Automated Vehicles: The Leading Edge of Cyber — physical Systems

Since General Motors[1] and Ford emerged in the 1920s to denote the modern concept of the corporation,[2] there has been little fundamental change in automobile design that matched the first innovation. Through time, major changes included:

— The assembly line (1900s).

— First car electrical system, headlights (1910s).

— Pricing for the average consumer (1920s).

— The introduction of multiple product lines and colors (1930s).

— Synthetic rubber for tires (1940s).

— The use of mass marketing to drive sales, including television (1950s).

— The automatic transmission (1950s).

— The V-8 engine design became popular (1950s).

— Vertical integration via assemblers buying suppliers (1950s)

— Reducing vehicle weight to improve gas millage via new materials (1970’s).

— The application of statistical quality control methods (1980’s).

— Lean manufacturing (1980’s).[3]

— Modular Platforms by VW (2000’s).[4]

All the above made important contributions to productivity in both manufacturing and the incremental improvement in the average land speed (and comfort) of transportation within the United States. However, none matched, or came close, to the first burst of productivity that the automobile brought to the general economy in terms of freeing people (and business) from geographical constraints caused by horse power (slow ground speed), rail (limited connections), and inland water transportation (physical limitations).

In addition, there were secondary productivity effects from the first innovation. For example, in 1900 there were between 100,000 to 200,000 horses in New York City, causing a horse manure crisis.[5] An estimated 20,000 New Yorkers died each year from illnesses related to the pileup of manure and dead horses in the streets. The advent of the automobile and public transportation ended this public health problem.

Today, automotive manufacturing is at the leading edge of the cyber-physical systems revolution through rapidly advancing technologies such as collision avoidance and automated vehicles. Over 263 firms[6] are in the development and road testing stage for various products ranging from mapping software, algorithms, computers, radars, LIDARs, cameras, and other sensing technologies.

Even aside from these test products, the average car includes various electronics and dozens of Electronic Control Units (ECU), all needing chips. Many overlook that “almost a third of a vehicle’s weight is the wiring between all of its parts.”[7]

Though it is early to make market predictions, one research team “estimates that the global shared mobility market will reach $2.6 trillion annually by 2030.”[8] In a different study, IHS Automotive forecasts sales equal to 600,000 of automated vehicles by 2025 plus a 43% compounded annual growth rate in units sold between 2025 and 2035.[9] The growth rate estimates approach similar massive innovations such as the industrial and consumer products that caused surges in demand for electric power just before the Great Depression of the 1930s.

Across the business landscape there are few other opportunities to accelerate American economic growth and productivity as compared to the automotive industry powered by automated vehicles. And yet at the core of this technology rests Artificial Intelligence (A.I.) and Machine Learning (ML), and sensors, which all have many applications outside of the automotive industry. These include basic, everyday tasks such as classifying images or doing speech translation to entirely new concepts for enterprise computing. For 2016, “over 200 A.I. — focused companies have raised nearly $1.5 billion in funding, and equity deals to startups in AI increased 6x from roughly 70 in 2011 to almost 400 in 2015.”[10] Besides startups, companies such as Amazon, Google, and Facebook are building A.I. into vast global networks.[11] As a case-in-point, Google Sheets, which is web — based, now includes machine learning. It helps users to build charts.[12]

The driving force of these technologies will increase demand for specialized computing chips[13] to fuel expanding A.I. and ML. This occurs at a time when many traditional demand sources, such as desktop computing or smartphones, are declining in market growth or in some cases flat. These specialized chips are unique in that they use new materials and strive to pack even more transistors closer together. This is necessary to increase computational power per unit of area. Heat dissipation is also important. Without these technological developments, innovations like automated vehicles would not be possible.

Deep in the supply chain, there exist specialized tool makers that sell lithography equipment to fabricators such as Intel, Taiwan Semiconductor, and Samsung. The tool makers are critical to the future of all aspects involving A.I. and ML. Likewise, we see a need to put forth a new form of computer operating system capable of incorporating aspects of A.I. and ML. This is a new frontier worth tracking.

Carmine Senatore — Senior Associate, Vehicle Engineering — Exponent: Natick, MA. csenatore@exponent.com

www.exponent.com

E. W. Schuster — Industrialist — Edm. Wm 8, LLC: Nashua, NH. ew@schuster.us.com

schuster.us.com

References:

[1] Sloan, Jr., A.P., 1990. My Years with General Motors. New York: Crown Business.

[2] Drucker, P.F., 1946. Concept of the Corporation. New York: John Day.

[3] Womack, J.P., T. Jones, and D. Roos, 2007. The Machine that Changed the World. New York: Free Press.

[4] VW MQB Platform — https://en.wikipedia.org/wiki/Volkswagen_Group_MQB_platform

[5] Lee, J., 2008. When horses posed a public health hazard. The New York Times, Jun. 9.

[6] Steward, J., 2017. Mapped: the top 263 companies racing toward autonomous cares. Wired, May 10.

[7] Ray, T., 2016. Faster, smarter, better: the next chip revolution. Barron’s, Oct. 22.

[8] Power, D., 2016. Self-driving car market worth trillions by 2030? Readwrite, Jun. 8.

[9] Power, D., 2016. 76 million self-driving cares expected on world’s roads by 2035. Readwrite, Jun. 15.

[10] Guest, 2016. How machine learning can make humans better managers. VentureBeat, Nov. 27.

[11] Gallagher, D., 2017. How the big chip rally knocked leaders off their perches. The Wall Street Journal, Jun. 4.

[12] Gundrum, D., 2017. Visualize data instantly with machine learning in Google Sheets. The Official Google Blog, Jun. 1, 2017.

[13] Ray, T., 2017. Micron, up 170%, could move higher. but be wary. Barron’s, Mar. 25.