...
...

Autonomous Vehicles | The 5 Levels of Autonomy

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


Publication | Update: Sep 2020

 

...

Level 0

Even without autonomous capabilities, Level 0 marks an important milestone, because it indicates the presence of sensors and electronics to improve driver performance. Informational ADAS provides surround-view and rear-view cameras, while machine vision can help identify dangerous situations and warn drivers with features such as a front camera with lane-departure warning, ultrasonic park assistance, and blind-spot detection radar.

System Topology

According to Hannes Estl, sector general manager of ADAS, Texas Instruments, at Level 0, all systems can be independent and perform their tasks without significant data transfer between them. Slow-speed data interfaces like Controller Area Network (CAN) are usually sufficient. Video for informational ADAS is only displayed for the driver but not used for machine vision.

Processing

All data processing and algorithms for machine vision are performed on the edge (at the sensor’s location) or in a dedicated electronic control unit (ECU) with application processors selected for the necessary performance.

Safety

Given that drivers must observe their environment and stay in control of the car at all times, the loss of ADAS functionality would not lead to immediate danger. The systems’ erratic behavior and false alarms should still be avoided, as they can distract drivers and lead to undesired reactions.

Synergy

Because the systems operate independently, no real synergy occurs between them.

1:  Driver Assistance: 

At this driver-assistance level most functions are still controlled by the driver, but a specific function (like steering or accelerating) can be done automatically by the car.  Many new cars now come equipped with sensors and advanced electronics that give drivers audible warnings when they’re crossing lanes or following too closely.

System Topology

The various sensor systems can still be independent, with completely local processing and decision-making, but they will need to connect to other ECUs in the car in order to issue commands for acceleration and steering. The amount of data is still small and does not require more than CAN interfaces, for example. One sensor type is often sufficient to perform a given Level 1 function.

Processing

On the edge with little to no influence from other systems. Algorithms become more complex because they control certain driving functions.

Safety

Safety requirements increase because the ADAS can perform driving operations without the driver initiating or controlling them. Incorrect operation may now lead to dangerous situations or accidents.

Synergy

No significant difference from Level 0: systems operate independently without sharing information or data.

 

 

2: Partly Automated Vehicle

 

Semi-autonomous driving assistance systems, such as the Steering and Lane Control Assistant including Traffic Jam Assistant, make daily driving much easier. They can brake automatically, accelerate and, unlike level 1, take over steering. at least one driver assistance system of both steering and acceleration/ deceleration using information about the driving environment is automated, like cruise control and lane-centering. It means that the "driver is disengaged from physically operating the vehicle by having his or her hands off the steering wheel AND foot off pedal at the same time. But drivers are always ready to take the control. 

System Topology

A coordinated effort between different sensors and ECUs makes data connections between all ECUs necessary, as well as a central entity that makes the final decision and issues control commands. The data shared is typically object data and does not need high-bandwidth interfaces.

Processing

Image processing and object identification can still occur at the sensor’s location, but a single entity combines the data from the different sources and makes decisions. Optimized processor technologies typically handle the different processing tasks.

Safety

Safety requirements at the sensor modules remain the same or can be slightly lower than in the previous level as driving decisions or control of driving functions are not made in the sensor module. The central decision-making unit has higher safety requirements because of the control it has over the car. In fault or unexpected conditions, the system is designed to transfer control back to the driver immediately.

Synergy

Using data from different sensors allows more robust decision-making and helps overcome the shortcomings of individual sensor types. As long as processing occurs at the sensor location, only a limited set of data is available at the central decision-making unit.

 

3: Highly Automated Vehicle

 

In this step, we are still in the game. Anytime needed, we can intervene for driving. We still need to monitor the car and way of driving. This step give drivers more freedom to completely turn their attention away from the road under certain conditions. Drivers will be able to hand over complete control to the car system. With highly automated systems, the car will be able to drive autonomously over long distances in certain traffic situations, such as on motorways. The driver, however, must be able to take over control within a few seconds, such as at road construction sites.

System Topology

With the number and types of sensors increasing, a different system topology becomes viable. Sensor modules without local processing are smaller, require less power and cost less. Because the sensor data is not processed at the sensor location, high-bandwidth, low-latency interfaces become necessary.

Processing

With raw data coming from sensors, the central ECU must have sufficient performance and optimized processors to handle image processing, object identification and classification, sensor fusion, and decision-making.

Safety

Requirements increase again at this level with so much of the processing moving into the central ECU, as well as the fact that the system can’t immediately return control to the driver. Some sensor or processing functions need to maintain functionality even under fault conditions to safely hand control back to the driver in a defined time frame.

Synergy

Most or all sensor data goes a central location without data loss or alteration from pre-processing in the sensor modules, enabling a maximum of synergies during the process of sensor data fusion.

 

4: Fully Automated Vehicle


Human driver can still request control, and the car still has a cockpit. In level 4, the car can handle the majority of driving situations independently. The technology in step 4 is developed to the point that a car can handle highly complex urban driving situations, such as the sudden appearance of construction sites, without any driver intervention. The driver, however, must remain fit to drive and capable of taking over control if needed, yet the driver would be able to sleep temporarily. If the driver ignores a warning alarm, the car has the authority to move into safe conditions, for example by pulling over. While level 4 still requires the presence of a driver, cars won’t need drivers at all in the next, final level of autonomous driving.

 

5: Full Automation- No need for a driver


Unlike levels 3 and 4, the “Full Automation” of level 5 is where true autonomous driving becomes a reality: Drivers don’t need to be fit to drive and don’t even need to have a license. The car performs any and all driving tasks – there isn’t even a cockpit. Everyone in the car is a passenger. Cars at this level will clearly need to meet stringent safety demands, and will only drive at relatively low speeds within populated areas. They are also able to drive on highways but initially, they will only be used in defined areas of city centres.

This refers to a fully-autonomous system that expects the vehicle's performance to equal that of a human driver, in every driving scenario—including extreme environments like dirt roads that are unlikely to be navigated by driverless vehicles in the near future. 

System Topology

In addition to the sensors needed to perceive its immediate surroundings, Levels 4 and 5 require a way to determine the car’s current location with precision GPS, along a map to identify the best route and the ability to connect to other sources of real-time information through 4G or 5G, WiFi, and/or V2X, a car’s connection to the outside world. Most sensors will focus on high-resolution data acquisition and/or the processing of that data, while mission planning and decision-making occur in a central location. Mission-critical functions must operate even under fault conditions.

Processing

In addition to handling all driving functions, the vehicle now handles route planning and execution as well.

Safety

Without a driver to take over (there might not even be a steering wheel), the vehicle must safely operate under all road and weather conditions as well as during faults in the system itself. Fail-safe is not sufficient anymore; reliable operation during a failure is needed for mission-critical functions.

Synergy

In addition to the data from its own sensors, the system also connects to external data from a GPS, various cloud-based services through 4G or 5G, or other vehicles and infrastructure (WiFi/V2X).

Framed Content - Publisher | Sponsor:
...
APU Insights
...Industry: Automotive
SKU code : CF9F8526-CF76-D5CE-F27E-E408A0828A2A
Delivery Format:
HTML ...
Immediate Delivery
...Access Rights | Content Availability:
  • The Big Picture - Intelligence Center
  • The Big Picture - Platform

...

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