Autonomous Vehicles | Reaping the Benefits

Autonomous Vehicles | Reaping the Benefits

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • AI
  • Electric Vehicles
  • Emerging Technologies

Publication | Update: Sep 2020

The introduction of ADAS and active safety features equipped cars with abilities they did not have before, such as using sensors to perceive the external environment. For more than two decades, sensors have been used in applications like engine management; passive safety for closed-loop controls; or to react to events happening to the car, such as a hydroplaning wheel or a crash.

ADAS continually perceives and scans the external environment, which helps the vehicle not only to react to events but even anticipate problems. Sensors can widen and sharpen the driver’s view with features including surround view and night vision, and even prevent accidents through warnings such as lane-departure alerts and counter-act when necessary, such as applying an automated emergency brake.



Kambria research, and Pete Goldin of ITS Digest list the key benefits we can reap from autonomous vehicles:

Fewer Accidents – Self-driving cars have the potential in the future to reduce deaths and injuries from car crashes. Their cameras and sensors can detect and track people and vehicles from every angle of the car, detect possible collisions and steer the vehicle safely to avoid accidents. It is estimated that accidents will be reduced by 5% per year in the first decade of implementation alone and could go down as much as 0.15% by 2040.

According to the USDOT: "With 94 percent of fatal vehicle crashes attributable to human error, the potential of autonomous vehicle technologies to reduce deaths and injuries on our roads urges us to action."

The House Energy and Commerce Committee clarifies: "Self-driving cars are projected to reduce traffic deaths by 90%, saving 30,000 lives a year."

Decrease or Totally Eliminate Traffic Congestion – the future of autonomous vehicles is to have them communicate with each other, thereby reducing traffic congestion and car accidents even more! Driving to your office 10 – 15 miles away will take less than half the time it before self-driving cars were allowed in the streets.

Increase Highway Capacity – a recent Columbia University study showed that if there were 100% self-driving cars occupying one lane of a given highway, then vehicle capacity could be increased to as much as 12,000 cars per lane per hour driving at a constant speed of 75 mph. None of our current traffic technology could achieve this feat despite government expenditures reaching several hundred million dollars per annum.

Reduced Traffic Congestion - Reduced Travel Time and Transportation Costs – assuming that all self-driving cars will be hybrid or all-electric, they could save over 2.9 billion gallons of gasoline per year in the US alone! If electricity was also harvested from clean and renewable energy sources, then that would reduce fuel consumption even further.

"AVs may cut travel time by up to 40 percent, recover up to 80 billion hours lost to commuting and congestion, and reduce fuel consumption by up to 40 percent. These cost/time-saving benefits are expected to be worth about US$ 1.3 trillion in the country. Other potential cost-saving domains include reduced manpower — drivers and law enforcers," according to a report from KPMG.

The reduction in congestion will most likely result in a reduction of CO2 emissions as well. The Future of Driving report from Ohio University states: "Since software will drive the car, the modern vehicle can now be programmed to reduce emissions to the maximum extent possible. The transition to the new-age cars is expected to contribute to a 60% fall in emissions."

Lower Fuel Consumption - "AV technology can improve fuel economy, improving it by 4–10 percent by accelerating and decelerating more smoothly than a human driver. Further improvements could be had from reducing distance between vehicles and increasing roadway capacity. A platoon of closely spaced AVs that stops or slows down less often resembles a train, enabling lower peak speeds (improving fuel economy) but higher effective speeds (improving travel time). Over time, as the frequency of crashes is reduced, cars and trucks could be made much lighter. This would increase fuel economy even more," states Rand Corporation's Autonomous Vehicle Technology: A Guide for Policy Makers.

Enhanced Human Productivity –Productivity levels would increase and passengers would be able to accomplish more in the same amount of time.

Last Mile Services - "Autonomous vehicles are well-positioned to provide first/last-mile services to connect commuters to public transportation. Larger cities have the problem of providing adequate public transportation. Many lack the appropriate infrastructure to support the needs of their residents, a void that could partially be filled by self-driving cars. AVs could potentially supplement public transport, solving the first-mile-last-mile problem," according to a report from KPMG:

More Efficient Parking - Hunting for Car Parking Space Will be Eliminated – self-driving cars would also be able to communicate with parking lots to immediately locate good parking.

"AVs remove commuters' demands for street and lot parking. Some cities devote a third of their land to parking and AVs could free up significant real estate for other uses, from parks to residences to office space. For personal AVs, commuters may be dropped at a location and the vehicle would park itself away from the destination, where space is available. Cutting back on the land used for parking might even reduce real estate costs, " according to a report from KPMG

The Future of Driving report from Ohio University states that with autonomous taxis, "The waiting time for a cab will come down from the average five minutes today to just 36 seconds. The cost of a ride too will come down to just $ 0.5 per mile in a driverless car."

The Future of Driving report from Ohio University says a significant "impact of driverless cars is that such cars can be parked in 15% less space. Currently, cars need to be parked with enough space between them for the driver to exit after parking and enter when removing the car from the parking space. With self-driving cars, vehicles can be stacked right next to each other. Urban areas facing acute space shortage will gain from the transition to driverless cars."

Transportation Accessibility - Improved Mobility for Children, the Elderly, And the Disabled – there are three types of people who will have trouble driving cars; a) children, b) the elderly, and c) people with disabilities. Giving them the option to own self-driving cars will greatly increase their self-reliance and enable them to function more without needing further assistance. “The aging of the population converging with autonomous vehicles might close the coming mobility gap for an aging society,” said Joseph Coughlin, the director of the Massachusetts Institute for Technology AgeLab in Cambridge, quoted in an article in the New York Times.

Higher Speed Limits Increased Lane Capacity - because self-driving cars will be able to communicate with each other, predict collisions, and adjust the car’s speed and direction in a nanosecond, cars will be able to travel faster. Faster travel times could mean never being late for work, school or an important meeting ever again.

Research from the State Smart Transportation Initiative (SSTI) shows potential for autonomous vehicles could increase highway capacity by 100 percent and increase expressway travel speeds by more than 20 percent.

Autonomous Vehicle Technology: A Guide for Policy Makers, a report from Rand Corporation, states: "While AVs might lead to an increase in overall vehicle travel, they could also support higher vehicle throughput rates on existing roads. To begin with, the ability to constantly monitor surrounding traffic and respond with finely tuned braking and acceleration adjustments should enable AVs to travel safely at higher speeds and with reduced headway (space) between each vehicle. Research indicates that the platooning of AVs could increase lane capacity (vehicles per lane per hour) by up to 500 percent."

Lighter, More Versatile Cars – with the efficiency of self-driving cars, soon we won’t need to make cars from metals to reinforce them for safety in case of accident. This is because autonomous vehicles will virtually eliminate road accidents. Therefore, cars will be made from cheaper composite materials, and since they’ll be lighter, they will travel faster and further on a single charge.



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This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

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Direct contribution to GDP
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Forecasts of GDP growth: GDP = CN+IN+GS+NEX

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All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.


Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates


The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

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Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.


The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.