver factors, equipment factors, environmental factors, and unexpected factors.
Among them, driver factors mainly include aggressive driving, fatigue driving, dangerous driving, and distraction. Aggressive driving includes speeding, turning too fast, overtaking and scratching, not keeping a safe distance, etc.; fatigue driving includes long-term driving without adequate rest, poor physical condition, etc.; dangerous driving includes making phone calls, looking at mobile phones, smoking, etc.; distraction Including retrograde, slip car and so on.
Equipment factors are dominated by equipment blind spots. The visual blind spot refers to the part of the area where the driver is located in the normal driving position and his sight is blocked by the car body and cannot be directly observed. Compared with cars, due to the high body and long trailer, heavy trucks have larger visual blind spots during driving, especially when turning right.
Environmental factors include weather, road conditions, etc. Sudden factors include natural disasters, passive accidents, etc.
(4) Logistics enterprises have a strong demand for cost reduction and efficiency increase
The two major costs of road freight in the United States are driver compensation accounting for nearly 39%, and fuel and maintenance accounting for 53%.
In China, the main costs of road freight are tolls accounting for 24.09%, fuel costs accounting for 22.36%, and driver salaries accounting for 21.05%. Rising labor costs and fuel costs have further squeezed the meager profit margins of logistics companies, and companies have a strong need to reduce costs and increase efficiency.
The landing scenarios of autonomous driving technology in the logistics field can be mainly divided into trunk lines, terminal distribution and closed scenarios. As shown in Figure 1.
Figure 1 The implementation scenarios of autonomous driving technology in the logistics field
Judging from the above three types of application scenarios, the application scenarios of the logistics industry are relatively simple and closed, the task purpose is clear, and the difficulty of technical implementation is generally lower than that of the passenger car scenario. From the perspective of laws and regulations and the impact on urban life, logistics vehicles are relatively less restricted. Therefore, it is easier to implement autonomous driving applications and form large-scale batch replication.
Therefore, with the commercialization of autonomous driving technology, the logistics field has become a very good entry point.
In terms of cost: In recent years, transportation costs have accounted for more than 50% of the total logistics cost, while labor costs and fuel costs in transportation costs account for a large proportion, and there is room for compression. By changing the "three drivers" to "two drivers" and "one driver", the autonomous driving technology will eventually realize no one, which can effectively reduce the cost of drivers. On the other hand, by optimizing the driving speed and acceleration and deceleration strategies, the efficiency of fuel use can be improved, and the fuel cost can be reduced by 10% to 15% every year, which is about 30,000 to 50,000/year/car. If the formation driving technology is adopted, the following distance of multiple trucks will be shortened, wind resistance will be reduced, and fuel consumption will be further reduced by about 10%.
In terms of efficiency: autonomous driving can increase the continuous driving time of vehicles, while driving at higher speeds and shorter distances. L4 level and above self-driving heavy trucks can theoretically achieve 24-hour operation, which means shorter delivery cycle and more transportation volume. In addition, the fleet management platform can be used for unified scheduling and management, optimize driving tasks and driving routes, and comprehensively improve the transportation efficiency and operation management efficiency of road freight. It is estimated that autonomous driving will approximately double revenue growth for logistics companies by increasing operating hours and improving efficiency.
In terms of safety: automatic driving can effectively avoid safety accidents caused by driver factors such as aggressive driving, fatigue driving, dangerous driving, distraction, etc., and 360° no dead angle perception and super long sight distance can reduce safety accidents caused by visual blind spots , with a faster reaction speed than human drivers, to create safer road freight. On the other hand, through the Internet of Vehicles technology, self-driving heavy trucks can predict various potential dangers ahead of the road in advance and avoid traffic accidents in advance.
In terms of environmental protection: the application of autonomous driving technology can optimize driving strategies, save fuel consumption, thereby reducing pollutant emissions of road freight, and creating green logistics [3] .
Among the three major scenarios, the overall transport capacity of trunk logistics is the largest, and the scenarios are relatively concentrated. At the same time, trunk line vehicle logistics transportation is a relatively standardized product with a high degree of technical reuse and strong economies of scale.
(two)
5G Internet of Vehicles Empowers Autonomous Driving of Trunk Logistics
Mainline logistics are mostly used in high-speed scenarios, and the vehicles run fast, so there are high requirements for the environmental perception range of the automatic driving system. Due to the poor maneuverability, stability and precision of heavy trucks, longer braking distances, larger turning radiuses, and more precise and robust control are required. Therefore, higher requirements are placed on technology at the perception and control levels.
With the development of L3 self-driving heavy trucks to L4 self-driving heavy trucks, the requirements for the perception, calculation and execution capabilities of the self-driving system will be further improved. The drive-by-wire chassis is an indispensable key component for the implementation of autonomous driving, but China's local OEMs and suppliers have little accumulation of drive-by-wire chassis technology and products in this field. The upstream and downstream enterprises of autonomous driving heavy trucks should cooperate to solve the pain points of the scene, make adaptations in product design and development, and jointly promote the R&D and production of key technologies and components suitable for higher-level autonomous driving systems.
In addition, self-driving heavy trucks also focus on transportation efficiency. The decision-making layer of autonomous driving not only needs to control the acceleration, braking, and steering of vehicles, but also needs to coordinate vehicle scheduling and plan driving paths from a macro level to optimize the carrying capacity.
C-V2X technology will be widely used in autonomous driving heavy trucks. The autonomous driving cloud platform establishes real-time communication with vehicles through RSUs and differential base stations installed at the roadside, and uses roadside sensors and roadside units to monitor the running status of vehicles and obtain auxiliary information such as traffic signals. The cloud control platform aggregates data to establish a high-precision electronic map, completes tasks such as vehicle positioning, vehicle scheduling, and fault detection, and realizes remote takeover of the vehicle when an abnormality is detected.
The use of IoV software and hardware will further enhance the real-time and richness of information. The self-driving heavy-duty truck perception module uses recognition algorithms to perceive objects in the environment that may affect vehicle driving, estimate the positions of pedestrians, vehicles, and static obstacles, predict the movement of moving objects, and identify traffic signs and road markings. But for autonomous driving decisions, if you only rely on the vehicle's own perception of the environment, it is equivalent to the driver driving only by "intuition". To enable vehicles to determine the best driving route in advance, technologies such as cloud control platforms, high-precision maps, and Internet of Vehicles are needed. Only by integrating information from the vehicle end, road end, and cloud, can the autonomous driving heavy truck decision-making layer issue instructions to the wire control actuator according to the actual situation, and complete operations such as steering, acceleration, deceleration, and parking.
The combination of autonomous driving and formation driving will maximize road traffic efficiency and vehicle fuel economy. On the special lane for automatic driving, the combined application of "automatic driving + formation driving" is easier to realize.
Platooning describes the sharing of one or more messages that support coordinated longitudinal motion of vehicles and can also include lateral control of vehicles. The types of information that can be exchanged include position, trajectory, headway and qualifying status. The formation process management is realized through the information interaction between vehicles, including the process of creating a fleet, joining a fleet, leaving a fleet, and disbanding a fleet. As shown in Figure 2, the leading vehicle 1, following vehicles 2 and 4 in the convoy, and the free vehicle 3 outside the convoy all send corresponding messages to confirm each other's identities and formation operations. The vehicle formation management application can provide efficient and convenient member management means for the fleet business, and improve the intelligence level of the vehicle formation.
Figure 2 Schematic diagram of formation driving
Using C-V2X technology, it is possible to realize the state sharing between vehicles in formation, and the following vehicles in the formation can follow more closely and more stably than in other situations; it can realize the intention sharing among vehicles in formation, and the pilot vehicle 1 detects possible For the forward danger that requires queue deceleration, it will share its own braking action intention with following cars 2 and 4, so that the deceleration of all vehicles will be more stable; it can realize the collaborative decision-making of joining the queue, and the free car 3 can join the queue in the middle of the driving process . As shown in Table 1.
Table 1 Formation driving using C-V2X technology
Function |
Classification |
Transmission Mode and Directionality |
interactive information |
functional class |
Queue awareness and vehicle control |
A. Status Sharing |
two-way Leading car 1 —following cars 2 , 4 Pilot Car 1 — Freedom Car 3 |
platoon activation status; speed, trajectory and position of vehicles in the platoon |
Support: Following vehicles in a platoon can follow more closely and stably than would otherwise be the case |
Support: Liberty car 3 further perceives that lead car 1 is forming a queue with other cars |
||||
Prediction of braking action |
B. Intent Sharing |
unidirectional Leading car 1 —following cars 2 , 4 |
speed reduction plan |
Support: Lead car 1 detects a forward hazard that may require platoon deceleration, resulting in smoother deceleration for all vehicles |
join the queue |
C. Collaborative decision-making |
two-way Pilot Car 1 — Freedom Car 3 Leading car 1 —following cars 2 , 4 |
seek to join the queue; allow to join the queue in the middle; notify other vehicles in the queue |
Empowerment: Liberty Car 3 can join the queue in the middle |
(three)
The commercial value of network-linked logistics autonomous driving
Participants in autonomous driving trunk line logistics mainly include autonomous driving technology companies, OEM vehicle manufacturers, and logistics platform companies.
Autonomous driving technology companies in trunk logistics usually build a technical architecture and underlying hardware configuration for L4-level autonomous driving. Of course, some companies choose to cut into the L3-level autonomous driving track. The commercialization process of L4 self-driving heavy trucks can be divided into five stages: prototype, engineering verification, design verification, production verification and mass production. Autonomous driving technology companies also need to conduct in-depth cooperation with key component suppliers, including suppliers of autonomous driving sensors, computing power platforms, and commercial vehicle wire-controlled chassis.
According to the classification standard of China Association of Automobile Manufacturers, trucks are divided into four categories according to their total mass: heavy, medium, light, and micro. Among them, those with a total mass of more than 14 tons are called heavy-duty trucks (heavy trucks). Heavy-duty trucks can be roughly divided into three market segments, namely complete vehicles (complete vehicles), incomplete vehicles (chassis) and semi-trailer tractors, of which the sales of complete vehicles account for half of the country.
China's heavy-duty truck OEMs have a high market concentration. FAW Jiefang, Dongfeng Group, Sinotruk, Shaanxi Automobile Group, and Foton Group are firmly in the top five, and the market share of the top five companies has been above 75% for a long time. In 2020, the sales volume of heavy trucks was 1.623 million units, of which the sales volume of complete vehicles was 287,000 units. FAW Group sold 377,000 heavy trucks, with a market share of 23.3%; followed by Dongfeng Motor, with a sales volume of 311,100, with a market share of 19.2%; Sinotruk and Shaanxi Automobile Group both sold more than 200,000 vehicles, with market shares of 18.5% and 19.2%, respectively. 14.0%; Beiqi Foton also sold more than 100,000 vehicles, reaching 150,200 vehicles, with a market share of 9.3%.
Logistics platform companies include Ali Cainiao, JD.com, Manbang, G7, Shiqiao, SF Express, Suning, Aneng, Fuyou Truck, Debon, etc., and they have key ecological resources such as fleets and cargo sources.
At present, there are mainly three business operation models for autonomous driving trunk logistics. For trunk logistics autonomous driving technology companies, the main business can be divided into autonomous driving technology services, autonomous heavy truck leasing services, and autonomous driving freight services. According to the business portfolio and assets, the business models of autonomous driving technology companies can be divided into asset-light model, asset-heavy model and hybrid model [3] .
The first is the asset-light model . Autonomous driving technology companies, OEMs, and logistics platform companies establish tripartite cooperation. Automakers sell autonomous driving heavy trucks to logistics platform companies, and autonomous driving technology companies only provide autonomous driving technology services.
Logistics platform companies directly purchase self-driving heavy trucks, and then rely on the technical support provided by self-driving technology companies to provide logistics services to customers and charge service fees. As shown in Figure 3.
Figure 3 Autonomous driving heavy truck light asset model
The second is the asset-heavy model . Self-driving technology companies purchase self-driving heavy trucks from OEMs, provide logistics platform companies with self-driving heavy-duty truck rental services and self-driving technology services, or directly provide customers with self-driving freight services when capacity is tight, and charge by mileage Shipping service fee. As shown in Figure 4.
Figure 4 Mode of self-driving heavy trucks and heavy assets
The third is the mixed mode . On the one hand, the self-driving technology company provides self-driving technology services for the logistics platform company's own vehicles. On the other hand, purchase self-driving heavy trucks from OEMs, provide logistics platform companies with self-driving heavy-duty truck rental services and self-driving technology services, or directly provide self-driving freight services to customers when the capacity is tight, and charge self-driving transportation according to mileage service charge. As shown in Figure 5.
Figure 5 Autopilot heavy-duty truck hybrid mode
If the solution cost of autonomous driving trunk line logistics reaches 300,000 yuan/vehicle, it can achieve profitability within 3 years. The solution cost mainly includes perception sensors, computing platforms, redundant braking, steering, power supply and other hardware and corresponding software brought about by functional safety requirements. Assuming a service life of 3 years, 15% of the annual operation and maintenance costs. Each year, due to automatic driving, the driver's labor cost and fuel cost savings are 60,000 to 150,000/year/car, 30,000 to 50,000/year/car, and the median is 145,000/year/car. The car can achieve breakeven in 3 years. See Table 2 [4] .
Table 2 Profit changes brought about by self-driving heavy trucks
cost changes |
|
Autonomous Driving Solution Acquisition Cost |
+300,000 yuan |
Operation and maintenance cost |
+45,000 yuan / year |
Driver's labor cost |
-105,000 yuan / year |
fuel costs |
-40,000 yuan / year |
Changes in profitability after 3 years of use |
0 million |
Heavy-duty trucks autonomously driving in formation will bring additional commercial value. The trucks are heavy and the braking distance is long. In the case of manual driving, the maximum safety distance between trucks needs to be maintained. Adding a radar anti-collision system can effectively reduce the perception reaction time, but it still needs to start the braking of the rear car after the radar detects that the front car has decelerated. In the formation driving mode, the steering is manual, but the braking is automatic. After the leading vehicle makes a braking command, V2V can achieve an instantaneous response between the front and rear vehicles, and the rear vehicle can even stop before the front vehicle starts to decelerate. Braking is automatically applied, and this instantaneous response means the truck can safely follow at very small distances.
In the state of driving in formation, because the distance between the vehicles is very close, an "airflow vacuum zone" is formed between the two vehicles, and no air vortex will be generated, which can effectively reduce air resistance, fuel consumption and carbon dioxide emissions. Independent fuel efficiency tests conducted by NACFE (North American Council on Freight Efficiency) and the US Department of Energy and Transportation have shown that fuel savings of at least 10% can be achieved.
(Four)
Typical Cases of Automated Driving in Networked Trunk Logistics
Case 1: Autonomous driving formation driving on Yanchong Expressway
The Yanchong Expressway is an expressway from Yanqing, Beijing to Chongli, Zhangjiakou, with a total length of about 116 kilometers. It is a direct expressway between the Yanqing and Zhangjiakou Chongli venues for the 2022 Winter Olympics. Among them, the total length of the Beijing section is about 33.2 kilometers, starting from the west side of Dafutuo Village in Yanqing District, and ending at the city boundary and connecting with the Hebei section of Yanchong Expressway.
In December 2019, it demonstrated L4-level automatic driving and platoon-following tests based on C-V2X vehicle-road coordination technology in a two-way four-lane fully enclosed environment. The demonstration road section starts from the Banquan service area, passes through a 1-kilometer plain road section, 4 tunnels and a 3-kilometer viaduct section, and ends at the exit of the Xiaohaituoshan Stadium.
There are multiple unfavorable conditions in the tunnel section, such as poor positioning signals, rapid light and dark changes, and low temperatures of -20 degrees Celsius, which put forward higher requirements for the overall intelligent level of the transportation system. For the first time in the Xiyangfang super-long tunnel, L4-level automatic driving of Audi passenger cars in a 2-kilometer tunnel, Beiqi Foton/Tucson’s 14-kilometer heavy-duty truck platoon following and passenger car 3-vehicle formation driving (including 9.8 kilometers of continuous driving) ultra-long tunnel group section).
The demonstration uses the manual driving mode of the leading car, and the automatic driving mode of the rear car to perform platoon cruise, platoon acceleration, platoon lane change, queuing synchronous deceleration and parking, and platoon-vehicle-road coordination scene tests. The test results show that the platooning of vehicles can achieve the technical index of maintaining a distance of 10 meters between vehicles at a speed of 80 km/h. Single-driver multi-vehicle platoon car-following has three aspects of competitiveness, that is, saving fuel (about 10% to 15% of fuel consumption can be reduced, as well as driver labor costs), improving safety (the system can complete the operation within 0.1 seconds, The driver needs 1.4 seconds to react), and the road traffic capacity is improved (the distance between vehicles is reduced, and the number of vehicles accommodated on the road surface will increase).
Smart highways mainly deploy C-V2X RSUs, cameras, millimeter-wave radars, switches and other equipment. Among them, the RSU is deployed in a zigzag pattern at an interval of 210 meters on both sides of the two-way lane; the cameras are deployed symmetrically at an interval of 105 meters on both sides of the two-way lane; the millimeter-wave radar is deployed symmetrically at an interval of 210 meters on both sides of the two-way lane. Through the edge intelligent computing of multi-source data such as millimeter-wave radar and video, real-time perception of abnormal traffic events such as highway accidents and pedestrians can be realized around the clock, and sent to the vehicle in real time through the C-V2X network. deceleration and emergency stop [5] .
Case 2: L3 autonomous driving heavy truck
FAW Jiefang and Zhijia have released L3 self-driving heavy trucks, which have five product advantages of intelligence, safety, fuel economy, reliability and interconnection. Zhijia L4 technology stack dimension reduction application allows drivers to free their hands and feet on high-speed roads. The fuel-saving algorithm can effectively reduce fuel consumption by 10% to 20%. The safety development process, redundant architecture design, and vehicle-level certification of software and hardware ensure driving safety in an all-round way. It can also effectively alleviate the fatigue of long-distance driving, and finally realize cost reduction and efficiency increase in an all-round way. Finally, the data engine and OTA remote upgrade function can realize data closed-loop, providing users with continuous iterative upgrades of products.
Manbang united with Zhijia to carry out commercial operation based on this self-driving heavy truck. The Manbang platform already has 9 million certified drivers and 4 million certified cargo owners, covering 339 cities across the country and 110,000 lines. The annual transaction scale of the platform reaches 800 billion yuan. The new model will be connected to the Manbang platform, using its excellent safety and fuel economy to help create a benchmark for customer service. And through the optimization of supply matching and route design, a large amount of real operation road test data is collected, the automatic driving software system is efficiently trained, and the vehicle performance is continuously improved by using the OTA remote update system [6 ] .
references
[1] Yuantong Research Institute. Application of 5G network technology in the new generation of logistics industry [R]. 2019,5.
[2] G7, Kearney Consulting. China Road Freight Safety White Paper 2021[R]. 2021.
[3] Yiou Think Tank. 2021 Research Report on the Commercial Application of China's Autonomous Driving Trunk Logistics [R]. 2021.
[4] China Electric Vehicle 100. Autonomous Driving Application Scenarios and Commercialization Path[R]. 2020,6.
[5] 5G industry application. An article to understand the status and future of smart expressway vehicle-road collaboration [N]. 2020,5.
[6] Leifeng.com. Zhijia Technology and Manbang Group deepen strategic cooperation to accelerate the implementation of L4 unmanned driving technology [N]. 2020,9.
- END -
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