Autonomous Commercial Vehicle Industry Report, 2019-2020

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Mar 23, 2020, 08:10 ET

NEW YORK, March 23, 2020 /PRNewswire/ --

Self-driving commercial vehicle study: billions of dollars swarm into the field where fifty players compete fiercely, according to our recent report the Autonomous Commercial Vehicle Industry Report, 2019-2020

Read the full report: https://www.reportlinker.com/p05877105/?utm_source=PRN

Automated commercial vehicle will be realized ahead of self-driving passenger car. The number of enterprises that forayed into commercial vehicle autonomy in 2019 doubled the prior-year figure. A case in point is TuSimple raised funds up to $215 million in 2019 and won 18 contract clients that devote themselves to transportation in the United States.

After over a year's halt, Waymo restarted self-driving truck tests in May 2019. Pony.ai, a competitor in RoboTaxi operations and with the investment of $400 million from Toyota and other investors, was also pressing ahead with self-driving truck tests in 2019.

Truck platooning still remains the focus of automated truck test and gets increasingly combined with 5G technology in 2019, into which the companies have set foot including Daimler, CiDi, Scania, Iveco, Volvo, DAF, Peloton, SAIC, Foton, Huawei, TuSimple and Hyundai.

It can be seen from the top 10 holders of platooning patents that the competitive ones are Continental, Scania, Peloton, Ford, Toyota, among others. Chinese counterparts are rarely seen.

Commercial vehicle going smart coincides with road intelligence, about which the laws and regulations are getting perfect. Road infrastructure for automated driving is classified by ERTRAC (European Road Transport Research Advisory Council) into the five in the table below.

China Highway & Transportation Society (CHTS) Automated Driving Working Committee and Automated Driving Standardization Working Committee issued the Intelligent Connected Road System Levels and Interpretations (Exposure Draft) in September 2019, according to which traffic infrastructure system is divided into I0 level (zero information/intelligence/autonomy), I1 (preliminarily digital/intelligent/automated), I2 (partially connected/intelligent/automated), I3 (conditional autonomy and high connectivity based on traffic infrastructure), I4 (highly automated and based on traffic infrastructure), and I5 (fully automated driving based on traffic infrastructure).

The importance of road intelligence was shown in the platooning test on the Beijing-Chongli Expressway (Yanchong Expressway Beijing Section) in December 2019 that has complex road conditions for more than 94% of the sections are tunnels and viaducts. In most cases, autonomous driving in the tunnel became difficult because the GNSS signal was bad. Moreover, it was the middle of winter and the outdoor temperature remained as low as -20°C, challenging a multitude of supporting equipment. Huawei has installed the 5G vehicle terminal T-Box with 5G+C-V2X technology on Foton Commercial Vehicles, and also provides roadside sensing terminal cameras, radars, road side unit, edge computing, C-V2X Server, etc. Through the C-V2X services for the entire road sections, the deficiencies in positioning, communication and perception in the tunnel can be compensated. Foton commercial vehicles completed a 14-km L4 platooning demonstration, including a 9.8-km continuous extra-long tunnel.

Billions of dollars flocked to the maturing self-driving commercial vehicle market in 2019. Vehicle intelligence is prioritized in foreign countries, while CVIS (Cooperative Vehicle Infrastructure System) prevails in China. There will be greater development space from 2020 on.

The report details technologies about automated driving of commercial vehicle, organizations, the status quo of the market as well as progress in platooning; nearly 50 suppliers' and automakers' investments, deployments, technical routes, products & solutions, business models, plans and goals, tests, collaborations, applied scenarios, etc.

Read the full report: https://www.reportlinker.com/p05877105/?utm_source=PRN

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