ABSTRACT

KEYWORDS: Automated Vehicle, Connected Vehicle, Electric Vehicle, Technology Adoption APAC-21-134 i. Active Safety Systems: vehicles equipped with multiple Active Safety Systems that provide driver support (such as Auto Emergency Braking also known as AEB, lane keeping assistance, adaptive cruise control) ii. Conditional (L3) Automated driving: vehicles capable with no more than a fallback ready driver for at least some Operational Design Domains (ODDs) such as motorway segments iii. Highly Automated Driving – early ODDs: covers expected early Operational Design Domains (ODDs) such as some full door to door urban journeys and urban and higher volume rural motorways. iv. Highly Automated Driving – broader ODDs; covers an expected broader range of ODDs, extending to more urban and rural roads and conditions. Cooperative-ITS equipped: vehicles equipped with standards-based interoperable Cooperative ITS systems v. Connectivity to cloud: vehicles equipped with cloud-based communication that may be used for services such as live traffic information, over-the-air updates, automated crash notification, concierge and booking services, etc vi. Battery Electric Vehicles: Electric Vehicles for which a battery is the primary energy source (includes vehicles with range extenders and PHEVs) vii. For hire cars with driver: Cars available for transport use other than by owner / lessee, driver provided with vehicle (e.g. taxi, rideshare)

A common method of defining Automated Vehicle (AV) technologies that are the subject of forecasts is to draw upon the Society of Automotive Engineers (SAE) levels of automation as set out in J3016 (e.g. L2, L3, L4). While this has the advantage of alignment with commonly used language in industry, the breadth of each level (and particularly Level 4) causes challenges in both producing forecasts and interpreting the impacts resulting from technology adoption.

For this project each technology to be forecast therefore used a more specific description that sought to align with expected product releases into vehicles.

Australian and New Zealand vehicle fleets may be at the start of a period of significant change due to the emergence of Automated, Connected and Electric Vehicles as well as new models of vehicle ownership and use. To inform its research programs and decision-marking by member transport agencies, Austroads commissioned forecasts to explore the likely adoption of certain vehicle technologies within the vehicle fleets of 2030 in Australia and New Zealand. These forecasts have since undergone the first of intended periodic reviews and been extended out to 2031. The forecasts found that the adoption of each of the technologies would take place over an extended period and that different technologies were at different stages of the adoption process. The forecasts are purely forecasts, not combined with consideration of impacts of technology adoption. This approach assisted a purity of focus on what appears likely to occur, rather than introducing any bias as to what might be desirable. Technologies such as Active Safety Systems are well progressed in adoption and although Electric Vehicle technologies are not as well progressed, they appear to be following a clear adoption pattern. Highly Automated Driving is anticipated to feature in only a small number of vehicles within the forecast period (e.g. 20302031). The project also explored differences between Australia and New Zealand, passenger vehicles and commercial vehicles, and cities and regional areas.

Forecast viii was included in the original 2030 forecasts but discontinued for the 2031 update due to both a shift in focus and data challenges associated with pandemic-related travel pattern changes.

It is useful to note that all the technologies defined for forecasting are technologies fitted to new vehicles at time of manufacture or first purchase. This series of forecasts does not seek to cover retrofitting of technologies to vehicles during their life-cycle.

2.2 Modelling Technology Adoption in Newly Purchased Vehicles

Although the term modelling is used in the title of this section, no specific model was used. Instead, a technology adoption life-cycle was used, informed by the diffusion of innovation theory developed by E.M. Rogers in 1962 [2] and which remained relevant for subsequent adoptions of technology. To the extent a modelling approach was then adopted, it is best described as curve fitting (of the technology adoption life-cycle) within approximate points that reflected the available evidence bases (see Section 2.4).

The technology adoption life-cycle incorporates an appreciation of human behavior by reflecting that there are stages by which any person adopts an innovation:

• awareness of the need for an innovation;

• decision to adopt (or reject) the innovation;

• initial use of the innovation to test it; and

• continued use of the innovation.

If an innovation proceeds successfully through adoption, the diffusion occurs over time as individuals progressively adopt the innovation. The balance between perceived benefits and costs of a technology influences the pace at which such progression through the adoption stages occurs. For vehicle technologies, analysis of prior technology adoptions (e.g. ABS, ECC, AEB) identified that this rate of progress is typically slower than for consumer electronics. This finding was consistent with BITRE’s 2021 analysis of a wide range of vehicle technology adoptions [1].

As noted earlier, the technology adoption lifecycle is not a specific model but rather acts as a clarifying framework for the modelling of technology adoption. This framework sets the task for the later analysis of the evidence base for each technology as needing to inform:

• A series of estimates (medium, rapid, slow) for when each technology will first become available on Australian passenger vehicles; and

• A series of estimates for how quickly adoption will then progress through each relevant subsequent phase of adoption (e.g. from innovators to early adopters, from early adopters to early majority, etc).

There is no guarantee that any innovation will receive widespread adoption, as there is a need for a clear relative advantage of the innovation to the alternative choice or course of action. Many of the technologies assessed in these forecasts are at the early stages of adoption or prior to adoption. For each forecast, an assumption has been made that they will progress through the stages of adoption, therefore the question in each case is as to the rate of progressive adoption. The progression is not necessarily rapid or one that goes through to full penetration; only that penetration will increase through to 2030 rather than fall away. This assumption is appropriate as technologies for which there was insufficient confidence of increasing penetration through to 2030 were excluded from the forecasts.

2.3 Modelling Fleet Penetration of Newly Adopted Technologies

The technology adoption life-cycle framework (Section 2.2) when combined with the analysis of the evidence base (Section 2.4) forecasts how a specific technology penetrates sales of new vehicles. A fleet penetration model was developed to provide a detailed basis for the estimation of how sales in a certain year translate into fleet penetration in a future year. The primary data source used for the development of this model was the Australian Bureau of Statistics (ABS) series 9309.0 - Motor Vehicle Census [3].

ABS Motor Vehicle Census microdata was extracted for each of the available years of 2013, 2014, 2015, 2016, 2017 and 2018. The pattern was then established for vehicles leaving the fleet, providing an attrition rate by age of vehicle. The primary attrition rate function was developed for passenger vehicles, Separate attrition rates were calculated for other vehicle types to inform later steps of the process (e.g. Section 2.5) as the attrition patterns differ between vehicle types.

The next step in the model development was validation of the developed attrition function with aggregate fleet level attrition provided by the ABS. This was followed by analyzing trend growth over time in the fleet. A function was also added to the model to reflect that peak number of vehicles from any year of manufacture is not reached for several years (e.g. the highest number of 2016 manufactured vehicles does not occur until 2018 due to delays in shipping, sales and registration).

sales penetration estimates to fleet penetration forecasts. Alternative methods such as assuming turnover based on average fleet age (~10 years) introduce unnecessary levels of error given that the data is not available to provide this more reliable approach.

The original Future Vehicles 2030 forecasts [4] were developed prior to the COVID-19 pandemic, which has had significant effects on the Australian, New Zealand and international vehicle markets.

The Future Vehicles 2031 update [5] assessed the impact of changes to new passenger vehicle sales over the course of the pandemic. It found that the reduction over the first 12-months of the pandemic was equivalent to a loss of between one- and two-months’ worth of sales. The impact of semiconductor shortages was also considered, however only limited evidence was available at that time for the impacts of shortages (merely evidence that there were shortages).

2.4 Building and Analyzing Evidence Bases for Each Technology

A transparent evidence base was established for each technology to be forecast. For technologies already available, the desired evidence was for the progress rate of adoption of the technology and current levels of sales penetration. For technologies not yet available, evidence was sought for:

• Credible statements as to the expected timing of first availability of the technology; and

• The progress rate of adoption for the closest reasonable similar technologies. Forecasts produced by others were considered as a cross-check to forecasts produced from this evidence base. Other than BITRE’s 2019 Electric Vehicle sales forecast [5], they were not used as a primary evidence source.

The evidence base for each forecast is set out in the original published report [4] and any updated evidence in the update report [5].

While space limitations prevent covering each of these evidence bases in full here, it is warranted to briefly explore the evidence base used for Highly Automated Driving. Both the original forecasts [4] and the update [5] relied upon statements direct from manufacturers, with consideration given to whether those statements still reflected likely time of technology availability. This additional consideration was critical given a repeated pattern slippage in announced dates, helpfully tracked by an analyst at the US based Center for Automotive Research [7].

Figure 2 shows that although the largest single time-segment of vehicles in the future fleet will be recent vehicles (e.g. last 5 years), these still make up less than one-third of the fleet. This fidelity within the fleet penetration model allows for confidence in translating technology

At time of developing the original 2030 forecasts, this diligence on current credibility of evidence was essential to producing meaningful forecasts for Highly Automated Driving. This resulted in the production of forecasts that substantially differed from other contemporary efforts to the extent that a specific appendix was included to explore and explain the difference. Fortunately, industry forecasts for Highly Automated Driving have been updated in the meantime and as of 2021 have less optimism bias than previously. This can for example be seen in comparing the 2017-2021 forecasts of the UK Connected Places Catapult [8] and [9] respectively. To their credit, the Catapult continues to make available their previous forecast for comparison, a practice that is far from universal.

While Austroads forecasts were revised between the original version developed during 2019 and 2020 [4] and the 2021 update [5], the changes were relatively small. The Austroads forecasts also transparently show and discuss the changes made, as it was considered important to share how forecasts change over time in response to changing evidence.

For each of the produced forecasts, a slow, medium and rapid scenario were modelled. While the medium forecast seeks to approximate a median forecast within the evidence base, the available evidence bases are too small to put definitive percentiles to the slow and rapid forecasts, although they approximate 15th and 85th percentile forecasts in their intent.

2.5 Developing Descriptive Commentary for Uptake in Other Vehicle Types

The forecasts each addressed uptake of technologies for new Australian passenger vehicles, encompassing cars and SUVs. Descriptive comparisons were provided for:

• New Zealand compared to Australia;

• Light and heavy commercial vehicles relative to passenger vehicles; and

• Rural and remote areas compared to major urban centers.

In each case, data was sought to provide a comparison point from which descriptive inferences could be made. Due to limitations of available data, this was often a comparison of relative vehicle ages, however fitment of current market technologies was included where available.

2.6 Consideration of Alternative Methods

As this is a fast-moving field, there would be only limited value in including in this paper a literature review that explored other forecasts. Other forecasts were reviewed as part of the evidence base in both the original [4] and update editions [5]. The most frequent conclusion would be that the forecasts may have been relevant at a point in time but had become outdated – indeed this was already frequently the case for prior forecasts reviewed in the original edition.

What is more meaningful is to consider whether other forecasts offer a superior alternative method to that used here.

The method used here is not unique, and in many cases drew upon work by others, for example:

• There is strong alignment to the approach of BITRE [1] with respect to how the fleet penetration model was developed and utilized; and

• The use of vehicle feature availability by ANCAP [10](e.g. standard, optional, only some models) was adapted for use in technologies already in deployment. Forecasting methods used by others were not always disclosed, at least at the detail level, for example where the publication took the form of whitepapers or similar by large international firms.

Where differences were clear, a significant explanation of differences comes about in cases where a much longer time horizon was used. The methods used here are most suitable for near-term forecasts and would require a different approach to extend beyond the 10-year horizon used. Available approaches for this include:

• A form of meta-analysis of multiple forecasts by others, e.g. as used by BITRE [1] and other work by Austroads [11] where adoption is forecast out to 2050 – 2070

• Construction of a model where the model functions are established by extensive analysis of driving factors for the progress so far in the deployment of the technology, e.g. BITRE’s analysis of international market uptake for Electric Vehicles [6]

• Construction of a model that seeks to establish explanatory variables ahead of any (significant) uptake, e.g. Bansal and Kockelman’s focus on willingness to pay for Automated Vehicle technology [12]

These alternative methods were not adopted for this work, as either their value becomes most relevant for a longer forecast horizon or their use would have required a substantial increase in project budget, without sufficient justification in the form of improved forecast outputs.

3. Forecast Technology Adoption

The sections below provide a selection of the forecast outputs. The results provided are mostly from the forecast update [5] as the updated forecasts effectively supersede the earlier work [4].

3.1 Active Safety Systems

This forecast covers vehicles equipped with multiple Active Safety Systems that provide driver support (such as Auto Emergency Braking, lane keeping assistance, and adaptive cruise control).

The forecast for Active Safety Systems anticipated that uptake would continue a rapid growth phase through (at least) the forecast period.

A check of updated data for the forecast update [5] identified that many of the highest selling passenger vehicle models have standard fitment of Active Safety Systems on most or all models in the range. Indeed, the uptake of Active Safety Systems (LKA and ACC) appeared to be tracking well ahead of the forecast. The original forecast [4] estimated current year (2021) sales penetration of 31% in the medium scenario (and 42% in the rapid scenario), whereas implied current year uptake based on analysis of top selling models was closer to 70%.

Consultation with industry stakeholders indicated that it was not yet clear that this observed pattern represented a sustained acceleration in uptake.

An update was therefore made so that the rapid forecast for Active Safety Systems reflects the observation that current uptake may be running significantly ahead of forecast levels. This updated forecast features in Figure 4. For the medium and slow forecasts, no such adjustment was yet made, with a recommendation that the next review period re-check to validate whether the apparent acceleration in uptake has been sustained.

3.2 Conditional and Highly Automated Driving

This set of forecasts covers three types of Conditional Automation and Highly Automated Driving:

• Conditional Automation such as Traffic Jam Pilot or Motorway Pilot (at a minimum of SAE Level 3)

• Highly Automated Driving (minimum SAE Level 4) –early ODDs: covers expected early Operational Design Domains (ODDs) such as some full door to door urban journeys and urban and higher volume rural motorways.

• Highly Automated Driving – broader ODDs covers an expected broader range of ODDs, extending to more urban and rural roads and conditions.

Figure 5 below provides the vehicle sales forecast for automation features in the medium scenario. The sales forecast has been shown as the forecast penetration is at such low levels to be difficult to observe at all if the fleet forecast were shown. Indeed, even in this sales forecast, the categories of Conditional Automation and Highly Automated Driving are small enough to be difficult to distinguish.

This apparently slow progress into sales occurs despite even the medium forecast scenario using adoption progress estimates that are very much at the faster end of those seen for previous automotive technologies. This highlights that while the timing of first availability of technology is important and has a number of implications, it is likely to be some years beyond that before a technology is particularly prevalent.

3.3

Embedded Mobile Data Connectivity

This forecast covers vehicles where the mobile data connectivity is embedded into the vehicle, such that there is no reliance on a tethered smartphone or similar.

This was a challenging area to forecast as almost all available evidence was for international and not local markets (Australia and New Zealand). This evidence gap was combined with an indication through consultation that the local markets may not be following the international pattern, but with limited quantification available of the difference. To address this tension, the forecasts included in the original Future Vehicles 2030 edition used a very broad forecast range between scenarios, particularly given that this technology is well into adoption phases.

For the 2031 update, some additional analysis was able to be performed to provide a partial validation of the previously made forecast. The forecast horizon was extended from 2030 to 2031 but otherwise no change was made. The wide spread of scenarios was retained as some uncertainty remains, albeit with some reassurance that the forecasts appear appropriate.

3.4 Electric Vehicles

This forecast covers Electric Vehicles (EVs) in which a battery is the primary energy source. This includes vehicles with range extenders and Plug-in Hybrids (PHEVs) but excludes milder forms of hybrid technology and hydrogen fuelcell vehicles.

As noted in Section 2.4, the evidence base for this forecast was centered on work by BITRE [6]. Sales of Electric Vehicles through 2020 tracked closely to these forecasts, although more recently released figures for 2021 suggest a potential acceleration towards the rapid adoption scenario.

This forecast is the one that leads to the greatest discussion of government policy and the impact on these forecasts. There is good evidence to confirm that government policy (such as EV subsidies) makes a difference to adoption, e.g. analysis of international market adoption factors in [6]. There have also been recent announcements by some Australian state governments of targets for EV adoption that would sit slightly above (but close to) the rapid forecasts here.

In keeping with the forecasts being pure forecasts (see Section 1), the forecasting approach adopts an assumption of continuation of government action. This does not mean no further government action, but rather a continuation of the current trend of government action. With respect to Australia, this was explicitly assumed to mean “no significant new Australia-wide incentives for Electric Vehicles”. If there were to be a change to the pattern of government action to support EVs, such as through a change of government, then this would be factored into a future forecast update.

3.5

Differences between Australia and New Zealand

The base forecasts for both the original [4] and update [5] editions were for the Australian passenger vehicle fleet. New Zealand is a member of Austroads, and therefore a part of the project was to consider how uptake may be different in New Zealand.

Imports of used vehicles play a significant role in New Zealand; in recent years around 50% of the additions to the light vehicle fleet have been used vehicles [13]. Although the import of used vehicles has led to a higher average fleet age in New Zealand, it can also be a means by which technologies are introduced, such as the import of used Electric Vehicles.

It was therefore appropriate to consider differences on a technology-by-technology basis, for example:

• Active Safety Systems are likely to see slower uptake in New Zealand than Australia. Although used imports offer a potential adoption channel, the adoption rate in these used imports of a certain age may not be higher than for Australian new vehicles of equivalent age. Although some elements such as AEB may be prevalent in and possibly even mandated for used imports, the Active Safety System forecast covers not AEB but rather active Lane Keeping Assist (LKA) and Adaptive Cruise Control (ACC).

• Highly Automated Driving is likely to have slower uptake than Australia, with limited initial adoption through used import vehicles.

Although this may change over time, this is unlikely within the forecast period.

• Electric Vehicles are likely to see more rapid uptake than Australia as data is suggestive of buyers choosing used imports as an affordable entry into Electric Vehicles and the availability of sufficient supply from overseas.

3.6 Differences between Major Cities and Rural Areas

Although this was an area of interest to Austroads members, only limited conclusions could be drawn as no specific data was available for the uptake of vehicle technologies into new vehicles by different geographical regions. What was possible to explore using available data was the relative difference in the ages of vehicles between different geographic areas. Figure 8 below shows that the newest vehicles are a larger proportion of the vehicle fleet in major cities than in regional and remote areas.

3.7 Differences for Light Commercial Vehicles

The base forecast covers passenger vehicles, including cars and SUVs. Models such as the Toyota Hilux, Ford Ranger and Mitsubishi Triton are popular vehicles in both Australia and New Zealand but fall instead within the light commercial vehicle category.

Figure 9 shows that the highest fitment of key Active Safety System technologies in 2019 in Australia was on cars, followed by SUVs, then utilities (utes or light “trucks”) and vans. These figures are subject to some variability, as there is much less model diversity within utilities and vans than for cars and SUVs. This means that fitment to a key model (e.g. Toyota Hilux and HiAce) can lead to significant differences in results, as seen here between Adaptive Cruise Control and Lane Keep Assistance, with the latter becoming a standard fitment on some key models.

In general, however, the slower uptake of technologies in light commercial vehicles (utilities and vans) relative to passenger vehicles (cars and SUVs) is anticipated to continue, albeit in a manner more subject to decisions made for key vehicle makes and models.

3.8 Differences for Heavy Commercial Vehicles

Heavy vehicles represent 3% of registered vehicles in Australia, however, they are vital to the transport task and account for just over 8% of total vehicle kilometers travelled on public roads [14]. Heavy vehicles play a similarly important role in New Zealand, making up about 4.5% of the fleet but around 8% of vehicle kilometers travelled.

Uptake of Active Safety Systems on heavy vehicles lags uptake on passenger vehicles and light commercial vehicles. Looking at the top car models sold in Australia, 77% of sales are for models with standard Auto Emergency Braking (AEB). Across heavy vehicles this figure is only 6%, of which articulated trucks (at 23% of new sales) are the best equipped sub-fleet [14].

Subsequent to the publication of the original report, a mandate for AEB has been implemented in Australia, phasing in between 2023 and 2025. This mandate will increase fitment of this technology but does not change the overall pattern for lagging technology on heavy vehicles relative to light vehicles.

Nevertheless, while uptake of these early forms of automation appears slower in heavy vehicles than passenger vehicles, the potential benefits for heavy vehicles from more advanced automation technologies such as various forms of platooning may exceed those for passenger vehicles and encourage more rapid adoption.

4. Conclusion

Australian and New Zealand vehicle fleets may be at the start of a period of change due to the emergence of Automated, Connected and Electric Vehicles as well as new models of vehicle ownership and use.

The forecasts commissioned by Austroads to explore the likely adoption of certain vehicle technologies within the vehicle fleets of 2030 in Australia and New Zealand have proven fit for purpose to inform its research programs and decision-making by member transport agencies. In a fast-moving field, even the best forecasts can become outdated as the situation develops. To address this, the forecasts have undergone the first of intended periodic reviews and been extended out to 2031. While many of the forecasts were updated in this process due to the availability of updated evidence artefacts, the overall approach to forecasting remained sound and the extent of change within the updated forecasts was small.

The forecasts confirmed that the adoption of each of the technologies would take place over an extended period and that different technologies were at different stages of the adoption process. The methodology adopted for forecasting catered well for both these factors.

Technologies such as Active Safety Systems are well progressed in adoption and have already passed 30% of Australian new passenger vehicle sales and 10% penetration into the Australian passenger vehicle fleet. Electric Vehicle adoption is less well progressed in Australia but appear to be following a clear pattern and the differences to other advanced economies can be explained by the relatively smaller adoption incentives by Australian governments.

Highly Automated Driving is anticipated to feature in only a small number of vehicles within the forecast period (e.g. 2030-2031). Although this forecast differs from some previous estimates, many comparative forecasts have

References

[1] Australian Government Bureau of Transport and Regional Economics (BITRE), Forecasting uptake of driver assistance technologies in Australia, Research Report 153, 2021

[2] W La Morte, Behavioral Change Models: Diffusion of Innovation Theory, 2019

[3] Australian Bureau of Statistics (ABS), Series 9309.0 - Motor Vehicle Census, 2019

[4] Austroads, Future Vehicles 2030, Report AP-R623-20, 2020

[5] 2019Austroads, Future Vehicles Forecasts Update 2031, Addendum to Future Vehicles 2030, Report AP-R654-21, 2021

[6] Australian Government Bureau of Transport and Regional Economics (BITRE), Electric Vehicle Uptake: Modelling a Global Phenomenon, Research Report 151,

[7] E-P. Dennis, Announced Deployment Timeline, Center for Automotive Research, 2019-2022 (ongoing updates)

[8] Connected Places Catapult, Market Forecast for Connected and Autonomous Vehicles, 2021 been updated over recent years to (significantly) reduce that previous optimism.

The project also explored differences between Australia and New Zealand, passenger vehicles and commercial vehicles and cities and regional areas.

To continue to maintain the relevance of the forecasts, the periodic update process will remain important. This update process should also reconsider regularly both the suitability of the method and the elements to be forecast. In the first update round, changes were made to some technology definitions to reflect market changes and one forecast was discontinued due to having lost some of its original relevance.

Acknowledgement

Austroads is thanked for their support in undertaking this project and for the opportunity to publish this paper. The project itself could not have happened without the contribution of government and industry stakeholders, providing both critical review and insights and intelligence that were not publicly available.

[9] Transport Systems Catapult, Market Forecast for Connected and Autonomous Vehicles, 2017

[10] Australasian New Car Assessment Program (ANCAP) Analysis Report: Availability of Autonomous Emergency Braking (AEB) in Australia, 2019

[11] Austroads, Minimum Physical Infrastructure Standard for the Operation of Automated Driving Part B: Scenarios for Potential Availability and Usage of Different Levels and Types of Automated Driving, Research Report AP-R665B-22, 2022

[12] P. Bansal and K. Kockelman, “Forecasting Americans’ Long-term Adoption of Connected and Autonomous Vehicle Technologies”, Transportation Research Part A: Policy and Practice, Volume 95, pp.49-63, 2017

[13] New Zealand Ministry of Transport (NZ MoT), Vehicle Fleet Statistics, 2019

[14] Australian Government Department of Infrastructure, Transport, Cities and Regional Development (DITCRD) Reducing Heavy Vehicle Rear Impact Crashes: Autonomous Emergency Braking Regulation Impact Statement, 2019