...
...
The Future of Enhanced Mobility | The 4 ACES - Mobilitys autonomous future

The Future of Enhanced Mobility | The 4 ACES - Mobilitys autonomous future

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


Publication | Update: Mar 2020
...

According to the Mckinsey perspective ‘The trends transforming mobility’s future’, the levels of disruption coming over the next dozen years are likely to exceed those of the previous 50 or more.

In this context, four trends - ACES: autonomous driving, connectivity, the electrification of vehicles, and shared mobility, are identified by Mckinsey.

The global revenues associated with AVs in urban areas could reach 1.6 trillion a year in 2030.

Companies are enhancing their systems for developing integrated platforms and promote cross-functional activities among its business fields, including semiconductors, sensors and motors.

Nearly every auto OEM and major supplier has an AV project in progress, aimed at reshaping the very nature of how people experience mobility. To better understand the size and scope of the AV opportunity, the McKinsey Center for Future Mobility modeled more than 40 transportation use cases across a global mix of urban and highway settings, and under a range of technological, economic, and other conditions.

                                    ...

Nearly one-third of the benefit would arise from the public sector’s redevelopment of unnecessary parking spaces into more productive commercial or residential property. For context, the amount of land taken up by car parking in Los Angeles is more than 17 million square meters—equivalent to nearly 1,400 soccer fields.

About 15 percent would accrue annually to workers in the form of more productive commuting time. Further, we anticipate a yearly benefit of about one-half of 1 percent (somewhat less than 4 billion) in the form of reduced environmental damage, since, for example, more efficiently utilized vehicles idle less than others do.

Finally, more than half of the benefits would stem from safer roadways and the avoidance of the millions of fatal and nonfatal accidents caused each year by human error. A comparable analysis of Germany found that by 2040, self-driving vehicles could save the country €1.2 billion a year through lower costs for hospital stays, rehabilitation, and medication alone.

Though not all the impact of the AV-driven future are as positive as saved lives. The insurance industry, for example, could face disruption if revenues from premiums shrink and new issues of liability arise; alcohol consumption could well increase as cars become more of a living space, energy consumption would rise as self-driving cars, despite their efficiency, tap new pools of demand; and, most worryingly for cities, revenues from vehicle taxes and licensing fees would decrease dramatically.

The improved capabilities of 5G make autonomous driving more feasible, and more trustworthy.

According to Noah Waldman of Here360, 5G technology presents opportunities for automated vehicles and connected mobility, to carry out critical communications for safer driving, support enhanced V2X, and connected mobility solutions.

 “5G will usher in the future of the automotive and telecommunications industries by enabling new in-car services, enhanced safety and autonomous driving,” said Edzard Overbeek, CEO of HERE360.

“We’re moving from being just a hardware provider to being a hardware, software, and experiences provider,” says Don Butler, head of Connected Vehicle and Services for Ford Motor Company. “The future is going to be different, and we are embracing that difference, and we’ll continue to be a part of people’s lives.”

Considering the 280 hours per year the average American spends behind the wheel, there’s real pressure to make that time productive, or at least entertaining, and it’s turning your car into an intensely personalized and customized extension of yourself.

The increased speed and capacity of 5G compared to other wireless standards increases the level of efficiency and safety that autonomous vehicles would be able to deliver to drivers and pedestrians alike. The increased bandwidth would allow connected cars to receive constant updates from other 5G-connected devices: a pedestrian's cell phone, other cars, traffic infrastructure, and more – giving the vehicles a clearer and more accurate picture of the what's around them and what awaits them down the road. The increased speed would allow them to communicate and react faster to everything the cars share a that road with.

For example, in live demonstrations performed in Turin, 5G connected cars were able to identify and react to pedestrians crossing in front of them, a connected bike riding beside them, and other cars that presented collision risks on straight ways and cross junctions. Additionally, 5G would enable “see-through” functions to help drivers avoid dangerous overtaking. Emergency services would be able to visualize an emergency situation using on-board cameras of surrounding vehicles, improving response times and providing situational awareness prior to arriving at the scene.

According to Asutosh Padhi senior partner in the McKinsey Chicago office and cofounder of the McKinsey Center for Future Mobility, there are still a couple challenges that remain. “The first one is object detection and categorization, which is the ability of a car, for example, to recognize a pedestrian: this is what it looks like if a pedestrian is pushing a stroller, if the pedestrian is carrying an umbrella, if the pedestrian is carrying a plant, when a pedestrian doesn’t look like a pedestrian, etc. And the second challenge is decision making. When there is human driving, there are a lot of subtle signals that drivers send to each other—right of way, etc.—and often if you’re an autonomous car, you can’t pick those up.”

In relation to testing and validation, being a concern for autonomous-driving-technology readiness Asutosh Padhi said. “There’s going to be a completely new paradigm that has to emerge for testing and validation in the world of autonomous vehicles. In this new world, it is less about driving millions of miles. If you drive millions of good miles, there is, in effect, no new learning that happens. It really is about looking at the millions of edge cases that you’d expect that do not usually happen on a more traditional and a more frequent basis. And it’s about teaching the car and the algorithms to recognize object detection as well as decision making in those edge cases. And what it essentially implies is that you can’t do this through physical validation and testing, because you can’t have a car drive a million miles. You have to borrow techniques that are used around software-based simulation from other industries like gaming as a way to be able to complete the necessary level of validation.”

When it comes to consider what type of autonomous-driving use cases we expect to see in the future, Philipp Kampshoff, a partner in McKinsey’s Houston office and coleads the McKinsey Center for Future Mobility contests: Let me tie all these elements together to give you a couple of examples of these use cases. If we talk about transport of people in an urban environment [with AVs] owned by a professional company instead of a private person, then we are talking about the use case of robo-taxis. One question that we are always being asked is, “Is there going to be an Airbnb model of autonomous cars in the future, where people own autonomous vehicles, the autonomous vehicle takes them to work, and then they put it into the mobility-as-a-service system to work for them?” The math suggests that it is difficult for this vehicle to compete with a professional-fleet provider. The reason is that the professional-fleet provider, just because of economies of scale and purchasing power of not buying just one car but many, will get a different purchase price. They will be much more professional in terms of servicing and maintaining the fleet. And likely, the utilization of the car is going to be higher, too. So they will be able to operate at a different cost point in comparison to a private person who owns an AV and brings that into the system to work as a robo-taxi. Most likely we do not see the Airbnb model for robo-taxis, but professional-fleet providers providing that.”

 

 

Framed Content Aggregator - Publisher | Sponsor
...
APU INSIGHTS
...Industry: Automotive
SKU code : 76DC66E7-CA97-B1CF-AC41-8F286B9038F9
Delivery Format:
HTML ...
Immediate Delivery
...Access Rights | Content Availability:
...

...

Objectives and Study Scope

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.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology

...

We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.

Research Portfolio Sources:

  • BBC Monitoring

  • BMI Research: Company Reports, Industry Reports, Special Reports, Industry Forecast Scenario

  • CIMB: Company Reports, Daily Market News, Economic Reports, Industry Reports, Strategy Reports, and Yearbooks

  • Dun & Bradstreet: Country Reports, Country Riskline Reports, Economic Indicators 5yr Forecast, and Industry Reports

  • EMIS: EMIS Insight and EMIS Dealwatch

  • Enerdata: Energy Data Set, Energy Market Report, Energy Prices, LNG Trade Data and World Refineries Data

  • Euromoney: China Law and Practice, Emerging Markets, International Tax Review, Latin Finance, Managing Intellectual Property, Petroleum Economist, Project Finance, and Euromoney Magazine

  • Euromonitor International: Industry Capsules, Local Company Profiles, Sector Capsules

  • Fitch Ratings: Criteria Reports, Outlook Report, Presale Report, Press Releases, Special Reports, Transition Default Study Report

  • FocusEconomics: Consensus Forecast Country Reports

  • Ken Research: Industry Reports, Regional Industry Reports and Global Industry Reports

  • MarketLine: Company Profiles and Industry Profiles

  • OECD: Economic Outlook, Economic Surveys, Energy Prices and Taxes, Main Economic Indicators, Main Science and Technology Indicators, National Accounts, Quarterly International Trade Statistics

  • Oxford Economics: Global Industry Forecasts, Country Economic Forecasts, Industry Forecast Data, and Monthly Industry Briefings

  • Progressive Digital Media: Industry Snapshots, News, Company Profiles, Energy Business Review

  • Project Syndicate: News Commentary

  • Technavio: Global Market Assessment Reports, Regional Market Assessment Reports, and Market Assessment Country Reports

  • The Economist Intelligence Unit: Country Summaries, Industry Briefings, Industry Reports and Industry Statistics

Global Business Reviews, Research Papers, Commentary & Strategy Reports

  • World Bank

  • World Trade Organization

  • The Financial Times

  • The Wall Street Journal

  • The Wall Street Transcript

  • Bloomberg

  • Standard & Poor’s Industry Surveys

  • Thomson Research

  • Thomson Street Events

  • Reuter 3000 Xtra

  • OneSource Business

  • Hoover’s

  • MGI

  • LSE

  • MIT

  • ERA

  • BBVA

  • IDC

  • IdExec

  • Moody’s

  • Factiva

  • Forrester Research

  • Computer Economics

  • Voice and Data

  • SIA / SSIR

  • Kiplinger Forecasts

  • Dialog PRO

  • LexisNexis

  • ISI Emerging Markets

  • McKinsey

  • Deloitte

  • Oliver Wyman

  • Faulkner Information Services

  • Accenture

  • Ipsos

  • Mintel

  • Statista

  • Bureau van Dijk’s Amadeus

  • EY

  • PwC

  • Berg Insight

  • ABI research

  • Pyramid Research

  • Gartner Group

  • Juniper Research

  • MarketsandMarkets

  • GSA

  • Frost and Sullivan Analysis

  • McKinsey Global Institute

  • European Mobile and Mobility Alliance

  • Open Europe

M&A and Risk Management | Regulation

  • Thomson Mergers & Acquisitions

  • MergerStat

  • Profound

  • DDAR

  • ISS Corporate Governance

  • BoardEx

  • Board Analyst

  • Securities Mosaic

  • Varonis

  • International Tax and Business Guides

  • CoreCompensation

  • CCH Research Network

...
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:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

CLICK BELOW TO LEARN MORE
...

Market Quantification
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.

Revenues

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.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

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.

CLICK BELOW TO LEARN MORE
...

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.

Elasticities
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
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.

CLICK BELOW TO LEARN MORE